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Wearable sensing systems for biomechanical parameters monitoring
Available online at www.sciencedirect.com
ScienceDirect Materials Today: Proceedings 7 (2019) 560–565
www.materialstoday.com/proceedings
BioM&M_2018
Wearable sensing systems for biomechanical parameters monitoring Giorgio De Pasquale*, Leonardo Mastrototaro, Valentina Ruggeri Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129, Italy
Abstract The use of wearable sensing systems in clinical rehabilitation has demonstrated high potential in improving the trainings effectiveness and reducing mobility recovery time. The monitoring of biomechanical parameters in upper limbs provides valuable supports to neurological patient’s recovery trainings, where the self-awareness of human body must be restored. After preliminary activities of the author’s group, which led to the GoldFinger prototype for HMI applications, the development of the sensing glove described in this paper has been carried out. Here, small dimensions and lightness are accompanied by flexibility of supports, reliable connectors and interfaces and low power consumption. © 2018 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/). Selection and Peer-review under responsibility of 1st International Conference on Materials, Mimicking, Manufacturing from and for Bio Application (BioM&M). Keywords: Biomechanics; HMI; smart fabrics; e-textiles; neurologic rehabilitation.
1. Introduction The number of people affected by neurological pathologies and/or traumas is progressively increasing because of life expectancy growth. The stroke, for instance, is caused by loss of blood supply (ischemia) or bleeding (hemorrhage) in the brain. Respect other cardiovascular diseases, stroke incidence is dramatically increasing in elderly people. Other frequent neurologic pathologies are neurodegenerative dementias which cause invalidity of motion abilities (e.g. control of part or entire body, called “plegia”), disorders of language, memory and awareness.
* Corresponding author. Tel.: +39-011-0906912; fax: +39-011-0906999. E-mail address: [email protected] 2214-7853 © 2018 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Selection and Peer-review under responsibility of 1st International Conference on Materials, Mimicking, Manufacturing from and for Bio Application (BioM&M).
G. De Pasquale et al./ Materials Today: Proceedings 7 (2019) 560–565
561
Similar symptoms are present in people subjected to traumas associated to neurological injuries like road accidents. In this case, the consequent neurological deficiency affects younger ranges of population.. The incapacity to do even simple everyday actions and to interact with things and people has tremendous impact on the psychological health of patients. In addition to motion inabilities, the psychological uneasiness produces propensity to changes of mood and cognitive disorders. These disturbances are heavily penalizing the patient recovery and therapies effectiveness and duration. Highly targeted therapies still under investigation are addressed to these patients. The most advanced theories, supported by pilot campaigns on patients, are based on multisensory rehabilitation trainings with wearable devices and/or virtual reality. The patient can experience the motion of the body through direct measurements in the parts where the movement is inhibited; the recovery process has benefits in terms of effectiveness and duration. Different brain areas control the body movements and feelings. When a neurological pathology or trauma damages a certain part of the brain, that part may not work as well as it did before. This can cause problems with controlling movements, speaking, seeing or feeling. After a stroke, for example, patients often experience dramatically constrained mobility and partial paralysis of the limbs. Especially movements of the upper extremities, like grasping movements, are frequently hampered (reduced accuracy/speed, high variability). Common rehabilitative approaches are based on massive practice of a given motor task supervised by a therapist. Traditional rehabilitation consists of multiple repetitions of twisting and lifting small objects and different kinds of arm and wrist rotations, where a range of normal day-to-day activities (turning a key, picking up a can, moving a doorknob, etc.) is simulated. However, in addition to traditional rehabilitations, the most advanced theories of neuropsychology are addressed to “multisensory rehabilitation