Application specific study, analysis and classification of body area wireless sensor network applications

Computer Networks xxx (2015) xxx–xxx

Contents lists available at ScienceDirect

Computer Networks journal homepage: www.elsevier.com/locate/comnet

Survey Paper

Application specific study, analysis and classification of body area wireless sensor network applications Adnan Nadeem, Muhammad Azhar Hussain ⇑, Obaidullah Owais, Abdul Salam, Sarwat Iqbal, Kamran Ahsan Federal Urdu University of Arts, Science and Technology, Karachi, Pakistan

a r t i c l e

i n f o

Article history: Received 6 November 2013 Received in revised form 27 December 2014 Accepted 2 March 2015 Available online xxxx Keywords: Body area sensor networks Wireless sensor network Classification of BASN applications

a b s t r a c t The evolution of wearable computing and advances in wearable sensor devices has motivated various applications of Body Area Sensor Networks (BASN). In the last few years body areas sensor networks have emerged as a major type of wireless sensor networks (WSN). This emerging networking technology can be used in various walks of life. A number of surveys have been published on MAC or the physical layer mechanism of BASN but very few have focused on studying it from the application point of view. In this paper, we first review, from literature, existing use of body area sensor network and classify and then its application domain. Within these application domains of BASN, we propose a feasibility of scenarios where BASN can be used for both application and technical aspects. In addition, we classify the use of BASN from literature, based on certain parameters. Finally, we discuss and highlight issues where further research can be conducted in the future. Ó 2015 Elsevier B.V. All rights reserved.

1. Introduction Body area sensor network (BASN) standard is an output of the project of IEEE Wireless Personal Area Network working group [1]. This standard introduces the framework for BASN, which includes the network topology and the reference model explaining the supporting functionality of nodes and centric device. It also defines the functionality of the MAC sub layer and physical layer. BASN enables novel uses of this networking technology, especially in healthcare, fitness, entertainment, sports, etc. BASN allows users to connect wireless devices they carry on or with them. We have seen tremendous growth in wearable sensor devices for healthcare and beyond in

⇑ Corresponding author. E-mail addresses: [email protected] (A. Nadeem), a.hussain @fuuast.edu.pk (M.A. Hussain), [email protected] (O. Owais), a.salam@ fuuast.edu.pk (A. Salam), [email protected] (S. Iqbal), kamran.ahsan@ fuuast.edu.pk (K. Ahsan).

the last few years that has created opportunities for using BASN in various application domains. However, there are obstacles to widespread adoption of BASN. In [2] authors have first defined both manufacturer and user oriented requirement for widespread adoption of BASN. Major requirements include safety for human from wearable and implanted sensors, compatibility in terms of interoperability of nodes in a BASN, communication protocol and data storage, security and ease of use. They introduced the BASN environment as shown in Fig. 1. They have also highlighted major challenges faced by BASN, which are as follows:  Trade-off between processing and communication  Data rate and power consumption  Increased attenuation as compared to other applications of WSN  On-node storage, Energy Harvesting A number of surveys of BASN have been published. For example, in [3], authors have reviewed the existing MAC

http://dx.doi.org/10.1016/j.comnet.2015.03.002 1389-1286/Ó 2015 Elsevier B.V. All rights reserved.

Please cite this article in press as: A. Nadeem et al., Application specific study, analysis and classification of body area wireless sensor network applications, Comput. Netw. (2015), http://dx.doi.org/10.1016/j.comnet.2015.03.002

2

A. Nadeem et al. / Computer Networks xxx (2015) xxx–xxx

Fig. 1. A body area sensor network environment [5].

layer protocol fulfilling the low power consumption requirement of BASN. They have classified power efficient mechanism in categories (lower power listening, scheduled contention and TDMA based mechanism) and then investigated their strength and weakness. In [4] authors reviewed the channel estimation techniques for multiband UWB communication and summarized them on the basis of their operations, matrix used and suitability for their implementation in healthcare. In [5] authors first presented a review of physical layer communications mechanisms, including antenna design, in body radio frequency communication, propagation patterns. Then they reviewed the low power MAC layer mechanisms for BASN. Finally, they reviewed routing strategies for BASN. Barakah and Ammad-uddin [6] have presented a survey of challenges and applications of BASN in healthcare. They have also proposed the role of BASN as a virtual doctor by defining its architecture. Similarly, in [7] authors have investigated the sensor devices used in BASN and their physical and MAC layer mechanism. They have also reviewed some BASN projects and highlighted some design challenges and issues. Considering that most of the work has focused on reviewing only physical layer mechanisms [1] or MAC layer mechanisms [2] or both [3,4], and most of these surveys have focused on BASN applications in healthcare; only in [4] the authors have highlighted some BASN projects outside healthcare. In contrast to [1–4], we first review

the existing use of BASN in various application domains. Additionally, we propose various novel scenarios where BASN implementation is feasible. The rest of the paper is organized as follows. In Section 2, we introduce the major application domains for BASN. Section 3 presents the review of existing BASN application proposals from literature. In Section 4 we present our classification and analysis of existing BASN applications. In Section 5, we propose a feasibility of BASN use in some special scenarios as future research directions. Finally, Section 6 summarizes the work in this paper. 2. Application domain In this section, we introduce some major application domains of body area sensor networks. 2.1. Healthcare Healthcare is the diagnosis, treatment and prevention of disease, illness, injury and other physical and mental impairments in humans [8] as well as in animals. The world is facing many issues related to provide healthcare services to the peoples, especially with an aging population. In future, this ratio is expected to increase, and the shortage of medical staff and doctors is observed as a

Please cite this article in press as: A. Nadeem et al., Application specific study, analysis and classification of body area wireless sensor network applications, Comput. Netw. (2015), http://dx.doi.org/10.1016/j.comnet.2015.03.002

A. Nadeem et al. / Computer Networks xxx (2015) xxx–xxx

major issue. According to the World Health Organization (WHO) there are around 600 million persons aged 60 years and above, which is expected to rise to 1200 million persons in year 2025 [9], this statistic suggests the requirement for enhancement of immediate medical facility. To achieve sustainable access, effectiveness and quality of clinical processes, we suggest the use of technological solution for providing health services, especially in developing countries. The BASN can play a vital role in the provision of efficient healthcare services, reduce the burden on the clinical system and in fact using BASN could increase the throughput of the healthcare service providers. 2.2. Disability assistance Around one billion people of the world or 15% of the world population is suffering from some kind of disability [10] out of which 10% are victims of severe disabilities such as paralysis, blindness, learning disability and strongly depend upon support from their family, on government or any non-government organization. Disability ranges from mild to severe such as weak eyesight, slight lameness to paralysis and complete blindness. The life expectancy rate has increased in European countries as the result of provision of better healthcare services and enhanced quality of life. The population above the age of 65 years [11] is increasing and thus the demand for nursing homes for older people, hospital and work force required for providing care to this group is also increasing. Effects of disability can be greatly reduced by providing assistance to disabled persons and enabling them to perform activities of daily living [10]. Assistive technology aims to overcome the effects of disability, improve the efficiency, and increase the capability of disabled person to perform those activities that are otherwise difficult to perform. Assistive Technology (AT) enables persons with disabilities to become more independent in their lives, take care of basic needs, and actively participate in community activities and in obtaining employment. AT devices can be classified into two types, i.e. active and passive. An active AT device requires direct involvement of disabled persons in providing assistance such as an electric wheelchair for mobility-impaired person, and the white cane for blind person. While a passive AT device functions without intervention of the disabled person such as a fall detection system, fire & a flood detection system for those disabled and older people which live alone in their home. 2.3. Sports BASN is used in sports for training, monitoring, selfassessment as well as enhancement of the sports person performance. This is achieved by monitoring physiological parameters such as Gait length, heart rate, Oximetry, and acceleration. These parameters will be different for each sport. The manufacturers produce different types of sensors for sports and fitness. Smartphone and wearable watches are being connected to BASN. A good example is the Nike iPod sports kit, in which the sensor is placed beneath the sock liner of the left shoe and the receiver connects to

3

iPod. The sensor is used to measure pace, distance, time elapsed and calories burned. The information is transmitted wirelessly to the receiver and the Nike tracking device for real-time feedback during training. Current researchers used accelerometers on a body to identify the specific postures. With the help of these technologies, players of many sports such as football, cricket and golf, can easily improve their performance and also protect themselves from injuries due to incorrect postures. 2.4. Human activity monitoring BASN can also be used to monitor the human activities in the context of providing security or care for humans. Human activities can be detected using analysis of various postures of the human body during the movement. There are various inertial wearable sensors developed for the recognition of various human postures. This activity monitoring could not only analyse the elderly, children, and disabled person, but also it can be used to assist them in certain scenarios. 3. Review of existing BASN applications In this section, we study and review existing applications of BASN. 3.1. Healthcare applications of BASN The major application domain of BASN is healthcare use for monitoring and providing self-care functions of physiological changes in the body. In this section, we review the research literature focused on the Healthcare issues, categorized in three sections: (a) General Healthcare (b) Neonatal Healthcare (c) Animal Healthcare. 3.1.1. BASN for general healthcare In this subsection we present a review of proposed mechanisms where BASN system is suggested or implement for general Healthcare. To fight against cardiovascular disease (CVD) MyHeart [12,13] project is in progress. The MyHeart project supported by thirty-three partners from 10 different countries, including research institutes, academia, medical hospitals and different industrial partners like Philips, Nokia, Vodafone and Medtronic, a world-leader in cardiac technology. The idea of this project is based on functional clothes or smart clothes in which sensors are either integrated or simply embedded in the piece of clothing [14]. These sensors are powered from a centralized on-body power supply. This system is a self-managed monitoring system of CVD and it is capable of transmitting data to a remote location so that professional doctors or clinical staff can provide healthy lifestyle suggestions whilst monitoring real time patient status for preventative purposes or early prognosis to avoid critical emergencies. The author in [15] presented a heart rate monitoring system based on fuzzy logic. The proposed heart rate monitoring system, a continuation of their previous work,

Please cite this article in press as: A. Nadeem et al., Application specific study, analysis and classification of body area wireless sensor network applications, Comput. Netw. (2015), http://dx.doi.org/10.1016/j.comnet.2015.03.002

