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A low-cost energy-harvesting sensory headwear useful for tetraplegic people to drive home automation
Int. J. Electron. Commun. (AEÜ) 107 (2019) 9–14
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A low-cost energy-harvesting sensory headwear useful for tetraplegic people to drive home automation Giuseppe Piscitelli a, Vito Errico a,⇑, Mariachiara Ricci a, Franco Giannini a, Giovanni Saggio a, Alfiero Leoni b, Vincenzo Stornelli b, Giuseppe Ferri b, Leonardo Pantoli b, Iolanda Ulisse b a b
University of Rome ‘‘Tor Vergata”, Dept. of Electronic Engineering, Rome, Italy University of L’Aquila, Dept. of Industrial and Information Engineering and Economics, L’Aquila, Italy
a r t i c l e
i n f o
Article history: Received 4 March 2019 Accepted 10 May 2019
Keywords: Energy harvesting Sensory headwear IMU Home automation
a b s t r a c t Tetraplegic people need continuous assistance in every daily activity. Assistive technologies can improve, to a certain degree, their quality of life allowing partial autonomy with powering their residual capability of movements. In this work, we propose a novel wire-free low-cost user-friendly battery-operated sensory headwear, which allows home automation controlled by head movements. The headwear is equipped with an inertial measurement unit (IMU), a low power microcontroller and a transmission module to measure, condition and wireless transmit data related to head movements. Such a sensory headwear allows the subject, simply by head movements, either to select one computer icon among an ensemble or to select one actuator, among a number of others. Each icon and each actuator drive a specific physical action in a home or work environment. We devoted particular efforts to increase the battery autonomy, by means of radio frequency energy harvesting solutions, for lasting operational mode. The harvester, based on commercial chipsets, was optimized in the 2.4–2.5 GHz range to exploit headwear itself radiated energy and environmental energy, in particular from Wi-Fi and Bluetooth surrounding devices. An average efficiency, calculated as output to input power ratio, of around 60% at 5dBm input power level has been obtained. Ó 2019 Elsevier GmbH. All rights reserved.
1. Introduction Tetraplegia is a condition of partial or total paralysis of the four limbs and torso. It can result either from traumatic events involving spinal cord injury (SCI), with looseness of four limbs motor capabilities in the 32% of cases [1,2] or from progressive pathologies such as muscular dystrophies, amyotrophic lateral sclerosis and multiple sclerosis [3,4]. Tetraplegia can result from congenital diseases too, such as infantile cerebral palsy and some specific forms of muscular dystrophy, involving cognitive disorders and epileptic crisis predisposition too, even in young age [3–7]. Because of hard motor impairments, tetraplegic people need continuous assistance even for every-day actions, as it is for opening a window or turning on the lights. The assistive technology can improve their quality of life, helping them to act autonomously in home or work environment. We can mention the hands-free human machine
⇑ Corresponding author at: DIE-Dipartimento di Ingegneria Elettronica, Università degli Studi di ‘‘Roma – Tor Vergata”, Via del Politecnico, 1, 00133 Roma, Italy. E-mail addresses: [email protected] (V. Errico), [email protected] (G. Saggio), [email protected] (A. Leoni). https://doi.org/10.1016/j.aeue.2019.05.015 1434-8411/Ó 2019 Elsevier GmbH. All rights reserved.