trainings”, thanks to the support of wearable devices. They demonstrated the correlation of movements with “body ownership”, with the perspective to improve the rehabilitation of hemiplegic patients through the monitoring of motion recovery. Active systems for supporting trainings of neurological patients provide artificial actuation to muscles to regain mobility. In some cases [1], the EMG stimulus is detected, filtered and used to actuate a motor enabling the desired motion. Other devices [2] combine robotics and virtual reality: the patient immerges in virtual world in which next to the visual also the physical environment can be shaped. This helps patients to increase range of motion during training. In passive systems [3], the patient moves robotic arms and typical gestures (pull, lift, grasp, twist, etc.) are measured by the system and stored. Some innovative approaches are based on the association of auditory feedbacks to movement to enhance the patient’s perception of mobility [4]. About sensing gloves, several devices were developed in the past, starting from the 1970s MIT-LED glove and Sayre glove [5] for computer graphics animation. The Digital Entry Data glove [6] was equipped with touch, proximity, tilt, and inertial sensors, and it was able to convert fingers motions into ASCII letters. The Power glove [7, 8], introduced in 1989 for videogames, can measure fingers flexion by means of deposited resistive patterns. The next generation of gloves includes accelerometers (Acceleglove [9]), light detection (Ligthglove [10]), piezoresistive sensors and software for virtual modeling (CyberGlove [11]), Hall-effect sensors (Humanglove [12]), etc. In summary [13], the main limitations of glove-based systems are invasiveness (reduced portability and haptic sense), limited sensors accuracy, and needing of tedious calibrations. In 2014, starting from these limitations, the author developed the HMI glove called “GoldFinger” [14, 15]. Here, the key-features were components integration, wearability, wireless connection and energy harvesting from fingers motion. Through an optical channel and dedicated software, user’s gestures are converted to input commands for the machine. Electric connections are obtained with conductive fabrics and electric wires woven into fabrics. The properties and reliability of smart textiles are validated through dedicated test benches as artificial human joints [16] and e-textiles endurance testing systems [17]. In this paper, the improved glove device is presented. It is equipped with multiple sensors, specifically selected to serve precise neurological rehabilitation exercises: bending sensors on fingers, force sensors on fingertips, inertial sensors (accelerometer and gyroscope), pulse-oximeter, GSR (galvanic skin resistance) sensor, and temperature sensor. The electronic architecture also includes the microcontroller, the wireless communication interface, and the power management unit coupled to rechargeable battery. The device includes high integration and miniaturization of components to preserve wearability and comfort of use. The ergonomic package has been studied to preserve electronics against mechanical shocks and environmental contaminations. Innovative materials and textile-integrated
562
G. De Pasquale et al./ Materials Today: Proceedings 7 (2019) 560–565
conductors are also applied. The electronic design and components selection required preliminary PCB evaluation board prototypes; further layout optimization and size-scaling process produce the final electronic configuration. 2. Concept of the system The most important typologies and sequences of neurologic rehabilitation trainings for motion recovery are studied. Then, they have been categorized in few groups of physical parameters of interest, which can be measured by limited number of sensors. The sensors typologies identified are listed in Table 1. Table 1. Sensors typologies integrated into the system and associated rehabilitation trainings. Training Pinching Grasping Wrist motion Target hitting Mobile spot following Physiological measurements
Quantity to measure Execution time Errors sequence Fingertips force Fingers bending angle Wrist rotation Wrist bending Reaction time Reaction time Execution time Heart pulse/rate Temperature Sweating
Sensor Force sensor Force sensor Bending sensor 3-axial acceler. and gyros. Magnetometer Force sensor 3-axial accelerometer Pulsimeter/Pulse oximeter Thermometer Galvanic skin resp. (GSR) sensor
Fig. 1. Sensors typologies and layout.