4

A. Nadeem et al. / Computer Networks xxx (2015) xxx–xxx

is consisting of monitoring of electrocardiogram (ECG), heart rate (HR), three axes body acceleration and temperature sensors. State of health could be inferred based on the calculation of the autonomous heart rate variability (HRV) from the HR data using the threshold based model. This model simply monitors the ECG amplitude and compares it with the present threshold value during the different states of the body like exercise, lying or resting and standing position. The BASN is playing a vital role in real-time monitoring of healthcare. Different types of wearable sensors monitor different human physiological signs like blood pressure, body temperature, ECG, blood flow. The sensed data is sent to the caretaker or medical centre by using Bluetooth or wireless communication for real-time monitoring and analysing of human health condition [16,17]. In [18] author developed a web based remote health-monitoring interface named ‘‘Health Face’’. BASN system consists of MICAz nodes and Crossbow MTS400 sensor board. MICAz nodes have an ATmega128L microcontroller including internal memory, and a Chipcon CC2420 RF transmitter based on IEEE 802.15.4 standard. The MTS400 board is used for sensing the temperature, light, pressure, humidity, and acceleration. MIB520CA acts as a base station (PDA or Cell phone) for connecting the computer with the wireless sensor nodes through USB. The flow of the process is to send sensed data to the base station, which transmits this data to the medical centre server using Wi-Fi technology. The Health Face software is designed using MATLAB Builder NE with Web Figure and .Net technology. This web-based application is secured by providing User-ID and Password for authentication purpose. If the data received from any sensor cross its predefined threshold level defined in the software for different sensors, the software generates an emergency alarm to its doctor or caregiver for immediate necessary action. Authors in [19] suggested that EEG (electroencephalogram) signals can be monitored continuously by hospital staff ubiquitously without the personal visit or direct intervention of the patient. They proposed a BASN system, helping patients to send EEG signals through a smart phone to a remote terminal. The authors suggested Block sparse Bayesian learning (BSBL) as a new method to EEG compressed sensing. Block Sparse Bayesian learning answers the problem of energy consumption, data consumption and device cost. In this proposed method EEG signals have been compressed using sensors, hence utilizing the on chip energy of BASN. EEG signals return to normal state by a remote computer, hence not using the energy of sensor node in BASN thus the energy of BASN is not wasted. 3.1.2. Neonatal healthcare monitoring In this subsection we review proposed BASN systems for neonatal health care from the literature. The smart jacket [20] is designed for neonatal monitoring with wearable sensors; the purpose of this jacket is to provide wearable unobtrusive continuous health monitoring as well as a comfortable clinical environment for the new-born. The wearable jacket system design specially overcomes the level of discomfort faced by clinical staff in monitoring vital parameters of the neonate. This wearable jacket is a kind of

smart textile; the design of a jacket contains the integration of conductive textiles for ECG monitoring. This jacket is open at the front and has an open structure fabric on the back and the hat. The design of the jacket ensures the skin-on-skin contact, phototherapy and medical observation. The concept of Diversity Textile Electrode Measurement (DTEM) is chosen in which neonate wears a baby jacket. It contains six conductive patches that sense bio-potential signals at different positions to perform diversity measurements. The placement of conductive patches chooses with the care so that they will always remain in contact with the skin for un-abrupt communication. The patches consist of silver and gold textile electrodes cover with different layer of tricot for safety purpose. In [21] author presents a health monitoring system for the kids named KiMS. The KiMS focus on four fundamental features, i.e. early detection of infectious disease, monitoring of healthy habits, post treatment monitoring and detection of chronic health issues. For monitoring several parameters, the authors suggested a wristband type device which has a temperature and pulse rate sensor, microphone, processor, memory, and Bluetooth module. The proposed health monitoring system is based on acoustic signal processing. The authors in this paper extracted features from babies’ audio signals of interest and classified hem to identify certain health parameters such as cough, sneeze, cry and vomit by comparing with the training set. The resultant detected audio events will examine along with the data received from the temperature and pulse rate sensor for monitoring health situation. The author describes the events using vocabulary-based encoding scheme, assign 4-bits that represent the cough, sneeze, etc. and 3-bit for identifying 8 different times of the day. This coding scheme minimizes the bandwidth with low power operations and efficient utilization of storage space in the device. The proposed system is also capable of generating alert to the day-care specialist in case of any emergency. The authors claimed that gathering the extracted voice and sensor data history helps clinical staff in monitoring of a new-born baby or child. The author displayed the test result, which suggest using at-least third or higher level of wavelet decomposition for improvement in performance of the classification algorithm. 3.1.3. Animal healthcare monitoring Animal agriculture plays an important part in the world economy. Several researchers have proposed different models for the monitoring of animal healthcare using wearable sensors and Mobile Technology [22]. Quality of meat has improved in the last few years, but due to the lack of animal healthcare respiratory disease a variety of gastrointestinal and metabolic diseases exists [23]. The wearable sensors with the integration of wireless technology benefit the real-time overall health diagnoses of animals [24] and especially the chronic diseases [25]. Early detection of transmissible disease could be helpful to avoid the huge financial losses in the animal agriculture industry. The wearable sensors can also be used to identify the activity, position and social behaviour of the animals, which can be used for the growth development and better environment [26].

Please cite this article in press as: A. Nadeem et al., Application specific study, analysis and classification of body area wireless sensor network applications, Comput. Netw. (2015), http://dx.doi.org/10.1016/j.comnet.2015.03.002

A. Nadeem et al. / Computer Networks xxx (2015) xxx–xxx

In [27] authors designed an ambulatory instrument based on predictive model for disease detection in cattle while observing the physiological and behavioural changes. The design of this instrument is based on the wearable sensors, electronic identification circuit and movement observation device. The author uses several behavioural and physiological vital signs measure the health of the cattle like core body temperature, heart rate, respiratory rate, ambient temperature, humidity, wind pattern and its behavioural factor like feed and water intake. The authors, as a team, fixed the instrument containing different wearable sensors in the belt worn by cattle. The author integrates the Radio-frequency identification (RFID) tags use for identification of the cattle, accelerometer and GPS for monitoring the cattle movement in this instrument. The Microchip PIC18F8720™ microcontroller performs the on-board processing of these sensors and electronic equipments. The sensed data from wearable sensors are processed and stored using the microcontroller. The communication mode transfers data using the Bluetooth module. On detection of nearest Bluetooth access point, the microcontroller transmits these stored data wirelessly to the server for analysis. The author used checksum method to check the wireless data integrity. 3.2. Disability assistance using BASN A BASN comprises of different tiny sensor nodes, deployed on human body for sensing important measure such as temperature, and blood pressure [31]. These sensors are capable of performing some computation, storing small amount of data and transmit sensed data to the sink or to a desired destination. BASN has used for disability assistance in many ways, such as activity monitoring, posture detection, way finding for blind/deaf-blind person and support aging in place. In most cases, a BASN for disability assistance lies in passive assistive device category because generally it was used for monitoring the condition or current state of disabled persons without any interaction with them. However, in some cases, BASN can work actively and fall into active assistive technology device category. In this section, we review research work from the literature regarding the application of BASN in disability support. BASN has implemented for assisting disability as per following category: 1. 2. 3. 4. 5.

BASN BASN BASN BASN BASN

for for for for for

rehabilitation activity monitoring (real-time) posture detection way-finding for blind/deaf-blind person support for elderly person

3.2.1. BASN for rehabilitation Rehabilitation is a dynamic process for the patients that have suffered a stroke, joint replacement/reconstruction surgery, amputation or any motor functional disability caused by Parkinson disease. Specialized medical operator helps patients for restoration of functional capabilities to normal state for which medical operators required to monitor and control the rehabilitation process. A

5

traditional system for monitoring the patients comprises of markers which patient wears on different body parts and a camera that records the movement of patients. These systems are complex, expensive and formation of the system, i.e. markers and cameras are required each time a patient visit to a rehabilitation centre. The BASN helps both patients and medical persons in term of the feature of remotely supervising and monitoring patient’s recovery and rehabilitation process. Continuous monitoring in the patient’s natural environment greatly reduces the burden over the medical centre as patients are not required to visit regularly. Further, it is less expensive and unobtrusive as patients are not required to tether with the system. A rehabilitation and recovery monitoring system prototype proposed, evaluated and some experimental results are presented in [28]. The system consists of IRIS motes (transceiver modules from Crossbow™) which are placed on the wrist and upper part of right arm. Both IRIS motes are connected to a sink node placed in centre of front waist. Sink node is working as a gateway for BASN and connected to PC. A RS. SI (Received Signal Strength Indicator) based algorithm using SVM (Support Vector Machine) was applied to determine and classify the arm movement. Some rehabilitation exercises which author named as Activity 1, Activity 2 and Activity 3 were carried out by subject. Results presented in the paper showed good detection performance of movement. RehabSPOT, a rehabilitation and recovery monitoring system for stroke and other physical dysfunction, is proposed in [29]. RehabSPOT is a 3-tier architecture system; in tier-1 kinetic sensor equipped with Sun SPOT freeRange node forming BASN by a wireless mesh network between sensor nodes using IEEE 802.15.4, Sun SPOT base station connected to a PC over a wired network in tier-2 and PC is connected to a central server over the internet is in tier-3. Sensor nodes are capable of monitoring different physical behaviour of patients, such as body movement, joint bending, and gait analysis. Accurate patient evaluation, reassessment time identification, and patient group’s identification are issues faced in management of Low Back Pain patients. A multi-sensor wearable wireless system, IMPAIRED is proposed in [30] for evaluation of the patients with low back pain. The BASN topology proposed has been used in order to form BASN using MicroStrainÒ Inertia-LinkÒ wireless inertial sensors. The aim of the project is to monitor the patients’ degree of disability with low back pain, creating an integrated device that helps long term monitoring of patients in their natural environment, and usability of inertial sensor and their suitable placement of human body of patient with LBP. Patients with hip replacement surgery have a risk of dislocating the hip after surgery. Therefore, they need to control their movement, avoid exerting the force on the operated leg, and observe the direction of the surgeon during the recovery period. Using inertial sensors (3-axis accelerometer and 3-axis magnetic sensor) HipGuard system is proposed in [32] to help patients measure the force they are exerting on the operated leg, monitoring the

Please cite this article in press as: A. Nadeem et al., Application specific study, analysis and classification of body area wireless sensor network applications, Comput. Netw. (2015), http://dx.doi.org/10.1016/j.comnet.2015.03.002