interfaces (HMI), which include eye-movement tracking (Tobii Technology, in Sweden or The Eye Tribe in Denmark) [8], voice recognition (Alexa by Google, USA, Cortana by Microsoft, USA) [9,10], mouth-stick systems [11,12] and brain computer interfaces (BCI) [13–17]. Those HMI present meaningful advantages but some drawbacks too, such as wearisome and cumbersome eyemovement tracking, user-unfriendly mouth-stick systems, and high-demanding setup and computational time BCI. In recent years, the hands-free HMI technologies enriched the portfolio with inertial sensor-based devices. Integrated inertial sensors can include 3-axes accelerometer, gyroscopes and magnetoresistances, so to implement compact, light, low-cost inertial measurement units (IMUs). Mandel and Röfen realized an IMU-based headwear to drive a wheelchair by means of head movements [18–21]. Bureau at al. realized an inertial HMI to drive a wheelchair and to command movements of a PC pointer [22]. Joseph and Nguyen used a wireless IMU module to fed data to a processing unit to define neural network-based commands [23–25], but with a computational delay ranging within 948–1624 ms. Recently, Samket and Shikhar realized a wireless inertial HMI based system, integrated with a rechargeable battery, a microcontroller, an
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accelerometer, an ADC converter and a wireless ZigBee module. This system, together with an eye-blink sensor integrated on glasses, allowed interaction with a wheelchair model, a personal computer, and a scale-model of home automation system [26]. Wearable electronics devices have been gaining more and more importance for assessing body motion capabilities of able people [27–30] or for improving residual motion capabilities of disabled people. This is the case of the sensory headwear, reported in our previous work [31], useful to help tetraplegic people. The realized inertial HMI for home automation facilities [31] is controlled by means of the sensory headwear, which integrated IMU, microcontroller and radio frequency transmission module, all powered by rechargeable battery. The head movements were converted into home automation commands in domestic or work environments by feeding data wirelessly to a USB dongle, connected to a personal computer, on which an ad-hoc realized graphical interface unit (GUI) runs to drive a home automation system. A drawback of the previously proposed system was the battery lifetime and dimensions. Consequently, here we propose an extended battery lifetime architecture taking advantage from harvesting technique, gathering environmental radio frequency (RF) energy, based on commercial components. Energy recovery from environmental power sources is an innovative way to capture and store energy. Energy Harvesting (EH) systems are particularly suitable for low voltage and/or low power circuits, in wireless sensor network (WSN) systems [32–41], and wearable electronics. By means of EH systems, the ambient energy can be converted into other useful forms [42–54]. In particular, EH systems can gather RF energy and convert it to DC energy with good efficiency. Furthermore, they gain in performance improvement with wide dynamic and spectrum range handling. In particular, the harvester is designed to gather radiated power in the 2.4–2.5 GHz frequency band of the headwear circuitry itself and from the surrounding radiated environmental energies, as those from Wi-Fi and Bluetooth devices. Within such a frame, our work focused on improving a sensory headwear system developed for increasing the autonomy of tetraplegic people. The improvements regard compactness, lightness, costs and power consumption, with optimized hardware solution, especially concerning EH techniques. 2. The designed systems Fig. 1 shows the architectural block scheme of the overall system. The headwear allows the user to drive, simply by head movements, home actuators that can be selected, directly or indirectly, by choosing self-explaining icons on a screen.
Fig. 1. Headwear system: data gathered from the head movements are wireless sent to a receiver and converted into home automation commands.