3. Analog sensors 3.1. Force sensors Four FSR (force sensing resistors) FlexiForce A301 are coupled to fingertips to measure contact forces. They are made with polymer layers or printable ink with inclusions of sub-micrometer particles combined in order to reduce temperature dependency, improve mechanical properties and increase surface durability. The main advantages of FSRs are the small size (thickness lower than 0.5 mm), low cost and good shock resistance. The precision is affected by 10% of variability among measurements in average, which is compatible with the current application (force range detected is 0-25 N). The sensor is modified with a couple of rigid discs to distribute the applied force on the sensing area and to prevent measurements errors due to the high deformability of the polymer (Fig. 2a, Tab. 2). 3.2. Bending sensors Bending sensors for wearable applications are generally passive resistive transducers made with resistive inks (containing carbon or silver particles) on flexible plastic substrate. When bending occurs, few adjacent resistive particles inside the ink come into contact, by increasing the resistance. The nominal resistance is generally in the range between 10 and 50 kΩ and increases by a factor of 10 at full deflection. Different lamination and coating options can be used to increase durability and stiffness. The bending sensor Flexpoint (Fig. 2b, Tab. 2) is selected for this system. It includes flexible polyimide film coated with carbon/polymer based ink. This resistive element is commonly used to make thick film resistors, resistor networks, slide potentiometers and transducers. The bonding between polyimide and ink is very strong and brittle. During fabrication process, micro cracks are introduced into the ink coating so that, when the film is bent, they open and close. The transducer functioning is limited to certain bending angles. 3.3. GSR sensor The galvanic skin response (GSR) sensor is used to monitor the emotional status of the patient through the skin conductance (SC) or electro-dermal activity (EDA). Skin conductance can be measured with skin electrodes applied
G. De Pasquale et al./ Materials Today: Proceedings 7 (2019) 560–565
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to fingers. The sensor is basically an ohmmeter and measures the electrical conductance between two points. In the literature, there are different solutions for this measurement. The GSR based on cotton fiber [18] is based on single natural fiber functionalized and doped and interfaces with a liquid electrolyte in contact with silver wire gate. The device demonstrated effectiveness in electrochemical sensing of NaCl concentration, which is the measuring parameter also in human sweat. The GSR reported in [19] instead measures the difference of skin conductance by using two electrodes placed on the fingers as terminals of a resistance. The device uses a resistance in series with the skin resistance to form a voltage divider. The output voltage is inversely proportional to the skin resistance. The sensor by Seeed Studio Electronics is used (Fig. 2c, Table 2) in this wearable device because of its electric parameters: 5 or 3.3 V supply voltage, adjustable sensitivity through potentiometer and pair of measuring finger cots included.
Fig. 2. FSR FlexiForce A301 with rigid 3D printed support (a), bending sensor Flexpoint (b), GSR Seeed Studio Electronics (c). Table 2. Properties of analog sensors. Force sensor Property Dimensions Force range Non-actuated resist. Drift Operating temp.
Value 25.4x14x0.20 mm 0-111 N > 10 MΩ < 5% -40 °C ÷ 60 °C
Bending sensor Property Value 0.13 mm Thickness 76.2 mm Length 7 mm Width Operating temp. -30 °C ÷ 90 °C Reliability > 106 cycles
GSR sensor Property Value 24 mm Thickness 20 mm Length 9.8 mm Width Weight 28 g Input voltage 3.3 – 5 V
4. Digital sensors The MEMS sensor module STMicroelectronics LSM6DS0 (Tab. 3) integrating 3D accelerometer and 3D gyroscope is used. The sensing elements are manufactured by using specialized micromachining processes, and IC interfaces are built with CMOS technology. The 3-axes magnetometer STMicroelectronics LIS3MDL (Tab. 3) includes I2C serial bus interface that supports standard and fast sampling modes (100 kHz and 400 kHz) and SPI serial standard interface. It is used to provide information about the orientation with respect to the components of the terrestrial magnetic field in a specific reference system. This kind of data, combined with the accelerometer and gyroscope outputs, is able to provide the spatial position of objects. The MAX30105 by Maxim Integrated (Tab. 3) particle-sensing module is used as pulse-oximeter. It includes internal LEDs, photodetectors, optical elements, and low-noise electronics with ambient light rejection. The sensor provides smoke detection and hence can also be used as pulse oximeter. Red, green, and IR modulated LED pulses allow particle-sensing measurements. The LED pulse width can be programmed from 69 to 411 μs to allow the algorithm to optimize particle-sensing accuracy. The MAX30205 by Maxim Integrated (Table 3) temperature sensor