6

A. Nadeem et al. / Computer Networks xxx (2015) xxx–xxx

movement, such as leg and hip position and rotation, thus preventing the risk of dislocation of joints in a home environment. Seven sensors are integrated into trousers, worn by patients to monitor its activities, generate alarms when an abnormal posture is detected or excess force is measured on the operated leg. These sensors are connected to control device using ANT radio link while the control unit is connected to PC using Bluetooth. The system requires 10 s of training during which patient should stand still so that the system creates a reference point for future movement recognition. 3.2.2. BASN for real time activity monitoring A wearable BASN system can also be used to support rehabilitation of patients with motor impairment. This also support for motor impairment training in healthcare and for exercise instruction of an elderly person. For evaluation of patients with motor impairment, therapist instructs the patient to perform special activities, watch movement style, measure the time in performing a task, and counting the number of steps, in which therapist require qualitative measurement of movement. Author proposed a system in [33] for measurement of gait analysis parameters using inertial sensor placing on the body. 3.2.3. BASN for posture detection Accelerometer with wireless networking capability are widely used for capturing the posture of humans specially fall detection in elderly people. Using inertial sensors, integrated in the jacket on the upper part of trunk a novel algorithm is proposed in [34], for fall detection for the people who undertake physical activities in extreme and severe conditions such as fire fighters and civil protection apparatus. The proposed novel algorithm mitigates the effects of jumping or running, a normal routine during firefighting during which accelerometer affects same as in the condition of fall, resulting in false detection of fall. This algorithm reduced the effects of false alarm significantly. 3.2.4. BASN for way finding A blind person can easily move in its familiar environment like home, school or workplace. However, there is a problem for them when these persons go to a new place, such as students enrolled in a new school or a person moves to a new workplace. An RFID based information grid is proposed in [35] using passive, low-cost, high frequency operated RFID tags embedded in floors. RFID tags receive and store location information. The RFID reader is integrated in the walking cane or in the shoe of blind persons. The RFID reader reads the location information from the encounter tag and then transfers the information to the users’ PDA. In this way, a blind person can easily move into the new and unknown location. 3.2.5. BASN for support for elderly persons Abnormal falls are a general cause of disability in older persons and this fall can become life threatening. Researchers proposed several techniques to provide support to elderly persons living alone, such as fall detection carpet or systems consisting of cameras that records the activities of elderly people and detects any abnormal

condition using computer vision techniques. However, these systems are expensive and do not provide full coverage of the living place. A body area sensor network consisting of inertial and biosensors can greatly help in providing support for elderly people living a safer life. Abnormal Condition Detection system has been proposed in [36] for supporting elderly people living alone in their living place such as home. It uses ‘‘Wireless Bio Sensor Evaluation Kit’’ which was developed by the Medical Electronic Science Institute (MESI). They developed a system that detects object’s activities such as sitting, walking, lying and falling. They evaluated their system and reported a 90.91% accuracy of fall detection. Parkinson disease is a common neurodegenerative disease, affecting 3% of the population of the age above 65 years. It is a disorder of the brain, leading to tremor (shaking), slowness in movement and in walking and coordination. For evaluation of the person with Parkinson disease, a system is proposed in [37]. The system consists of a body area sensor network formed by SHIMMER biosensor. Intel Digital Health Group’s develops these sensors. SHIMMER is capable of sensing movement in 3-axis using tri-axial accelerometers. It records, process data and sends the process data wirelessly to the base station. For clinical assessment, patient movement, such as; quiet sitting, finger tapping, alternating hand movements, heel tapping, and walking was recorded and processed for evaluation. A fall detection system for older people is proposed in [38]. The system is comprised of tri-axial accelerometer and tri-axis gyroscope. The authors proposed algorithm that significantly differentiates the ADL (activities of daily living) and in actual fall. Author use SVM (signal vector magnitude) to differentiate ADL, such as sitting, standing, running, climbing stairs, and laying. The experimental results show 100% measure of specificity without any inaccuracy and measured sensitivity of 81.6%. Another fall and accident detection system is proposed in [39]. The sensors used in proposed architecture are an accelerometer, a gyroscope and a magnetometer. The sensors are placed on the upper torso, on the hip and on one of the user’s legs. The preliminary results show 85.6% accuracy of fall detection in normal fall. The authors proposed to investigate the detection of hampered fall detection as future plan. 3.3. Human activity monitoring Activity recognition may provide benefits in many areas. With the development of miniature sensors for various applications, it is now possible to create a BASN, which can help in the recognition of human activities. The body area sensor network is used to monitor human activities in the following situations: 1. Emergency Situation (firefighters, volunteers, rescuer, etc.) 2. Remote Monitoring (staff, kids, elderly, etc.) 3. Training (interactive dance, stage performance, etc.) 4. Security (soldiers) 5. Safety (personal safety, elderly, etc.)

Please cite this article in press as: A. Nadeem et al., Application specific study, analysis and classification of body area wireless sensor network applications, Comput. Netw. (2015), http://dx.doi.org/10.1016/j.comnet.2015.03.002

A. Nadeem et al. / Computer Networks xxx (2015) xxx–xxx

Researchers have used BASN to recognize body postures and monitor activities such as standing, walking, running, lying for example, in [40] authors used BASN, consisting of four sensor nodes, for human activity recognition. Keeping in view that RSSI can be used to detect the position of unknown object the authors described indoor localization method through the use of RSSI. Their method calculates the position of target node through measuring the distance, and the distance is measured using RSSI values as input to Extended Kalman Filter. The authors’ evaluation is based on HMM (Hidden Markov Model), using BAUM-Welch algorithm (used to find unknown parameters of HMM) to train the system and Vitebi algorithm (a dynamic programming algorithm used to find the most likely sequence of hidden states) to identify the activity series. Forwards backward algorithm (an algorithm to compute the posterior margin of all hidden state variables given a sequence of observations) is used for determining the probability of particular output signals. Author recognized altogether nine postures, including standing, walking, running and transition from one posture to another. Additionally, the authors suggested that the same technique can be used for fire fighters. In [41], the authors proposed a prototype of ambulatory (relating to or adapted for walking) monitoring of human activity using wireless sensor system. They concluded that the prescribed work can be used for monitoring other physiological parameters such as heart or muscle activity. The proposed BASN consists of three acts sensor platform in which two of the sensors are placed on the ankles for ambulatory monitoring and one is placed on upper body tilt to monitor ECG activity. They investigate user physiological state using an on-board bio-amplifier, which is implemented on the ISPM board. They passed the signal output of the EMG and ECG to two microcontrollers, one for local microcontrollers and the other for Telosboard (a type of sensor platform). The AcitS platform was used for the monitoring of steps and to measure stride at the time of walking or running. The authors conclude that the same technology can be used to measure activities in various computer assisted physical rehabilitation applications. Sitting, sitting-reclining, lying-down, standing, walking, jogging, and other physical activities are recognized in [42]. The authors used HMM and multimodal sensing paradigms to recognize postures. They claimed that the detection of these postures is not possible using the traditional accelerometer based approaches. They proposed relative sensor proximity and sensor orientation as two new modalities. The authors used RSSI values of the RF signal to find the relative proximity. Activity-intensive and nonintensive body postures, such as sitting, standing; walking and running are recognized by the work proposed in [43]. They used proximity sensors based solution for the detection of sitting and standing postures. The accelerometer is used for detecting postures of walking and running. Some posture detection needs fine details of positions like standing, sitting, and lying down. Low activity postures recognition is performed using radio frequency based proximity sensing and by applying HMM. In [44] authors used RSSI (Received Signal Strength indicator) values of nodes in order to find the location of

7

firefighters working in the field. Their location tracking scheme will locate the firefighter and also monitor the different vital parameter such as CO (Carbon monoxide) and HCN (Hydrogen Cyanide) in the environment and send the information to fire chief which make enable him to take appropriate decision about the health of his valued firefighter. They simulated the proposed system using a renowned simulator QualNet™ and also mimic the situation in an environment to test the results. Seven sensor nodes are used in the simulation and in the actual environment setup. Their implementation exploits the idea of MANET (mobile adhoc network) to create a network on the fly, enabling firefighters to join and/or leave the network at any time. Authors in [45] proposed a routing mechanism to measure the fatigue level of soldiers using BASN. If the fatigue level of soldiers is known then it can help to send back up in order to smooth running of the ongoing operations. Temperature, heartbeat, and blood glucose sensor are used to measure the fatigue level of soldiers. In this work sensors monitor the increase in body temperature and heartbeat, and decrease in blood glucose level, while performing activities such as walking, slow running and fast running, as an input to BMR (Basal Metabolic Rate). BMR takes as parameter to measure fatigue level. BS (Station) is placed on the wrist of the soldier and receives the data from all three sensor nodes. Threshold limits of temperature, heartbeat, and blood glucose has been set as obvious simulation parameter and if the values of these parameters cross the threshold limits then the transmitter in each sensor sends data to BS. BS takes decisions on the received data and measure the fatigue level and if fatigue level is observed BS informs headquarter for taking future decision. BASN has also been employed be the researcher to monitor soldiers. For example, in [46] the authors proposed to recognize four postures for soldiers’ tele-monitoring. They used accelerometer and relative proximity for the detection of postures of soldiers. In [47] authors proposed a complete implementation of different posture recognition. They prepared a wireless node named WiMoCa and recognized seven different postures through three accelerometer nodes. All three nodes are placed as the endpoints of a star network topology for sensing and acquiring data. WiMoCa nodes monitor the inclination of certain parts of the body and average these sensed date with respect to gravity. In [48] authors described a complete system in which soldiers wear body sensors to measure ECG signals, accelerometer, SpO2 and also a transceiver for communicating with other soldiers. They presented their work in a warlike situation in which a group of soldiers exposed to a bomb blast in a large area having no network infrastructure. To calculate the blast impact assessment authors present a blast source localization algorithm which calculates the blast location through the acceleration experienced by soldiers’ body. BASN can also be implemented in emergency situations to monitor activities of firefighters, rescue workers and volunteer. For example, in [49] authors observe the dehydration level of firefighters and other rescuers

Please cite this article in press as: A. Nadeem et al., Application specific study, analysis and classification of body area wireless sensor network applications, Comput. Netw. (2015), http://dx.doi.org/10.1016/j.comnet.2015.03.002