The headwear hosts motion tracking sensors, a power supply battery, a RF wireless transmission module and an energy harvesting circuitry. Fig. 2 depicts the architectural block scheme of the system. The IMU gather motion data, further processed by a microcontroller (MCU), and wirelessly transmitted by a system-on-chip (SoC). The Li-Po battery, the charge management system (CMS) and the buck regulator provide a stable source voltage. The USB/ UART IC allows programming microcontroller directly on board through the USB connector. The data elaboration core is the pico-power 8-bit microcontroller (MCU) ATmega328PB (by Microchip), an updated version of the previously adopted ATmega328P [31], which integrates additional serial peripheral interfaces and time counter modules. The wireless trans-receiver system-on-chip (SoC) and the IMU are the nRF8001 BLE (by Nordic Semiconductor) and the BMI055 IMU unit (by Bosch), respectively. MCU, IMU and SoC have a serial peripheral interface (SPI) to convey data flow. The nRF8001 transceiver is impedance matched to a 2.4 GHz ceramic SMD antenna (W3008C by Pulse Electronics) by means of a balanced-unbalanced (balun) transformer (2450BM14A0002 by Jhoanson Technology). One-cell 190mAh Li-Po battery (HPL402323-2C) provides the source, which is step-down DC-DC converted by the TPS622317 buck switching regulator (by Texas Instruments). A charge management system (CMS), based on pass-through technology, is realized by means of a BQ24232 device (by Texas Instruments). An USB-to-UART converter, the FT230XS (by FTDI), realizes the communication with an external USB 2.0 full speed port through a micro-b USB connector, allowing programming the microcontroller’s firmware. Selected ICs and adopted mastering PCB process rules were designed to reduce costs, size and power consumption of the system, as further detailed. SMD capacitors and resistors have standard 0805 package of 2 1.2 mm2; the battery dimensions are 23.5 24.5 4.2 mm3. Table 1 reports size and average costs of ICs. Particular attention was paid to the wireless communication protocol. Differently from the wireless local area network (WLAN) previously adopted [31], here we utilized a wireless personal area network (WPAN), optimized for low power consumption and harvesting applications. In particular, we adopted the Bluetooth low energy (BLE) protocol that ensures higher transmission efficiency with lower payload size [55–61], with respect to Wi-Fi, Bluetooth or ZigBee protocols. The working average current is as low as 936 mA, estimated considering a 20 ms (connIntervall) periodic transmission of 25 Byte payload (Npl), an idle mode of BLE module for no-data availability, and timing and current absorption of each nRF8001 sub-routine as reported in Table 2. Eq. (1) represents the average absorbed current (Iav), considering 10 Byte of data frames forming data (Novhd):
Fig. 2. Architectural block scheme of the system headwear. Continuous and dotted lines represent power and data paths, respectively.
G. Piscitelli et al. / Int. J. Electron. Commun. (AEÜ) 107 (2019) 9–14 Table 1 Average price and dimensions of adopted ICs (January 2019). IC
Package size (mm mm)
prize of minimum 5000 pcs (€)
ATmega328PB BMI055 BQ24232 FT230XS nRF8001 TPS622317
7 7 (TQFP) 3 4.5 (QFN) 3 3 (VQFN) 4 5 (SSOP) 5 5 (QFN) 1 1.45 (SON)
0.922 2.079 1.109 1.300 1.630 0.452
Table 2 Timing and current absorption of each sequentially sub-routine executed during one data frame transmission by nRF8001. Sub-routine
Current absorbed (mA)
Timing
Wake-up and preprocessing Reception IFS (Inter Frame Space) Transmission (0 dBm) Post-processing
I1 = 3.5 IRx = 14.6 I2 = 7 ITx = 12.7 I3 = 5
T1 = 1 ms s = 8 ms/byte T2 = 150 ms s = 8 ms/byte T3 = 2 ms
X 1 ðT i Ii Þ þ sIRx Nov hd þ sIRx ðNov hd þ Npl Þ i connInterv all i ¼ 1; 2; 3
I av ¼
ð1Þ
The adopted IMU contributes to maintain a low power absorption too, the integrated gyroscopes and accelerometers being set to idle mode when data transmission is not necessary. In particular, the gyroscopes current requirement varies within 1.667 mA and 5 mA, while the accelerometers require a current of 19.2 mA at 125 sample/sec and 55.25 Hz output data rate. The peak current absorptions for gyroscope and accelerometer modules are 5.1 mA and 130 mA, respectively. Moreover, we set the TPS622317 to work in power save configuration, so that it runs in pulse frequency modulation (PFM) until a high input voltage or low load current is imposed or drained, respectively, and in pulse width modulation (PWM) mode otherwise. The bias absorbed current depends on operational mode and varies from 25mA in PFM to 3 mA in PWM, respectively. Concerning the battery powered circuitry and harvesting system, the block architecture is shown in Fig. 3. The storage capacitor, C, gathers the energy converted from the incoming RF signal to DC by the rectifier block and charges up to a selected threshold voltage. When VC reaches its maximum value, a digital line VINT is set high by the detector block and the boost converter turns on, so VDC rises up to the DC-DC converter pre-settled output voltage. The storage capacitor then discharges until VC reaches its minimum value, and detector then sets VINT low, shutting off the boost converter until VC charges back up to its maximum value. This strategy allows first to decouple the harvester from the load and,
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successively, to decouple the load from the harvester, giving to the voltage regulator enough energy to be manipulated. Generally, the conversion efficiency is determined for a defined input power signal level, at a given frequency, once the load impedance is specified. In order to have good values of this characteristic, the input RF power matching is one of the more delicate aspects in the design of the antenna-rectifier circuitry. Concerning the RF to DC block, the Powercast PCC110 was used while the PCC210, always from Powercast, was selected for the DC-DC converter block. Finally, the battery charger controller is necessary when the harvester charges the battery. The switch limits the current draw on the battery from the harvester itself to a few nA. This ensures that the designed circuitry does not discharge the battery during the periods of non-harvesting.