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G. De Pasquale et al./ Materials Today: Proceedings 7 (2019) 560–565
is able to measure temperatures in the range 0°C - 50°C with 0.1°C accuracy from 37°C to 39°C. It also provides an over-temperature alarm/interrupt/shutdown. It works in 16-bits resolution. Table 3. Properties of digital sensors. Acceleration sensor / gyroscope Property Value Acceler. range ±2 / ±16 g Angular rate range ±245 / ±2000 dps Supply voltage 1.7 ÷ 3.6 V Acceler. consumption 330 µA Gyros. consumption 4000 µA
Magnetometer Property Value Measurement range ±4 / ±16 gauss Operating temp. -40 °C ÷ 85 °C Supply voltage 1.9 ÷ 3.6 V Max consumpt. 270 µA Low perform. consumpt. 40 µA
Pulse-oximeter sensor Property Value Supply voltage 1.7 ÷ 2.0 V 600 µA Supply current 660 nm Red LED wavel. 800 nm Infrared LED wavel. 537 nm Green LED wavel.
Temperature sensor Property Value Supply voltage 2.7 ÷ 3.3 V Error (0÷15 °C) ± 0.5 °C Error (37÷39 °C) ± 0.1 °C Error (45÷50 °C) ± 0.5 °C 600 µA Supply current
5. PCB layout The PCB prototype is designed according to the block scheme of Fig. 3. The nRF52832 microcontroller (Nordic Semiconductor) is used: 32-bits, 512 kB CPU + 64 kB RAM, 2.4 GHz Bluetooth transceiver. The eight analog signals of force and bending sensors are multiplexed with the 74HC4051 (NXP Semiconductors) device. The power management area is an embedded circuit that supplies the entire PCB, through (a) USB port, (b) DC power connector of 5.5 mm, (c) connector suitable for LiPo batteries, (d) couple of generic male headers for any other external sources. The PCB layout also includes storage area composed by 2 TB Micro SD and 1 MB EEPROM memories. The service area hosts few headers arrays usable as fast testing points. Some electronics schematics of different sensing areas are reported in Fig. 4.
Fig. 3. Printed circuit board: layout blocks scheme.
b) a)
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d)
c)
Fig. 4. Functional areas electronic schematics: force (a), temperature (b), pulsimeter (c) and inertial (d) sensing areas.
6. Conclusions The PCB prototype includes high potential of sensing and management of human body parameters, including inertial, thermal and physiological data. The design is specifically addressed to the monitoring of rehabilitation trainings of patients within predetermined exercise typologies. However, the capabilities included in the device could possibly open to other applicative fields with minimal variations or improvements. The redundancies provided in power supply ports, memories typologies and input channels could serve additional functionalities. The next step of the development is involving the circuit miniaturization and integration on the textile support. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19]
Hand of Hope: https://aumet.me/hand-of-hope/p/745 Tyromotion: https://tbirehabilitation.wordpress.com ReJoyce: http://www.fitness-gaming.com Xsens: https://www.xsens.com D.J. Sturman, D. Zeltzer, IEEE Comput. Graph. Appl. 14 (1994) 30-39. G. Grimes, U.S. Patent (1983) 4414 537. D.L. Gardner, Des. News vol. 45 (1989) 63-68. H. Eglowstein, Byte 15 (1990) 283-290. J.L. Hernandez-Rebollar, N. Kyriakopoulos, R.W. Lindeman, SIGGRAPH 259 (2002). B. Howard, S. Howard, IEEE Int. Symp.Wearable Comput. (2001) 172-173. C. Burdea Grigore, P. Coiffet, Virtual Reality Technology. New York, NY, USA: Wiley (2003). L. Dipietro, A.M. Sabatini, P. Dario, J. Rehabil. Res. Dev. 40 (2003) 179-190. L. Dipietro, A.M. Sabatini, P. Dario, IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., 38 (2008) 461-482. G. De Pasquale, S.G. Kim, D. De Pasquale, PowerMEMS (2015). G. De Pasquale, S.G. Kim, D. De Pasquale, IEEE/ASME Trans. Mech. 21 (2016) 565-575. G. De Pasquale, Meccanica, 51 (2016) 2259-2275. G. De Pasquale, A. Mura, J. of Industr. Textiles, 47 (2018) 1925-1943. G. Tarabella, M. Villani, D. Calestani, R. Mosca, S. Iannotta, A. Zappettini, N. Coppedè, J. Mater. Chem. 22 (2012) 23830. M. Viqueira Villarejo, B. García Zapirain, A. Méndez Zorrilla, Sensors 12 (2012) 6075-6101.