8

A. Nadeem et al. / Computer Networks xxx (2015) xxx–xxx

through Ionic Selective Electrodes (ISE) sensors on their worn fabric. This work is a part of ProeTEX project. ProeTEX is a project, which aims to develop a product for emergency operators such as firefighters and rescuers. One of the earliest works of ProeTEX presented in [50] is the development of a smart uniform for Civil Protection’s requirement and for firefighters. They placed sensors and electrodes in the inner and outer garments of the person. Inner garment sensors are used to measure physiological parameters like heart rate, breathing rate and temperature of the body. The outer garment is equipped with one thermocouple to sense environmental temperature and two tri-axial accelerometers to observe the movement (user activity and fall detection) of the person. GPS on the uniform is used to localization of information if the user is supposed to move into a large area. The data gathered from sensors are sent to a textile antenna, which has an RF module and is capable of sending information to a monitoring station as well as other operators. In [51] as part of the project ProeTEX, the authors proposed development of two sets of sensor-based garments. The first garment is for civil protection rescuers and the other for fire fighters. Sensors are placed in outer garment, inner garment and a pair of boots. Sensors in inner garments sense cardiopulmonary parameters and temperature. One detachable band is also a part of inner garment that contains all electronic modules. Sensors on outer garment can detect environmental temperature and concentration of carbon monoxide (CO) in the environment. Posture and activity, including the possible fall of a person are detected using two accelerometers, and in order to prevent possible burning of the person, one more sensor is attached in outer garment that measure the heat flux passing through the thermal insulation layer of the person’s jacket. A prototype application for remotely monitoring the condition of fire fighters in hostile environments is proposed in [52]. They deployed five sensors for the monitoring of the person. All sensors sense and directly sends data to sink node. The sink node is not a sensor, but it is a portable computer, which interacts with all sensor nodes to monitor the state of firefighter. The states which are recognized include running, walking, weaving the arm, standing, and lying. Temperature, humidity, and position of the object are sensed periodically through the sensor worn on the chest. Four wearable sensors detect the movements of the firefighter. In author’s point of view, the combination of all these sensed data can be used to estimate the fatigue level or comfort level of the firefighters. BASN has also been used to monitor physical activity of elder persons. For example, fall detection and heart rate are monitored in [53] through a wearable device wear on the chest of the person consisting of a tri-axial accelerometer, a two-axis gyroscope and a heartbeat detection circuit. The wearable device detects and sends signals using ZigBee. The system can send signals in case of alarming situations to health care personals or care givers. To identify fall and physical activities three machine learning methods, (i) Naive Bayes, (ii) Support Vector and (iii) Ripple down rule learner were used in this system. In another example, in [54] authors observed physical activities of the human body by implementing BASN. They used motion and

temperature sensor to monitor individuals. The data gathered from these sensors can also be utilized for fall detection of the person and temperature sensors can be used to sense person’s temperature. 3.4. BASN applications in sports Technology changes many aspects of our lives. The adoption and use of technology can also impact sports field where advancements have been seen in materials, equipment design, clothing and portable electronics. The development of tiny sensors brings revolution in many fields. Miniature sensors are being used in many applications including professional sports. BASN has already been used to assist sports persons for different purpose. Sports persons use BASN in sports in following four domains:    

Training Monitoring Self-assessment Performance Enhancement

3.4.1. BASN for training sports persons Training allows a sports person to enhance performance. It signifies the process of preparation for some task. Mostly this process extends to a number of days and even months and years depends upon the progress of candidates. The technologies play a vital role in the training of sportsman. Small sensors are attached to the body of the sports person during training to observe the performance level of that person. Sports’ training represents the adaptation of the certain exercises that can result in an improvement in his overall performance. Therefore, researchers are contributing to the development of sports specific coaching system. These systems consist of a framework which are capable of acquiring and processing the physiological and behavioural variables for a given sport. The athletes of any sports can improve their quality of training from feedback systems. In [55] a golf training system using BASN is presented. This system incorporates wearable motion sensors to get initial information and provide a feedback on the quality of movements. To capture the unique movement of the golf swing, sensors are placed on a golf club and at a certain position on an athlete’s body such as wrist and arms. The proposed system can work as a quantitative model, which apply signal-processing techniques on the collected data and measures the correctness of the performed actions. The authors evaluate the effectiveness of the proposed framework using four major segments of the golf swing: takeaway, backswing, downswing and follow-through. The result shows that the suggested model is useful in improving the quality of the golf swing of a player with respect to the angle of the wrist rotation. The dart is one of the sports in which the participant throws an object. In a game of dart, accuracy and repeatability are the key elements. The position a dart will hit the target depends upon the various factors including position, direction and speed of the motion at the point of release. The authors in [56] proposed a mechanism to identify

Please cite this article in press as: A. Nadeem et al., Application specific study, analysis and classification of body area wireless sensor network applications, Comput. Netw. (2015), http://dx.doi.org/10.1016/j.comnet.2015.03.002

A. Nadeem et al. / Computer Networks xxx (2015) xxx–xxx

the contributing factors for the lateral drift or landing errors in the horizontal plane. For this purpose, they used BASN by wireless inertial measurement devices to capture tilt, force and timings. An optical 3D motion capture system provides a complete kinematic model of the subject to monitor muscle activation patterns and a force plate and pressure mat to capture tactile pressure and force measurements. This work introduces the concept of constants throwing rhythm in the dart. It highlights how landing errors in the horizontal plane cause variations in arm force, speed, centre of gravity and other movements of the body. In addition, it could also monitor the fatigue level of the player. 3.4.2. BASN for monitoring sports persons Monitoring of athletes in real time is useful to maximize the performance while preventing injuries. It is also helpful in different applications like provision of refereeassistance and assist television broadcast. Due to the limited wireless range of worn sensors (BASN), it is difficult to take the physiological data of athlete in real time and it needs multi-hop routing mechanism to implement such type of system. In [57] the authors proposed a model that can produce synthetic dynamic topologies using stochastic attributes. The model is useful in simulating the performance of different routing strategies for monitoring soccer player’s movements during a game. It also allows the key parameters such as link auto and cross-correlation to study their impact on routing performance. This model is very helpful in understanding and modelling the dynamic topologies related to the sports monitoring and allows designing dynamic topologies for such environments. The extraction of data in real time from the sensors is difficult due to the small battery and short wireless range, particularly in a sport with a large play area. In [58], a BASN is deployed for monitoring the soccer player’s activity. In this BASN experimental setup shown in Fig. 2, each player wears the MicaZ mote mounted armband on the

9

right arm, which sends a hello packet after every second time interval with individual slotted time lines. The authors initially reviewed the two existing routing schemes, i.e. a random forwarding scheme and two-copy routing scheme. Later on, while analysing the existing protocols on the soccer field, the authors introduced their own routing protocol called ‘‘tunable flooding scheme’’. The features of the proposed routing schemes are replicated at the source, replication at intermediate nodes and data freshness. Analysis of the results shows low resource and minimum delay compared to the previous routing schemes. It is observed that the muscle fatigue is the main cause of a player’s performance degradation. Fatigue level of player is continuously changing parameters during play due to tiring activities like running and sprinting. So there is a need of a mechanism which continuously monitors each player in the team during a match and in the case of the occurrence of any critical situation, immediate precautionary measures should be taken to reduce the chance of any further injury. The fatigue level can be measured by sensing the accumulation of lactic acid in muscle. In [59] authors proposed a protocol Threshold based Energy-efficient Fatigue Measurement (THE-FAME) for soccer player using BASN. To achieve minimum delay and less energy consumption direct transmission is used to send data to the base station by using multiple sinks along the border of the ground. It uses a composite parameter, which consists of threshold parameter for lactic acid accumulation and distance covered by the player. When any value of a composite parameter goes beyond the defined threshold value the players are declared to be in fatigue state. MATLAB is used to perform the simulation of the proposed protocol. The result shows the effectiveness of the THEFAME in terms of energy and delay as compared to the previous multi-hop routing protocol. In [60], the authors used a BASN to determine player physical state during the match and presented a wireless sensor network that allows the interconnection between

Fig. 2. The experimental setup used by authors in [58].

Please cite this article in press as: A. Nadeem et al., Application specific study, analysis and classification of body area wireless sensor network applications, Comput. Netw. (2015), http://dx.doi.org/10.1016/j.comnet.2015.03.002

10

A. Nadeem et al. / Computer Networks xxx (2015) xxx–xxx

the BASN that are placed on each soccer player. In the 90 min soccer match, the player gets tired depending upon the dynamics of the soccer game. BASN helps the coach to monitor the match remotely and identify the exhausted player. In the proposed model, each player has BASN and also acts as a sensor node in the network to sense and transmit data. The data will be routed from a mobile source to a fixed sink located outside the game field. In order to get faster updates, the information routes through the players of both the teams, but decrypted by the players of the same team. A number of research works have focused on capturing and analysing the biometric and physiological signals, but these researches have been limited to a laboratory or some controlled environment. Sensory integration with textile industry is playing a vital role in the advancement of wearable sensor technology. However, there are a number of problems in the development of truly wearable monitoring technology (WMT). In [61], the authors reviewed the generic monitoring system architectures for sports person. They proposed that a monitoring system which could be split into three phases: sensing, processing and transmitting. They have also presented the custom implementation of commercially available component and evaluation board used for monitoring. In [62] authors presented a simple BASN platform, a mechanism to collect data in a real time environment and monitor the performance of the marathon athlete in a dense and highly dynamic environment. The collection of data during the event helps them to know the behaviour of the radio transmissions between different links in the network. Unlike [62] where they obtain the speed and energy expenditure from a body-mounted accelerometer, in [63] authors studied the problem associated with GPS based activity monitoring. They have conducted a study in which sensor nodes, equipped with GPS and accelerometer, are deployed on a group of professional players. The GPS is used to obtain the time and distance measurement. The accelerometer provides accurate information about the players’ speed by measuring the stride frequency. Stride frequency was compared to speed obtained from the GPS. The results showed that there is a linear relationship between the speed and the stride frequency of the athlete with respect to the ground. Sweating occurs due to thermoregulation in a body. During physical exertion, sweating rate increases in order to avoid a dangerous rise in temperature caused by the increase in metabolic rate. Sweat fluid includes sodium chloride, potassium, urea, lactate, bicarbonate, calcium, ammonia, organic compound and non-organic compound. Swot analysis plays an important role in a person’s wellbeing. The techniques for the analysis of sweat is quiet difficult. In [64] a textile-based sensor to provide real time information regarding sweat pH and sweat rate is introduced. The objective of this work was to create a system that integrates easily into a fabric. A pH sensitive dye is placed inside the fabric fluidic system, which determines sweat pH. The sweat activity detects textile substrate. All these sensors are integrated into a waistband and are controlled by a central unit with wireless connectivity. These sensors provide valuable physiological information.