3. Experimental results The electronics of the sensory headwear resides on a dual-layer board hosting surface mounting devices (SMDs). Fig. 4a and b shows front and back view, respectively, of the electronic circuitry, 51.92 30.23 mm2 in dimensions. Table 3 reports peak and average currents of the circuitry in fully operating mode. The peak current value is compatible with the maximum output current deliverable from TPS622317 (500 mA). Moreover, the buck regulator absorbs an instant maximum current of 343.3 mA in PWM mode when coupled with a 1 ± 20% mH inductor, with a load current of 5.149 mA. The irradiated power at BLE bandwidth central frequency (2.48 GHz) can be set at a maximum of 2.7dBm, related to the output transmission power of the nRF8001 IC (max 4dBm) and depending on the antenna efficiency. Taking into account the average operative current of the circuitry in fulloperational mode and the nominal 190mAh value of the Li-Po battery, the worst-case time duration is about 37 h without recharging and no harvester applied. The complete hybrid prototype of the harvester circuit was made and assembled in our laboratory on a FR4 substrate. Fig. 5 shows the rendering of the harvester PCB. Being the large and small signal parameter of the rectifier components not available, a circuit optimization technique using a dedicated test bench was performed for sizing the passive components for circuit optimization (in terms of conversion efficiency) in the desired 2.4– 2.5 GHz frequency band. A commercial compact GW26.0112 antenna from Touglas was used in the test bench. Fig. 6 shows the measured results of RF-to-DC energy conversion efficiency, defined as circuit output power to input power ratio. The structure reaches its maximum conversion efficiencies of 65% for an input power of about 5dBm while performs about 50% in the 5 to 0 dBm range, that is the expected radiated output power from the headwear transmitter. In general, the power efficiency is quite con-
Fig. 3. Block scheme of the battery powered circuitry and harvesting system.
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Fig. 4. (a) Front and (b) back views of the electronic circuitry devoted to measure, acquire, condition and transmit data related to the movements of the head of the subject.
Table 3 Current consumption for principal ICs of device, estimated at 2.9 V in reported operating conditions, starting from datasheet DC characterization.
ATmega328PB BMI055 nRF8001
Operating condition
Average current (mA)
Peak current (mA)
Active Mode, 10 MHz clock speed BWacc = 62.5 Hz, BWgyro = 64 Hz, 10 ms idle interval connInterval = 20 ms, payload = 25 byte, 0 dBm power transmission
2.50 1.67 0.96
2.50 5.10 14.60
5.15
22.20
total
Fig. 5. Final harvester PCB render.
Finally, the optimized harvester total average efficiency is about 60% at 5dBm input power level. In several working condition, with nearfield Wi-Fi sources as home router, this solution was estimated to be useful to increase the duration of the system of about 10%. 4. Conclusions
Fig. 6. Measured harvester efficiency in the 20 +20 dBm input power range at different frequency.
stant within the wide input power range under test from 5 to 15dBm; in other words, the architecture offers general constant performances over a wide range of input power.