ScienceDirect Materials Today: Proceedings 7 (2019) 560–565
www.materialstoday.com/proceedings
BioM&M_2018
Wearable sensing systems for biomechanical parameters monitoring Giorgio De Pasquale*, Leonardo Mastrototaro, Valentina Ruggeri Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129, Italy
Abstract The use of wearable sensing systems in clinical rehabilitation has demonstrated high potential in improving the trainings effectiveness and reducing mobility recovery time. The monitoring of biomechanical parameters in upper limbs provides valuable supports to neurological patient’s recovery trainings, where the self-awareness of human body must be restored. After preliminary activities of the author’s group, which led to the GoldFinger prototype for HMI applications, the development of the sensing glove described in this paper has been carried out. Here, small dimensions and lightness are accompanied by flexibility of supports, reliable connectors and interfaces and low power consumption. © 2018 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/). Selection and Peer-review under responsibility of 1st International Conference on Materials, Mimicking, Manufacturing from and for Bio Application (BioM&M). Keywords: Biomechanics; HMI; smart fabrics; e-textiles; neurologic rehabilitation.
1. Introduction The number of people affected by neurological pathologies and/or traumas is progressively increasing because of life expectancy growth. The stroke, for instance, is caused by loss of blood supply (ischemia) or bleeding (hemorrhage) in the brain. Respect other cardiovascular diseases, stroke incidence is dramatically increasing in elderly people. Other frequent neurologic pathologies are neurodegenerative dementias which cause invalidity of motion abilities (e.g. control of part or entire body, called “plegia”), disorders of language, memory and awareness.
* Corresponding author. Tel.: +39-011-0906912; fax: +39-011-0906999. E-mail address: [email protected] 2214-7853 © 2018 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Selection and Peer-review under responsibility of 1st International Conference on Materials, Mimicking, Manufacturing from and for Bio Application (BioM&M).