A sweat analysis is useful in sports and health domain. It gives the information about the change in molecules and ions due to the pathological disorder. The composition of sweat can also change during exercise due to dehydration. The loss of water in the body causes, symptoms, irritability, headache, dizziness, cramps, vomiting, increased body temperature and heart rate, increased perceived work rate, reduced mental function, slow gastric emptying. The performance of the athlete decreases due to 2% drop in body weight caused by dehydration [65]. In [65] the authors described the development and testing procedure of a fluid handling using BASN for the real-time analysis of sweat pH and sodium level during the exercise. The optical detection system is used to record pH induced chemical changes, which is displayed with the help of a sensor. They test the device under the control conditions and it manages to detect the increments of 0.2 pH units. Today the blind and visually impaired people have improved the quality of their life, sociality and confidence by using different assistive technologies. They use different types of walking assisted system, which are equipped with different types of advanced technologies. However, enjoying sports is still a difficult task for them because sports require the use of many visual senses. Currently only trained blind people can enjoy the sports. Therefore, there is a need for the development of sports assistance system for blind or visually impaired persons. In [66], the authors proposed an indoor positioning system, which will assist blind or visually impaired persons to determine moving objects. This system assists the blind or visually impaired persons in playing sports. This system uses three technologies: a BASN, an indoor positioning system and a wireless sensor networks. It consists of wireless heart rate monitor, wireless sensor network and four ultrasound satellite modules. The heart rate monitor worn on the wrist, ultrasound and RF transmitters worn on the head and ultrasound satellite modules on the ceiling. The ultrasound and wireless sensor networks are used to detect an opponent’s position and forward this information to a blind person or visually impaired person through the vibration belt which is worn by them during sports. This information helps them in playing and enjoying sports more easily. 3.4.3. BASN for self-assessment of sports persons Self-assessment in sports means that athletes analyse their performance on some standardized form. In [67] a novel mobility model is proposed to name DynaMo. DynaMo is capable of modelling the mobility pattern of both an individual and a group. Dynamo is used to model the mobility pattern of players during the soccer match. It has the ability to preserve the relative position while allowing free movement of players, which is not available in the previous models such as RPGM. The results of the mobility patterns compared with the existing solution to seeing their resemblance to the expected trajectories of players during a match. To generate a realistic pattern the proposed model represents the movement of players in an application scenario, where each player during a match uses wearable sensors. These sensors collect and transfer sensed data to sink by means of inter-BASN multi

Please cite this article in press as: A. Nadeem et al., Application specific study, analysis and classification of body area wireless sensor network applications, Comput. Netw. (2015), http://dx.doi.org/10.1016/j.comnet.2015.03.002

A. Nadeem et al. / Computer Networks xxx (2015) xxx–xxx

hop routing. The impact of mobility on network performance is analysed in terms of throughput and delay. In Baseball, players push their bodies to the extreme level especially pitchers. The survey report shows that 90% pitchers reported shoulders or elbow pain, which makes throwing difficult. Therefore, a system that monitors players, measure changes in playing techniques and indicates or predicts injury will be valuable. In [68] authors proposed a BASN that calculates force, torque and other features during extreme physical activity in a baseball game. The system uses inertial measurement units worn on various segments of the athlete’s body to measure the dynamic accuracy. Low and high range sensors are used to sense the slow and fast motion of the athletes with the addition of the compass which helps in tracking the joint angles. 3.4.4. BASN for performance enhancement of sports persons The key phases involve in improving the performance of a player in professional sports are task definition, training and performance assessment. The training could result in the redefinition of task with the help of the results obtained from performance measurement. The dart is a sport where accuracy and repeatability are the key elements for performance. In [69], the authors work on the biomechanical analysis of precision targeted throwing in competitive and recreational dart by using body area sensor network of wireless inertial measurement devices. The Wireless Inertial Measurement Unit (WIMU) is used to measure speed, acceleration and throwing timing. The measurement system was validated by employing a vicon 3D motion capture system to benchmark results obtained using the WIMU solution with a ‘gold standard’ optical inertial measurement system. In [70] the authors proposed the method of determining the performance of the cyclist in the real time. The system consists of BASN consisting of motion sensor nodes that can collaboratively process the information and give immediate feedback to the cyclists. The designed portable wireless is used for monitoring of lower limb kinematics during cycling. To assess the cycling technique the measurements of the knee and joint angles have been considered. They compare the obtained results with the gold standard camera-based system, which is widely used for the same purpose.

11

microcontroller and a wireless module. The accelerometer is used to sense the gravity and compass senses the magnetic force in 3 standard dimensions. Cyber-physical Game Controller works as a converter, which converts data gathered from sink node to game input and dispatches each game input for each of the four engines. Mental stress is detected by authors in [72] using multimodal sensing in a BASN. Two biosensors are employed to collect emotional data from 20 participants. Participants were made to sit in front of emotional pictures and then their emotional stress is detected using different MLA (machine learning algorithms). 4. Analysis of BASN applications A body area sensor network is found to be a very useful technology in terms of monitoring different physical parameters of human not only for healthcare, but can also for assisting disabled/elder population, and for assessment and analysis of sports personals. Some other application area where BASN plays a dynamic role is entertainment and gaming, mood analysis, remote monitoring, safety and security of fire fighters and security personals. The tremendous advantage that a BASN offers is optimizing the ubiquitous computing, where sensor nodes seamlessly integrate with humans and has the capability of communicating with other devices and application servers. This results in less human interaction with computer. As technology becomes more advanced, the capabilities of sensor nodes, i.e. sensing, processing and storage are also enhanced whereas the requirement of power consumption of the nodes is decreased. The trend of increase in capability and decrease in power consumption offers the researchers to explore new and versatile applications of BASN in human life. We have performed an in depth review and analysis of Section 3 of BASN applications in various walks of life. On the basis of this analysis, we have classified BASN applications in four major domains as shown in Fig. 3. These main areas are further divided into sub-domains, each playing an important role in human life. Any other application of BASN which could not fit into the first four categories is included in the ‘‘others’’ category. 4.1. Comparison and analysis of BASN applications

3.5. Other use of BASN Body area sensor networks can also be implemented in entertainment, especially in gaming. For example, in [71] the authors developed a multi stream cyber physical video game and used four sensor nodes on human body to capture motion. Each sensor node autonomously collects data and directly sends to the sink node. The sink node, then sends the data to Cyber-physical Game Controller. Four game engines are used to get data from Cyber-Physical Game Controller. Four game engines are used for cameras for each direction (east, west, north, and south) and provide a better visual effect to the player as a game scene. These sensor nodes are equipped with some inertial sensors, triaxial accelerometer and tri-axial electronic compass, a

We now compare the existing work from literature on various BASN applications described above in Table 1. We first analysed proposed applications of BASN based on the parameters such as application sub-domain, type of sensors used, routing or data aggregation techniques and the wireless technology. For clarity in Table 1, we have further divided our reviews in sections based on the subdomain of BASN applications classified in Fig. 3 such as general healthcare, and animal healthcare. We have also indicated the major contribution of the proposed application of BASN in Table 1 and show the comparison of most of BASN application specific proposals discussed in this paper. On some of the key observations, our reviews in this paper are highlighted below.

Please cite this article in press as: A. Nadeem et al., Application specific study, analysis and classification of body area wireless sensor network applications, Comput. Netw. (2015), http://dx.doi.org/10.1016/j.comnet.2015.03.002

12

A. Nadeem et al. / Computer Networks xxx (2015) xxx–xxx

APPLICATIONS SPECIFIC CLASSIFICATION OF BASN

HEALTH CARE

GENERAL HEALTH CARE

DISABILITY ASSISTANCE

ANIMAL HEALTH CARE

Support Elder Persons

ACTIVITY MONITORING

TRAINING

SELFASSESSMENT

MONITORING NEONATAL HEALTH CARE

WAY FINDING BLIND AND DEAF BLIND

REHABILITATION

HUMAN ACTIVITY MONITORING

SPORTS

MONITORING FIRE FIGHTERS ACTIVITIES

PERFORMANCE ENHANCEMENT

POSTURE DETECTION

MONITORING SOLDIERS ACTIVITIES

OTHERS

MONITORING EMPLOYEES ACTIVITIES

Fig. 3. Application specific classification of BASN.

 We can see from Table 1 that most of the BASN application reviews, for example [28,30,32,33,43,68,69] have utilized inertial sensors that can measure acceleration, tilt, rotation, vibration and various degrees of motions. This is because most applications of BASN such as rehabilitations, activity monitoring, and posture detection. Require inertial sensors.  It is obvious from Table 1 that the majority of the BASN applications has not considered the routing protocol to optimize the data dissemination; we believe this is because of the use of reduced function sensor devices. Most of the applications, reviews in this paper such as [12,15,18–21] have not implemented the network layer operations of data aggregation/routing as most of these applications use single hope architecture of BASN (i.e. All the sensor nodes are directly connected to the sink device). This is the reason we have indicated ‘‘Not Considered’’ in Routing/Data Aggregation technique column of Table 1. With the development of fully functional sensor devices in future, we believe that the data aggregation and routing techniques for BASN could further enhance the performance of the BASN in major applications in terms of energy consumption and Quality of Service. However, some researchers have proposed routing mechanism for their applications such as [57,58].  Test beds are used mostly to implement the ideas and few researchers have considered simulations, this is because very few simulators supports BASN protocols are available such as Castalia (a package used with OMNET++) or NS-click – an extension of the NS simulator platform. Finally, we believe sensor nodes must be capable of performing multiple sensing tasks so that the number of nodes deployed on the body can be reduced. 5. Novel BASN applications proposals In the previous section we have reviewed the existing application of BASN. In this section we will suggest novel application scenarios, where BASN could be deployed in the future.

5.1. BASN to identify frostbites People who spend significant time in very cold weather (skiers, hikers, soldiers, ice skaters, outdoor workers, etc.) may suffer from frostbite. Frostbite is a condition of skin damage if the skin is exposed to temperatures below than freezing point of skin. Toes, fingers, chin, cheeks, ears and nose are the body parts which are affected most. It falls in the category of cold related emergencies. A BASN can be used to identify and inform about the frostbite condition of the person. In frostbite condition, the temperature of the body decrease below zero degrees centigrade. Temperature sensors can be placed on the parts of the body which are highly expected to frostbite (the body parts which are far from the heart and the parts which are exposed) which will sense and relay temperature information. This information could be analysed on the cell phone/ PDA for frostbite condition and can be used to provide an alert to the susceptible. 5.2. BASN to assist blind swimmers BASN can also be useful to assist blind or visually impaired sportspersons such as swimming. A visually impaired person cannot see when they are approaching a wall at the end of the lap. One simple approach could be to use human assistance. For example, human tappers stand at both ends of the lane and taps the swimmer on the shoulder with a long rod when they approach the wall. There are two types of solutions have been proposed [73] for a blind or visually impaired swimmer to swim and practice independently; (a) mechanical solution and (b) electronic solution. The design of mechanical solution is very simple and safe. A waterfall is created with the help of mechanical pump under which the swimmers is passed. The mechanical solution worked well, but it is expensive, difficult to transport, hard to set up and cumbersome to operate. The electronic solution consists of two base stations and one receiver worn by the swimmer. The base stations create a wireless boundary around themselves; they are placed on the deck on either end of the lane above the water at a user

Please cite this article in press as: A. Nadeem et al., Application specific study, analysis and classification of body area wireless sensor network applications, Comput. Netw. (2015), http://dx.doi.org/10.1016/j.comnet.2015.03.002

Type of sensor used

Routing/data aggregation technique

Wireless standard

Major contribution

Implementation

ECG, respiration, activity

Not considered

IEEE 802.15.4

Test bed

ECG, heart rate (HR), tri-axial accelerometer and temperature sensors Temperature, light, pressure, humidity and acceleration external heartbeat monitor EEG Sensor

Not considered

IEEE 802.15.4

Sensor embedded smart cloths for monitoring Vital Body Signs Heart rate detection using fuzzy logic