We realized a novel hands-free sensory headwear-based HMI device useful for tetraplegic people to drive autonomously home automation devices. Key elements were compactness, low-cost, and battery lasting strategies. The system is compatible with different commercial apparatuses (such as mobile phones, personal computers and tablets), because of the adopted BLE as a standard wireless communication protocol. With respect to other handsfree HMI devices, the proposed system gains in user-friendly capabilities, with a total cost as-low-as of about 75€ (even lower for a large-scale production). Declaration of Competing Interest None.
G. Piscitelli et al. / Int. J. Electron. Commun. (AEÜ) 107 (2019) 9–14
References [1] Wyndaelee M, Wyndaele J-J. Incidence, prevalence and epidemiology of spinal cord injury: what learns a worldwide literature survey? Spinal Cord 2006;44:523–9. https://doi.org/10.1038/sj.sc.3101893. [2] Cosar SNS et al. Demographic characteristics after traumatic and nontraumatic spinal cord injury: a retrospective comparison study. Spinal Cord 2010;48:862–6. https://doi.org/10.1038/sc.2010.49. [3] Pugliatti M et al. The epidemiology of multiple sclerosis in Europe. Eur J Neurol 2006;13:700–22. https://doi.org/10.1111/j.1468-1331.2006.01342.x. [4] Murati R et al. Il Bergamini di neurologia. A cura di Edizioni Libreria Cortina Torino. 2012. pp. 467–481, 641–645, 713–715. ISBN: 9788882391683. [5] Bartolozzi G. Pediatria, Principi e pratica clinica. Edra Masson; 2013. p. 1464–5. ISBN: 8821432874. [6] Mah Jean K et al. A systematic review and meta-analysis on the epidemiology of Duchenne and Becker muscular dystrophy. Neuromuscolar Disorder 2014;24:482–91. https://doi.org/10.1016/j.nmd.2014.03.008. [7] Careddu P et al. Swartz Tiene – Manuale di pediatria. A cura di Casa Editrice Ambrosiana 1993. pp. 1182-1183, 1224-1227. EAN13: 9788808084668. [8] Chandra S et al. Eye tracking based human computer interaction: applications and their uses. In: International Conference on Man and Machine Interfacing. IEEE. p. 1–5. https://doi.org/10.1109/MAMI.2015.7456615. [9] Arriany A, Mohamed Musbah S. Applying voice recognition technology for smart home networks. In: IEEE International Conference on Engineering & MIS (ICEMIS). p. 1–6. https://doi.org/10.1109/ICEMIS.2016.7745292. [10] Zhu J et al. Developing a voice control system for Zigbee-based home automation networks. In: 2nd IEEE International Conference on Network Infrastructure and Digital Content, IEEE. p. 737–41. https://doi.org/10.1109/ ICNIDC.2010.5657880. [11] Arshak K, Buckley D, Kaneswaran K. Review of assistive devices for electric powered wheelchairs navigation. ITB J 2006;Vol. 7(Iss. 1):13–23. https://doi. org/10.21427/D7TB37. [12] Lu L, Wen JT. Human-robot cooperative control for mobility impaired individuals. Am Control Conf. ACC 2015:447–52. https://doi.org/10.1109/ ACC.2015.7170776. [13] Rebsamen B et al. A brain-controlled wheelchair based on P300 and path guidance. In: IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics. p. 