G. De Pasquale et al./ Materials Today: Proceedings 7 (2019) 560–565
561
Similar symptoms are present in people subjected to traumas associated to neurological injuries like road accidents. In this case, the consequent neurological deficiency affects younger ranges of population.. The incapacity to do even simple everyday actions and to interact with things and people has tremendous impact on the psychological health of patients. In addition to motion inabilities, the psychological uneasiness produces propensity to changes of mood and cognitive disorders. These disturbances are heavily penalizing the patient recovery and therapies effectiveness and duration. Highly targeted therapies still under investigation are addressed to these patients. The most advanced theories, supported by pilot campaigns on patients, are based on multisensory rehabilitation trainings with wearable devices and/or virtual reality. The patient can experience the motion of the body through direct measurements in the parts where the movement is inhibited; the recovery process has benefits in terms of effectiveness and duration. Different brain areas control the body movements and feelings. When a neurological pathology or trauma damages a certain part of the brain, that part may not work as well as it did before. This can cause problems with controlling movements, speaking, seeing or feeling. After a stroke, for example, patients often experience dramatically constrained mobility and partial paralysis of the limbs. Especially movements of the upper extremities, like grasping movements, are frequently hampered (reduced accuracy/speed, high variability). Common rehabilitative approaches are based on massive practice of a given motor task supervised by a therapist. Traditional rehabilitation consists of multiple repetitions of twisting and lifting small objects and different kinds of arm and wrist rotations, where a range of normal day-to-day activities (turning a key, picking up a can, moving a doorknob, etc.) is simulated. However, in addition to traditional rehabilitations, the most advanced theories of neuropsychology are addressed to “multisensory rehabilitation trainings”, thanks to the support of wearable devices. They demonstrated the correlation of movements with “body ownership”, with the perspective to improve the rehabilitation of hemiplegic patients through the monitoring of motion recovery. Active systems for supporting trainings of neurological patients provide artificial actuation to muscles to regain mobility. In some cases [1], the EMG stimulus is detected, filtered and used to actuate a motor enabling the desired motion. Other devices [2] combine robotics and virtual reality: the patient immerges in virtual world in which next to the visual also the physical environment can be shaped. This helps patients to increase range of motion during training. In passive systems [3], the patient moves robotic arms and typical gestures (pull, lift, grasp, twist, etc.) are measured by the system and stored. Some innovative approaches are based on the association of auditory feedbacks to movement to enhance the patient’s perception of mobility [4]. About sensing gloves, several devices were developed in the past, starting from the 1970s MIT-LED glove and Sayre glove [5] for computer graphics animation. The Digital Entry Data glove [6] was equipped with touch, proximity, tilt, and inertial sensors, and it was able to convert fingers motions into ASCII letters. The Power glove [7, 8], introduced in 1989 for videogames, can measure fingers flexion by means of deposited resistive patterns. The next generation of gloves includes accelerometers (Acceleglove [9]), light detection (Ligthglove [10]), piezoresistive sensors and software for virtual modeling (CyberGlove [11]), Hall-effect sensors (Humanglove [12]), etc. In summary [13], the main limitations of glove-based systems are invasiveness (reduced portability and haptic sense), limited sensors accuracy, and needing of tedious calibrations. In 2014, starting from these limitations, the author developed the HMI glove called “GoldFinger” [14, 15]. Here, the key-features were components integration, wearability, wireless connection and energy harvesting from fingers motion. Through an optical channel and dedicated software, user’s gestures are converted to input commands for the machine. Electric connections are obtained with conductive fabrics and electric wires woven into fabrics. The properties and reliability of smart textiles are validated through dedicated test benches as artificial human joints [16] and e-textiles endurance testing systems [17]. In this paper, the improved glove device is presented. It is equipped with multiple sensors, specifically selected to serve precise neurological rehabilitation exercises: bending sensors on fingers, force sensors on fingertips, inertial sensors (accelerometer and gyroscope), pulse-oximeter, GSR (galvanic skin resistance) sensor, and temperature sensor. The electronic architecture also includes the microcontroller, the wireless communication interface, and the power management unit coupled to rechargeable battery. The device includes high integration and miniaturization of components to preserve wearability and comfort of use. The ergonomic package has been studied to preserve electronics against mechanical shocks and environmental contaminations. Innovative materials and textile-integrated
562
G. De Pasquale et al./ Materials Today: Proceedings 7 (2019) 560–565
conductors are also applied. The electronic design and components selection required preliminary PCB evaluation board prototypes; further layout optimization and size-scaling process produce the final electronic configuration. 2. Concept of the system The most important typologies and sequences of neurologic rehabilitation trainings for motion recovery are studied. Then, they have been categorized in few groups of physical parameters of interest, which can be measured by limited number of sensors. The sensors typologies identified are listed in Table 1. Table 1. Sensors typologies integrated into the system and associated rehabilitation trainings. Training Pinching Grasping Wrist motion Target hitting Mobile spot following Physiological measurements
Quantity to measure Execution time Errors sequence Fingertips force Fingers bending angle Wrist rotation Wrist bending Reaction time Reaction time Execution time Heart pulse/rate Temperature Sweating
Sensor Force sensor Force sensor Bending sensor 3-axial acceler. and gyros. Magnetometer Force sensor 3-axial accelerometer Pulsimeter/Pulse oximeter Thermometer Galvanic skin resp. (GSR) sensor
Fig. 1. Sensors typologies and layout.