Not considered

IEEE 802.15.4 & 802.11

Monitoring of physiological conditions and send to clinical systems Compressed the sensed data and decompress on desktop computers to reduced nodes energy consumption during monitoring of EEG

Simulation

Application domain: Neonatal healthcare Bouwstra et al. [20] Smart Jacket

ECG

Not considered

Wired

Test bed

Basak et al. [21]

Temperature and pulse rate sensor

Not considered

IEEE 802.15.4

Smart jacket for monitoring ECG of Neonatal A system to monitor temperature, pulse rate to assist children in cough, sneeze

Body temperature, heart rate, respiratory rate, ambient temperature, humidity sensors, wind pattern and its behavioural factor like feed and water intake

Not considered

IEEE 802.15 & 802.15.4

Monitoring of physiological and behavioural changes in animal

Test bed

IRIS transceiver modules from Crossbow™ Inertial sensors

Not considered Not considered

IEEE 802.15.4 IEEE 802.15.4

Test bed Test bed

3D accelerometer, 3D gyroscope, sEMG sensors

Not considered

IEEE 802.15.4

Inertial (accelerometers, Magnetic sensor and gyroscope), Load sensor

Not considered

ANT Radio Link, Bluetooth

Calculate arm movement through RSSI Remotely monitor the rehabilitation process in Stroke Patients Monitor movement of spine, pelvis and muscle fatigue to support patients with low back pain Calculate Hip movement after Hip Surgery

Accelerometer, Gyroscope

Not considered

Bluetooth

A system to support motor rehabilitation of impaired patients

Test bed

Application domain: Posture detection Anania et al. [34] ProeTEX Project

Triaxial Accelerometer

Not considered

Bluetooth

Proposed algorithm for abnormal fall detection

Test bed

Application domain: Way finding Willis and Helal [35] N/A

RFID Reader, RFID Tags

Not considered

Bluetooth

Provide support to blind persons for navigation and way finding

Test bed

Not considered

Not Mentioned

Test bed

Not considered

IEEE 802.15.4

Remotely monitoring of abnormal conditions in elder persons Activity monitoring elder person with

Author

BASN application name

Application domain: General healthcare Habetha [12] MyHeart Tanaka et al. [15]

Button System

Ismail and Cuneyt [18]

Health Face

Zhang et al. [19]

Compressed Sensing of EEG

KiMS

Application domain: Animal healthcare Schoenig et al. [27] HealthCare

Application domain: Rehabilitation Guraliuc et al. [28] N/A Zhang and Sawchuk RehabSPOT [29] Chhikara et al. [30] IMPAIRED

PekkaIso-Ketola et al. [32]

HipGuard

Application domain: Activity monitoring Watanabe and Saito N/A [33]

Application domain: Supporting elder persons Yazaki and Matsunaga ACDS Triaxial Accelerometer, ECG and [36] Temperature Sensor Patel et al. [37] N/A Triaxial Accelerometer, Triaxial Gyroscope

Not considered

Test bed

Simulation

Simulation

Test bed

Test bed

A. Nadeem et al. / Computer Networks xxx (2015) xxx–xxx

Test bed

(continued on next page)

13

Please cite this article in press as: A. Nadeem et al., Application specific study, analysis and classification of body area wireless sensor network applications, Comput. Netw. (2015), http://dx.doi.org/10.1016/j.comnet.2015.03.002

Table 1 Comparison of application specific proposals of BASN.

14

Author

BASN application name

Type of sensor used

Routing/data aggregation technique

Wireless standard

Major contribution

Implementation

Baek et al. [38]

Fall Detection System Fall Detection System

Triaxial Accelerometer, Triaxial Gyroscope

Not considered

IEEE 802.15.4

Parkinson disease Fall detection in elder person

Test bed

Triaxial Accelerometer, Triaxial Gyroscope and magnetometer

Not considered

IEEE 802.15.4

Fall detection in elder person

Test bed

Dehydration sensor (electrochemical sensor)

Not considered

Not Mentioned

Not Mentioned

Two-axial gyroscope, tri-axial accelerometer, heartbeat sensor

ZigBee

IEEE 802

Proposed a human activity monitoring system for firefighters Develop sensor based garments for activity monitoring, fall detection

RS232 protocol

Not Mentioned

Not Mentioned

Accelerometer and proximity sensor

RS232 protocol

Not Mentioned

Proposed a method to detect sitting, sitting-reclining, lying-down, standing, postures using BASN used HMM for activity detection

CO, HCN, and TelosB, Raspberry Pi (RP)

Not mentioned in the paper

Wi-Fi

Location of fire-fighter determined using RSSI

They proposed their own routing mechanism Collision free MAC protocol Not Mentioned

Not mentioned in the paper

An event driven routing protocol for measuring fatigue of a soldier is presented

Simulation using Quaint, tested also Simulation done on Mat lab

Not Mentioned

Proposed a method to recognition 7 different body postures Proposed a method to estimate the impact of blast and its effects on soldiers in war

Not Mentioned

MicaZ motes

Multi-hop routing algorithms

Mobile ad-hoc network

Collected empirical data during multiple games

Simulation MATLAB

MicaZ mote

A tunable flooding scheme Not considered

Wireless

Proposed a routing protocol to monitor the activity of soccer players It measures the fatigue level of the player during the match.

Test bed

Not considered

Not Mentioned

Proposed a golf training system

MATLAB

Not considered

Wireless

Training of Indoor cyclist in a room (temperature 20°c, humidity 50%)

Test bed

Threshold detection/timer routine

Wireless

Sweat analysis of sportsperson

Test bed

Felisberto et al. [39]

Application domain: Fire fighter activity Xu [40] Human Activity Monitoring Magenes et al. [51] Project ProeTEX

Application domain: Posture and physical context detection Quwaider and Biswas Physical context A two-axes piezoelectric accelerometer, [42] detection Mica2Dot mote radio nodes Quwaider and Biswas [43] Ghosh et al. [44]

Human Activity Monitoring N/A

Javaid et al. [45]

No name mentioned in the paper

Temperature, heartbeat, and blood glucose sensor

Farrell et al. [47]

Human Activity Monitoring Monitoring Soldiers Activity

Mimosa Node (with three accelerometers)

Lim et al. [48]

Application domain: Monitoring Sivaraman et al. [57] Body area network to collect the physiological data of the athlete Dhamdhere et al. [58] Monitor soccer players Akram et al. [59] THE-FAME Application domain: Training Ghasemzadeh [55] Golf Swing Training Coyle [64]

Sweat analysis

Morris [65]

Analysis of sweat pH and sodium levels

Accelerometer, Temperature, EEG, SpO2 sensors

In-vivo sensor

TelosB, motion sensor, accelerometer, gyroscope Textile-based Sensors, Sweat rate Sensor, humidity sensor, pH sensor, sweat conductivity sensors In-vitro and in vivo sensor

Not Mentioned

IEEE 802.15.4.

Not Mentioned

Framework

Not Mentioned

MATLAB

A. Nadeem et al. / Computer Networks xxx (2015) xxx–xxx

Please cite this article in press as: A. Nadeem et al., Application specific study, analysis and classification of body area wireless sensor network applications, Comput. Netw. (2015), http://dx.doi.org/10.1016/j.comnet.2015.03.002

Table 1 (continued)

Simulation MATLAB Proposed a method to analyze mental stress and emotion detected Not Mentioned Not considered Application domain: Monitoring mental stress Aliberas and Wolisz Others Biosensor (ECG and GSR) [72]

Not Mentioned A better realistic interaction experiences provided to user IEEE 802.15.4 wireless Not considered Application domain: Gaming Wu [71] Others

Inertial sensors, triaxial accelerometer, triaxial electronic compass

TinyOs Monitoring tilt, force and timing IEEE 802.15.4 Not considered Application domain: Performance enhancement Electromyography (EMG), sensors, Texan Walsh et al. [69] BASN for inertial pressure mapping sensor, measurement devices

Test bed Proposed indoor positioning system to assist blind people in sports Wireless Not considered Vibration sensor

Simulation OPNET

IEEE 802.15.4

Application domain: Assessment Garcia et al. [60] BASN to determine player physical state ZigBee A system to help coach to monitor & identify tired players in soccer Lee [66] Indoor Positioning System

Biosensor

Not considered

Test bed Custom wireless protocol Lapinski et al. [68]

Sports

Inertial sensor, ADXL193 accelerometers, ADRX300 gyroscopes

Not considered

Training & monitoring force, torque and other features in a baseball game

A. Nadeem et al. / Computer Networks xxx (2015) xxx–xxx

15

defined distance. On crossing the electronic boundary, it vibrates to alert the blind swimmer. The electronic solution worked with a limited success. It is directionally dependent and the receiver did not perform reliably. We believe that a body area sensor network comprises of wireless waterproof sensors capable of detecting the motion of the person, can assist blind or visually impaired swimmer. 5.3. BASN to assist diabetic patients There are several real-time monitoring systems that have been proposed for monitoring the diabetic patient. However, one of the shortcomings of research in this area is the focus on alerting diabetic patients to stop eating sweets when their glucose level is high or urge the patient to eat sweets if the glucose level is low. A miniature taste sensing system has proposed by various authors such as in [74,75]. But we believe with the development of intelligent implanted sensors for taste sensing and instant blood glucose monitoring a BASN system could then be built for this purpose. This system will allow immediate alerts to a person, its caregiver, or clinical staff when the sugar level is above or below the thresholds. This will be very effective for the diabetic patients. 6. Conclusion In the last few years BASN has evolved as a major application area of wireless sensor networks and researchers have proposed its application in various fields. Researchers from academics and industries are continuously exploring the potential of BASN in various walks of life. The main focus of the researcher in BASN is to analyse parameters of sensor devices such as energy consumption, efficient MAC and Physical Layer Protocol [3–5,9], optimization of on-node computation and data compression. However, limited research exists on exploring the application of BASN in different areas of life. Therefore, in this paper, we have tried to fill this gap and reviewed BASN proposals in the literature from the application point of view. We classify BASN existing applications in four broad areas i.e. healthcare, disability assistance, sports and human activity monitoring. For clarity of the work, each broader area is further divided into its sub-domain as shown in Fig. 3. In the review of an application specific survey of BASN, it can be easily concluded that physiological sensor in healthcare category and inertial sensors in the category of sports, rehabilitation and activity monitoring are playing vital role in various applications of BASN. In analysis and comparing the main proposals through Table 1, we have highlighted a number of key similarities and differences in existing applications of BASN. The principal finding of the comparison is that most of existing BASN applications are not considered network layer operations such as routing protocol or data aggregation technique in their implementation. We believe that the data aggregation and routing techniques for BASN could further enhance the performance of the BASN in major applications in terms of energy consumption and Quality of Service. Another principal finding from the comparison

Please cite this article in press as: A. Nadeem et al., Application specific study, analysis and classification of body area wireless sensor network applications, Comput. Netw. (2015), http://dx.doi.org/10.1016/j.comnet.2015.03.002

16

A. Nadeem et al. / Computer Networks xxx (2015) xxx–xxx

and analysis of BASN application is that most of the applications implemented their research through test bed. Simulation can reduce the cost and time incurred on practical implementation of BASN. Additionally, this survey discovered that there are still some unexplored areas of life where BASN applications can be used. We have highlighted novel areas where BASN can be implemented in future.