1101–6. https://doi.org/10.1109/BIOROB.2006.1639239. [14] Rebsamen B et al. Controlling a wheelchair using a BCI with low information transfer rate. In: IEEE 10th International Conference on Rehabilitation Robotics. p. 1003–8. https://doi.org/10.1109/ICORR.2007.4428546. [15] Cavrini F, Bianchi L, Quitadamo LR, Saggio G. A Fuzzy Integral Ensemble Method in Visual P300 Brain-Computer Interface. Comp Intell Neurosci 2016;2016:1–9. https://doi.org/10.1155/2016/9845980. [16] Bianchi L, Quitadamo LR, Abbafati M, Marciani MG, Saggio G. Introducing NPXLab 2010: a tool for the analysis and optimization of P300 based BrainComputer Interfaces. In: 2nd International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL2009), Bratislava, Slovak Republic, November 24-27. https://doi.org/10.1109/ISABEL.2009.5373621. [17] Zhang R et al. A BCI-Based Environmental Control System for Patients With Severe Spinal Cord Injuries. IEEE Trans Biomed Eng 2017;64(8):1959–71. https://doi.org/10.1109/TBME.2016.2628861. [18] Mandel C, R. T, Frese U. Applying a 3DOF orientation tracker as a human-robot interface for autonomous wheelchairs. In: IEEE International Conference on Rehabilitation Robotics. IEEE. p. 52–9. https://doi.org/10.1109/ ICORR.2007.4428406. [19] Mandel C, Frese U. Comparison of wheelchair user interfaces for the paralysed: head-joystick vs. verbal path selection from an offered route-set. In: Proceedings of the 3rd European Conference on Mobile Robots, EMCR 2007, September 19-21. p. 1–6. [20] Mandel Christian, Frese Udo, Röfer Thomas. In: Lecture Notes in Computer ScienceComputational and Ambient Intelligence. Berlin, Heidelberg: Springer Berlin Heidelberg; 2007. p. 1052–9. https://doi.org/10.1007/978-3-54073007-1_127. [21] Rofer T, Mandel C, Laue T. Controlling an automated wheelchair via joystick/headjoystick supported by smart driving assistance. In: IEEE 11th International Conference on Rehabilitation Robotics, Kyoto International Conference Center. IEEE. p. 743–8. https://doi.org/10.1109/ICORR.2009.5209506. [22] Bureau M et al. Non-invasive, wireless and universal interface for the control of peripheral devices by means of head movements. In: IEEE 10th International Conference on Rehabilitation Robotics. IEEE. p. 124–31. https://doi.org/ 10.1109/ICORR.2007.4428417. [23] Kguyen HT, King LM, Knight G. Real-time, head movement system and embedded linux implementation for the control of powered wheelchairs. IEEE. San Francisco, vol. 2. p. 4892–5. https://doi.org/10.1109/IEMBS.2004.1404353. [24] Taylor PB, Nguyen HT. Performance of a head-movement interface for wheelchair control. IEEE, vol. 2. p. 1590–3. https://doi.org/10.1109/ IEMBS.2003.1279669. [25] Trevor J, Hug N. Neural network control of wheelchairs using telemetric head movement. IEEE., Vol. 5.. p. 2731–3. https://doi.org/10.1109/ IEMBS.1998.745241. [26] Bagewdi Sanket S, Shikhar D. Design and Development of Smart System to Assist Quadriplegics. In: IEEE international conference on advances in engineering and technology research. IEEE. p. 1–6. https://doi.org/10.1109/ ICAETR.2014.7012869.