3. Analog sensors 3.1. Force sensors Four FSR (force sensing resistors) FlexiForce A301 are coupled to fingertips to measure contact forces. They are made with polymer layers or printable ink with inclusions of sub-micrometer particles combined in order to reduce temperature dependency, improve mechanical properties and increase surface durability. The main advantages of FSRs are the small size (thickness lower than 0.5 mm), low cost and good shock resistance. The precision is affected by 10% of variability among measurements in average, which is compatible with the current application (force range detected is 0-25 N). The sensor is modified with a couple of rigid discs to distribute the applied force on the sensing area and to prevent measurements errors due to the high deformability of the polymer (Fig. 2a, Tab. 2). 3.2. Bending sensors Bending sensors for wearable applications are generally passive resistive transducers made with resistive inks (containing carbon or silver particles) on flexible plastic substrate. When bending occurs, few adjacent resistive particles inside the ink come into contact, by increasing the resistance. The nominal resistance is generally in the range between 10 and 50 kΩ and increases by a factor of 10 at full deflection. Different lamination and coating options can be used to increase durability and stiffness. The bending sensor Flexpoint (Fig. 2b, Tab. 2) is selected for this system. It includes flexible polyimide film coated with carbon/polymer based ink. This resistive element is commonly used to make thick film resistors, resistor networks, slide potentiometers and transducers. The bonding between polyimide and ink is very strong and brittle. During fabrication process, micro cracks are introduced into the ink coating so that, when the film is bent, they open and close. The transducer functioning is limited to certain bending angles. 3.3. GSR sensor The galvanic skin response (GSR) sensor is used to monitor the emotional status of the patient through the skin conductance (SC) or electro-dermal activity (EDA). Skin conductance can be measured with skin electrodes applied
G. De Pasquale et al./ Materials Today: Proceedings 7 (2019) 560–565
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to fingers. The sensor is basically an ohmmeter and measures the electrical conductance between two points. In the literature, there are different solutions for this measurement. The GSR based on cotton fiber [18] is based on single natural fiber functionalized and doped and interfaces with a liquid electrolyte in contact with silver wire gate. The device demonstrated effectiveness in electrochemical sensing of NaCl concentration, which is the measuring parameter also in human sweat. The GSR reported in [19] instead measures the difference of skin conductance by using two electrodes placed on the fingers as terminals of a resistance. The device uses a resistance in series with the skin resistance to form a voltage divider. The output voltage is inversely proportional to the skin resistance. The sensor by Seeed Studio Electronics is used (Fig. 2c, Table 2) in this wearable device because of its electric parameters: 5 or 3.3 V supply voltage, adjustable sensitivity through potentiometer and pair of measuring finger cots included.
Fig. 2. FSR FlexiForce A301 with rigid 3D printed support (a), bending sensor Flexpoint (b), GSR Seeed Studio Electronics (c). Table 2. Properties of analog sensors. Force sensor Property Dimensions Force range Non-actuated resist. Drift Operating temp.