References [1] IEEE, IEEE Standard for Local and Metropolitan Area Networks – Part 15.6: Wireless Body Area Networks, IEEE Std 802.15.6-2012, February 2012, pp. 1–271. [2] M.A. Hanson, H.C. Powell, A.T. Barth, K. Ringgenberg, B.H. Calhoun, J.H. Aylor, J. Lach, Body area sensor networks: challenges and opportunities, IEEE Comput. Soc. J. Comput. 42 (1) (2009) 58–65. [3] K.S. Kwak, M. Ameen, D. Kwak, C. Lee, H. Lee, A study of mac layer protocols for WBAN, Int. J. Sensors 10 (2009) 834–840. [4] S.R. Islam, K.S. Kwak, A comprehensive study of channel estimation for WBAN-based healthcare systems: feasibility of using multiband UWB, Springer J. Med. Syst. 36 (3) (2012) 1553–1567. [5] S. Ullah, H. Higgins, B. Braem, B. Latre, C. Blondia, I. Moerman, S. Saleem, Z. Rahman, K.S. Kwak, A comprehensive survey of wireless body area networks on PHY, MAC and network layer solutions, Springer J. Med. Syst. 36 (3) (2012) 1065–1094. [6] D.M. Barakah, M. Ammad-uddin, A survey of challenges and applications of wireless body area network (WBAN) and role of a virtual doctor server in existing architecture, in: Proceedings of Third International Conference on Intelligent Systems, Modelling and Simulation (ISMS), 2012. [7] Min Chen, Sergio Gonzalez, Athanasios Vasilakos, Huasong Cao, Victor C.M. Leung, Body area networks: a survey, Springer Mob. Netw. Appl. J. 16 (2) (2011) 171–193. [8] Wikipedia, Health Care, 2013. [9] W.H. Organization, International Day of Older Persons, 2012. . [10] WHO, WORLD REPORT ON DISABILITY, 2011. [11] OECD, Health at a Glance 2011, 2011. [12] J. Habetha, The MyHeart project – fighting cardiovascular diseases by prevention and early diagnosis, in: 28th IEEE, EMBS Annual International Conference, New York City, 2006. [13] J. Luprano, J. Solà, S. Dasen, J.M. Koller, O. Chételat, Combination of body sensor networks and on-body signal processing algorithms: the practical case of MyHeart project, in: International Workshop on Wearable and Implantable Body Sensor Networks, 2006, BSN 2006, 2006. [14] M. Pacelli, G. Loriga, N. Taccini, R. Paradiso, Sensing fabrics for monitoring physiological and biomechanical variables: E-textile solutions, in: 3rd IEEE/EMBS International Summer School on Medical Devices and Biosensors, 2006, 2006. [15] T. Tanaka, T. Fujita, K. Sonoda, M. Nii, K. Kanda, K. Maenaka, A.C. C. Kit, S. Okochi, K. Higuchi, Wearable health monitoring system by using fuzzy logic heart-rate extraction, in: World Automation Congress (WAC), 2012, 2012. [16] D.H. Lee, A. Rabbi, J. Choi, R. Fazel-Rezai, Development of a mobile phone based e-health monitoring application, Int. J. Adv. Comput. Sci. Appl. 3 (3) (2012) 38–43. [17] S. Kannan, Wheats: a wearable personal healthcare and emergency alert and tracking system, Eur. J. Sci. Res. 85 (3) (2012) 382–393. [18] Kirbas Ismail, Cuneyt Bayilmis, HealthFace: a web-based remote monitoring interface for medical healthcare systems based on wireless body area sensor network, Turk. J. Electron. Eng. Comput. Sci. 20 (4) (2012) 629–638. [19] Bhaskar D. Rao, Zhilin Zhang, Tzyy-Ping Jung, Scott Makeig, Compressed sensing of EEG for wireless telemonitoring with low energy consumption and inexpensive hardware, IEEE Trans. Biomed. Eng. 60 (1) (2013) 221–224. [20] S. Bouwstra, L. Feijs, W. Chen, S.B. Oetomo, Smart jacket design for neonatal monitoring with wearable sensors, in: Sixth International Workshop on Wearable and Implantable Body Sensor Networks, 2009, BSN 2009, 2009. [21] A. Basak, S. Narasimhan, S. Bhunia, KiMS: Kids’ Health Monitoring System at day-care centres using wearable sensors and vocabularybased acoustic signal processing, in: 13th IEEE International Conference on e-Health Networking Applications and Services (Healthcom), 2011, 2011.

[22] L. Nagl, R. Schmitz, S. Warren, T. Hildreth, H. Erickson, D. Andresen, Wearable sensor system for wireless state-of-health determination in cattle, in: Engineering in Medicine and Biology Society, 2003. Proceedings of the 25th Annual International Conference of the IEEE, 2003. [23] B. Sowell, M. Branine, J. Bowman, M. Hubbert, H. Sherwood, W. Quimby, Feeding and watering behaviour of healthy and morbid steers in a commercial feedlot, J. Anim. Sci. 77 (5) (1999) 1105–1112. [24] A. Martinez, S. Schoenig, D. Andresen, S. Warren, Ingestible pill for heart rate and core temperature measurement in cattle, in: 28th Annual International Conference of the IEEE on Engineering in Medicine and Biology Society, 2006, EMBS’06, 2006. [25] M. Chu, S. Iguchi, D. Takahashi, T. Arakawa, H. Kudo, K. Mitsubayashi, Wearable biosensor for monitoring tear glucose on rabbit eye as novel device of body sensor network, in: 5th International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 2009, 2009. [26] R.R. Fletcher, K.-I. Amemori, M. Goodwin, A.M. Graybiel, Wearable wireless sensor platform for studying autonomic activity and social behavior in non-human primates, in: Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE, 2012. [27] S. Schoenig, T. Hildreth, L. Nagl, H. Erickson, M. Spire, D. Andresen, S. Warren, Ambulatory instrumentation suitable for long-term monitoring of cattle health, in: 26th Annual International Conference of the IEEE on Engineering in Medicine and Biology Society, 2004, IEMBS’04, 2004. [28] A. Guraliuc, A. Serra, P. Nepa, G. Manara, F. Potorti, Detection and classification of human arm movements for physical rehabilitation, in: Antennas and Propagation Society International Symposium (APSURSI), 2010. [29] M. Zhang, A.A. Sawchuk, A customizable framework of body area sensor network for rehabilitation, in: 2nd International Symposium on Applied Sciences in Biomedical and Communication Technologies, 2009, ISABEL 2009, 2009. [30] A. Chhikara, A.S. Rice, A.H. McGregor, F. Bello, Wearable Device for Monitoring Disability Associated with Low Back Pain, World, vol. 10, 2008, pp. 13. [31] B. Lo, S. Thiemjarus, R. King, G.-Z. Yang, Body sensor network-a wireless sensor platform for pervasive healthcare monitoring, in: The 3rd International Conference on Pervasive Computing, 2005. [32] P. Iso-Ketola, T. Karinsalo, J. Vanhala, HipGuard: a wearable measurement system for patients recovering from a hip operation, in: Second International Conference on Pervasive Computing Technologies for Healthcare, 2008, Pervasive Health 2008, 2008. [33] T. Watanabe, H. Saito, Tests of wireless wearable sensor system in joint angle measurement of lower limbs, in: Annual International Conference of the IEEE on Engineering in Medicine and Biology Society, EMBC, 2011, 2011. [34] G. Anania, A. Tognetti, N. Carbonaro, M. Tesconi, F. Cutolo, G. Zupone, D. De Rossi, Development of a novel algorithm for human fall detection using wearable sensors, in: Sensors, 2008 IEEE, 2008. [35] S. Willis, S. Helal, RFID information grid for blind navigation and wayfinding, in: Proceedings of Ninth IEEE International Symposium on Wearable Computers, 2005, 2005. [36] S. Yazaki, T. Matsunaga, A proposal of an abnormal condition detection system for elderly people using wireless wearable biosensor, in: SICE Annual Conference, 2008, 2008. [37] S. Patel, K. Lorincz, R. Hughes, N. Huggins, J.H. Growdon, M. Welsh, P. Bonato, Analysis of feature space for monitoring persons with Parkinson’s disease with application to a wireless wearable sensor system, in: 29th Annual International Conference of the IEEE on Engineering in Medicine and Biology Society, 2007, EMBS 2007, 2007. [38] Woon-Sung Baek, Dong-Min Kim, F. Bashir, Jae-Young Pyun, Real life applicable fall detection system based on a wireless body area network, in: Consumer Communications and Networking Conference (CCNC), 2013 IEEE, 2013. [39] F. Felisberto, F. Fdez-Riverola, A. Pereira, A ubiquitous and low-cost solution for movement monitoring and accident detection based on sensor fusion, Sensors 14 (5) (2014) 8961–8983. [40] Bo Xu, Human Activity Recognition Using Body Area Sensor Networks, Doctoral Dissertation, Oklahoma State University, 2009. [41] E. Jovanov, A. Milenkovic, C. Otto, P. De Groen, B. Johnson, S. Warren, G. Taibi, A WBAN system for ambulatory monitoring of physical activity and health status: applications and challenges, in: 27th Annual International Conference of the Engineering in Medicine and Biology Society, 2005, IEEE-EMBS 2005, 2006.

Please cite this article in press as: A. Nadeem et al., Application specific study, analysis and classification of body area wireless sensor network applications, Comput. Netw. (2015), http://dx.doi.org/10.1016/j.comnet.2015.03.002

A. Nadeem et al. / Computer Networks xxx (2015) xxx–xxx [42] M. Quwaider, S. Biswas, Physical context detection using wearable wireless sensor networks, J. Commun. Softw. Syst. 4 (3) (2008) 191–201. [43] M. Quwaider, S. Biswas, Body posture identification using hidden Markov model with a wearable sensor network, in: Proceedings of the ICST 3rd International Conference on Body Area Networks, 2008. [44] S. Ghosh, S. Chakraborty, A. Jamthe, D. Agrawal, Comprehensive monitoring of firefighters by a wireless body area sensor network, in: IEEE Tenth International Conference on Wireless and Optical Communications Networks (WOCN), 2013, 2013. [45] N. Javaid, S. Faisal, Z. Khan, D. Nayab, M. Zahid, Measuring fatigue of soldiers in wireless body area sensor networks, in: Eighth International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA), 2013, 2013. [46] S. Biswas, M. Quwaider, Remote monitoring of soldier safety through body posture identification using wearable sensor networks, in: SPIE Defence and Security Symposium, 2008. [47] E. Farella, A. Pieracci, L. Benini, A. Acquaviva, A wireless body area sensor network for posture detection, in: Proceedings. 11th IEEE Symposium on Computers and Communications (ISCC ‘06), 2006. [48] H.B. Lim, D. Ma, B. Wang, Z. Kalbarczyk, R.K. Iyer, K.L. Watkin, A soldier health monitoring system for military applications, in: International Conference on Body Sensor Networks (BSN), 2010, 2010. [49] G. Marchand, A. Bourgerette, M. Antonakios, Y. Colletta, N. David, F. Vinet, C. Gallis, Development of a hydration sensor integrated on fabric, in: 6th International Workshop on Wearable Micro and Nano Technologies for Personalized Health (pHealth), 2009, 2009. [50] D. Curone, G. Dudnik, G. Loriga, J. Luprano, G. Magenes, R. Paradiso, A. Tognetti, A. Bonfiglio, Smart garments for safety improvement of emergency/disaster operators, in: Engineering in Medicine and Biology Society, 2007, EMBS 2007, 29th Annual International Conference of the IEEE, 2007. [51] G. Magenes, D. Curone, L. Caldani, E.L. Secco, Fire fighters and rescuers monitoring through wearable sensors: the ProeTEX project, in: Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE, 2010. [52] M. Iacono, P. Baronti, G. Romano, G. Amato, S. Chessa, Monitoring Fire-Fighters Operating in Hostile Environments with Body-Area Wireless Sensor Networks1, VGR, 2006. [53] A. Dinh, D. Teng, L. Chen, Y. Shi, C. McCrosky, J. Basran, D. Bello-Hass, et al., Implementation of a physical activity monitoring system for the elderly, people with built-in vital sign and fall detection, in: Sixth International Conference on Information Technology: New Generations, 2009, ITNG’09, 2009. [54] F. Lantz, B. Lewin, E. Jansson, J. Antoni, K. Brunberg, P. Hallbjorner, A. Rydberg, WBAN mass: a WBAN-based monitoring application system, in: 2nd IET Seminar on Antennas and Propagation for Body-Centric Wireless Communications, 2009, 2009. [55] H. Ghasemzadeh, V. Loseu, E. Guenterberg, R. Jafari, Sport training using body sensor networks: a statistical approach to measure wrist rotation for golf swing, in: Proceedings of the Fourth International Conference on Body Area Networks, 2009. [56] M. Walsh, J. Barton, B. O’Flynn, C. O’Mathuna, M. Tyndyk, A multitechnology approach to identifying the reasons for lateral drift in professional and recreational darts, in: International Conference on Body Sensor Networks (BSN), 2011, 2011. [57] V. Sivaraman, S. Grover, A. Kurusingal, A. Dhamdhere, A. Burdett, Experimental study of mobility in the soccer field with application to real-time athlete monitoring, in: IEEE 6th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), 2010, 2010. [58] A. Dhamdhere, H. Chen, A. Kurusingal, V. Sivaraman, A. Burdett, Experiments with wireless sensor networks for real-time athlete monitoring, in: 2010 IEEE 35th Conference on Local Computer Networks (LCN), 2010. [59] S. Akram, N. Javaid, A. Tauqir, A. Rao, S. Mohammad, THE-FAME: THreshold Based Energy-Efficient FAtigue MEasurement for wireless body area sensor networks using multiple sinks, in: Eighth International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA), 2013, 2013. [60] M. Garcia, A. Catal’a, J. Lloret, J.J. Rodrigues, A wireless sensor network for soccer team monitoring, in: International Conference on Distributed Computing in Sensor Systems and Workshops (DCOSS), 2011, 2011. [61] A. Salazar, A. Silva, C. Borges, M. Correia, An initial experience in wearable monitoring sport systems, in: 10th IEEE International

[62]

[63]

[64]

[65]

[66]

[67]

[68]

[69]

[70]

[71]

[72]

[73] [74]

[75]

17

Conference on Information Technology and Applications in Biomedicine (ITAB), 2010, 2010. M. Lauzier, P. Ferrand, H. Parvery, A. Fraboulet, J.-M. Gorce, et al., WBANs for live sport monitoring: an experimental approach, early results and perspectives, in: Euro-cost IC1004-European Cooperation in the Field of Scientific and Technical Research, 2012. J. Neville, A. Wixted, D. Rowlands, D. James, Accelerometers: an underutilized resource in sports monitoring, in: Sixth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 2010, 2010. S. Coyle, D. Morris, K.-T. Lau, D. Diamond, F. Di Francesco, N. Taccini, M.G. Trivella, D. Costanzo, P. Salvo, J.-A. Porchet, et al., Textile sensors to measure sweat pH and sweat-rate during exercise, in: 3rd International Conference on Pervasive Computing Technologies for Healthcare, 2009, PervasiveHealth 2009, 2009. D. Morris, B. Schazmann, Y. Wu, S. Coyle, S. Brady, J. Hayes, C. Slater, C. Fay, K. T. Lau, G. Wallace, et al., Wearable sensors for monitoring sports performance and training, in: 5th International Summer School and Symposium on Medical Devices and Biosensors, 2008, ISSS-MDBS 2008, 2008. M.-H. Lee, Indoor positioning system for moving objects on an indoor for blind or visually impaired playing various sports, J. Electr. Eng. Technol. 4 (1) (2009) 131–134. L. De Nardis, D. Domenicali, M. Di Benedetto, Mobility model for body area networks of soccer players, in: Wireless Technology Conference (EuWIT), 2010 European, 2010. M. Lapinski, E. Berkson, T. Gill, M. Reinold, J.A. Paradiso, A distributed wearable, wireless sensor system for evaluating professional baseball pitchers and batters, in: International Symposium on Wearable Computers, 2009, ISWC’09, 2009. M. Walsh, J. Barton, B. O’Flynn, C. O’Mathuna, M. Tyndyk, Capturing the overarm throw in darts employing wireless inertial measurement, in: Sensors, 2011 IEEE, 2011. R. Marin-Perianu, M. Marin-Perianu, D. Rouffet, S. Taylor, P. Havinga, R. Begg, M. Palaniswami, Body area wireless sensor networks for the analysis of cycling performance, in: Proceedings of the Fifth International Conference on Body Area Networks, 2010. C.-H. Wu, Y.-T. Chang, Y.-C. Tseng, Multi-screen cyber-physical video game: an integration with body-area inertial sensor networks, in: 8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), 2010, 2010. M.R. Aliberas, I.A. Wolisz, Mental Stress Detection Using Multimodal Sensing in a Wireless Body Area Network, Master Thesis, Technical University of Berlin, September 2011. P.B. Fischer, R. Stribinger, Pool Wall Detection for Blind Swimmers, 2007. G. Sehra, M. Cole, J. Gardner, Miniature taste sensing system based on dual SH-SAW sensor device: an electronic tongue, Sens. Actuat. B: Chem. 103 (1) (2004) 233–239. N. Angkawisittpan, T. Manasri, Determination of sugar content in sugar solutions using interdigital capacitor sensors, Meas. Sci. Rev. 12 (1) (2012) 8–13.

Adnan Nadeem is an assistant professor and in charge department of computer science, Federal Urdu University of Arts, Science & Technology, Pakistan. He is a fellow of Higher Education Commission UK and a member of IEEE & IEEE communication Society. He is a Higher Education Commission approved PhD supervisor. He has published his work in well reputed international journals and conferences. He has served as Technical Program Committee chair for ICICTT 2013 and as a reviewer for various international conferences and journals. He received his PhD degree from the Faculty of Engineering and Physical Sciences, Centre for Communication Systems Research (CCSR), University of Surrey, UK. His principal research interest includes security issues in wireless ad hoc networks, intrusion detection & prevention, secure routing, quality of services, performance analysis in MANETs, Wireless Sensor Networks, Body Area Sensor Networks and their applications.

Please cite this article in press as: A. Nadeem et al., Application specific study, analysis and classification of body area wireless sensor network applications, Comput. Netw. (2015), http://dx.doi.org/10.1016/j.comnet.2015.03.002

18

A. Nadeem et al. / Computer Networks xxx (2015) xxx–xxx Muhammad Azhar Hussain is studying in the PhD (CS) discipline of Federal Urdu University of Arts, Science and Technology. He also works in the National Telecommunication Corporation. His research areas are Information System, Mobile technology applications in the healthcare sector and disability assistance. He is also interested in MANET and sensor networks, including their security and routing issues.

Sarwat Iqbal is a PhD Scholar and a visiting faculty member in Computer Science Department, Federal Urdu University of Arts, Science and Technology. Her research areas are mobile technology applications in the healthcare, knowledge management and enterprise architecture. In addition she is also interested in mobile ad hoc network and sensor networks, including their security and routing issues and application of statistical techniques in MANET and healthcare.

Obaid Ullah Owais Khan is working as an Assistant Director (IT) and Deputy Secretary (IT/ICT) at Pakistan Standards and Quality Control Authority. He is involved in the standardization process of IT/ICT in Pakistan. He has done his BE (IT) from Hamdard University. He is doing his MS (CS) from Federal Urdu University of Arts, Science and Technology, Karachi, Pakistan. He is an active member of Joint Technical Committee 1 of the International Organization for Standardization (ISO) and the International Electro technical Commission (IEC). His research areas include mobile ad hoc network and its security, wireless sensor network, wireless body area network and mobile applications for healthcare.

Kamran Ahsan is an assistant professor at the department of computer science Federal Urdu University of Arts, Science & Technology. He has served as PhD researcher and lecturer in FCET (Faculty of Computing, Engineering and Technology) and, web researcher in Centre for Ageing and Mental Health, Staffordshire University, UK since 2005. He has an MSc in Mobile Computer Systems from Staffordshire University in Computer Science from University of Karachi. He is Visiting Faculty at University of Karachi. He is a consultant to businesses in IT applications, software development and web tools. His research interests are mobile technology applications in healthcare including knowledge management.

Abdul Salam has completed his MS (Computer Science) from Federal Urdu University of Arts, Science and Technology, Karachi. He is a a visiting faculty member in Computer Science Department, Federal Urdu University of Arts, Science and Technology. His research areas are mobile ad hoc network and sensor networks, including their security issues. Wireless sensor networks, Wireless Body Area Sensor Network, including Quality of Service in WBASN, Mobile technology applications in the healthcare sector.

Please cite this article in press as: A. Nadeem et al., Application specific study, analysis and classification of body area wireless sensor network applications, Comput. Netw. (2015), http://dx.doi.org/10.1016/j.comnet.2015.03.002