Value 25.4x14x0.20 mm 0-111 N > 10 MΩ < 5% -40 °C ÷ 60 °C
Bending sensor Property Value 0.13 mm Thickness 76.2 mm Length 7 mm Width Operating temp. -30 °C ÷ 90 °C Reliability > 106 cycles
GSR sensor Property Value 24 mm Thickness 20 mm Length 9.8 mm Width Weight 28 g Input voltage 3.3 – 5 V
4. Digital sensors The MEMS sensor module STMicroelectronics LSM6DS0 (Tab. 3) integrating 3D accelerometer and 3D gyroscope is used. The sensing elements are manufactured by using specialized micromachining processes, and IC interfaces are built with CMOS technology. The 3-axes magnetometer STMicroelectronics LIS3MDL (Tab. 3) includes I2C serial bus interface that supports standard and fast sampling modes (100 kHz and 400 kHz) and SPI serial standard interface. It is used to provide information about the orientation with respect to the components of the terrestrial magnetic field in a specific reference system. This kind of data, combined with the accelerometer and gyroscope outputs, is able to provide the spatial position of objects. The MAX30105 by Maxim Integrated (Tab. 3) particle-sensing module is used as pulse-oximeter. It includes internal LEDs, photodetectors, optical elements, and low-noise electronics with ambient light rejection. The sensor provides smoke detection and hence can also be used as pulse oximeter. Red, green, and IR modulated LED pulses allow particle-sensing measurements. The LED pulse width can be programmed from 69 to 411 μs to allow the algorithm to optimize particle-sensing accuracy. The MAX30205 by Maxim Integrated (Table 3) temperature sensor
564
G. De Pasquale et al./ Materials Today: Proceedings 7 (2019) 560–565
is able to measure temperatures in the range 0°C - 50°C with 0.1°C accuracy from 37°C to 39°C. It also provides an over-temperature alarm/interrupt/shutdown. It works in 16-bits resolution. Table 3. Properties of digital sensors. Acceleration sensor / gyroscope Property Value Acceler. range ±2 / ±16 g Angular rate range ±245 / ±2000 dps Supply voltage 1.7 ÷ 3.6 V Acceler. consumption 330 µA Gyros. consumption 4000 µA
Magnetometer Property Value Measurement range ±4 / ±16 gauss Operating temp. -40 °C ÷ 85 °C Supply voltage 1.9 ÷ 3.6 V Max consumpt. 270 µA Low perform. consumpt. 40 µA
Pulse-oximeter sensor Property Value Supply voltage 1.7 ÷ 2.0 V 600 µA Supply current 660 nm Red LED wavel. 800 nm Infrared LED wavel. 537 nm Green LED wavel.
Temperature sensor Property Value Supply voltage 2.7 ÷ 3.3 V Error (0÷15 °C) ± 0.5 °C Error (37÷39 °C) ± 0.1 °C Error (45÷50 °C) ± 0.5 °C 600 µA Supply current
5. PCB layout The PCB prototype is designed according to the block scheme of Fig. 3. The nRF52832 microcontroller (Nordic Semiconductor) is used: 32-bits, 512 kB CPU + 64 kB RAM, 2.4 GHz Bluetooth transceiver. The eight analog signals of force and bending sensors are multiplexed with the 74HC4051 (NXP Semiconductors) device. The power management area is an embedded circuit that supplies the entire PCB, through (a) USB port, (b) DC power connector of 5.5 mm, (c) connector suitable for LiPo batteries, (d) couple of generic male headers for any other external sources. The PCB layout also includes storage area composed by 2 TB Micro SD and 1 MB EEPROM memories. The service area hosts few headers arrays usable as fast testing points. Some electronics schematics of different sensing areas are reported in Fig. 4.
Fig. 3. Printed circuit board: layout blocks scheme.
b) a)
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d)
c)
Fig. 4. Functional areas electronic schematics: force (a), temperature (b), pulsimeter (c) and inertial (d) sensing areas.
6. Conclusions The PCB prototype includes high potential of sensing and management of human body parameters, including inertial, thermal and physiological data. The design is specifically addressed to the monitoring of rehabilitation trainings of patients within predetermined exercise typologies. However, the capabilities included in the device could possibly open to other applicative fields with minimal variations or improvements. The redundancies provided in power supply ports, memories typologies and input channels could serve additional functionalities. The next step of the development is involving the circuit miniaturization and integration on the textile support. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19]
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