13
[27] Orengo G, Lagati A, Saggio G. Modeling wearable bend sensor behavior for human motion capture. IEEE Sens J 2014;14(7):2307–16. https://doi.org/ 10.1109/JSEN.2014.2309997. [28] Saggio G, Quitadamo L, Albero L. Development and evaluation of a novel lowcost sensor-based knee flexion angle measurement system. Knee 2014;21 (5):896–901. https://doi.org/10.1016/j.knee.2014.04.014. [29] Saggio G, Sbernini L. New scenarios in human trunk posture measurements for clinical applications. In: IEEE International Symposium on Medical Measurements and Applications. p. 13–7. https://doi.org/10.1109/ MeMeA.2011.5966651. [30] Saggio G, Orengo G, Pallotti A, Errico V, Ricci M. Sensory systems for human body gesture recognition and motion capture. In: International Symposium on Networks, Computers and Communications, ISNCC 2018. https://doi.org/ 10.1109/ISNCC.2018.8531054. [31] Errico V, Ricci M, Pallotti A, Giannini F, Saggio G. Ambient assisted living for tetraplegic people by means of an electronic system based on a novel sensory headwear. In: Symposium on Medical Measuraments and Applications. IEEE. https://doi.org/10.1109/MeMeA.2018.8438638. [32] Al Agha K, Bertin M-H, Dang T, Guitton A, Minet P, Val T, et al. Which wireless technology for industrial wireless sensor networks? The development of OCARI technology. IEEE Trans Ind Electron 2009;56(10):4266–78. https://doi. org/10.1109/TIE.2009.2027253. [33] Gungor VC, Hancke GP. Industrial wireless sensor networks: challenges, design principles, and technical approaches. IEEE Trans Ind Electron 2009;56 (10):4258–65. https://doi.org/10.1109/TIE.2009.2015754. [34] Levron Y, Shmilovitz D. A power management strategy for minimization of energy storage reservoirs in wireless systems with energy harvesting. IEEE Trans Circuits Syst I Regul Pap 2010:1–11. https://doi.org/10.1109/ TCSI.2010.2073830. [35] Xiao H, Shao H, Yang K, Yang F, Wang W. Multiple timescale energy scheduling for wireless communication with energy harvesting devices ISSN=1805-9600. Radioeng J September 2012;Volume 21(Issue 3):815–22. [36] Khansalee E, Zhao Yan, Leelarasmee E, Nuanyai K. A dual-band rectifier for RF energy harvesting systems. In: 11th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and information Technology ECTI-CON (14 – 17 May 2014). p. 1–4. https://doi.org/10.1109/ ECTICon.2014.6839870. [37] Gutierrez M, Shahidi A, Berdy D, Peroulis D. Design and characterization of a low frequency 2-dimensional magnetic levitation kinetic energy harvester. ELSEVIER Sensors Actuat A: Phys 2015;236:1–10. https://doi.org/10.1016/j. sna.2015.10.009. [38] Piñuela M, Mitcheson PD, Lucyszyn S. Ambient RF energy harvesting in urban and semi-urban environments. IEEE Trans Microw Theory Tech July 2013;61 (7):2715–26. https://doi.org/10.1109/TMTT.2013.2262687. [39] Ricci M, Terribili M, Giannini F, Errico V, Pollotti A, Galasso C, Tomasell L, Sias S, Saggio G. Wearable-based electronics to objectively support diagnosis of motor impairments in school-aged children. J Biomech 2019;83:243–52. https://doi.org/10.1016/j.jbiomech.2018.12.005. [40] Saggio G, Orengo G, Pallotti A, Errico V, Ricci M. Evaluation of an integrated sensory glove at decreasing joint flexion degree. In: IEEE International Symposium on Medica Measurements and Applications. https://doi.org/ 10.1109/MeMeA.2018.8438767. [41] Leoni Alfiero, Stornelli Vincenzo, Ferri Giuseppe, Orengo Giancarlo, Errico Vito, Pallotti Antonio, Saggio Giovanni. A 10-17 DOF sensory gloves with harvesting capability for smart healthcare. JCOMSS 2019;15(2). [42] Sun M, Ranasinghe D, Al-Sarawi SF. RF energy harvester with peak power conversion efficiency tracking. In: 2016 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS), Jeju, South Korea. p. 107–10. https://doi.org/ 10.1109/APCCAS.2016.7803908. [43] Gleonec PD, Ardouin J, Gautier M, Berder O. Architecture exploration of multisource energy harvester for IoT nodes. In: 2016 IEEE Online Conference on Green Communications (OnlineGreenComm), Piscataway, NJ, USA. p. 27–32. https://doi.org/10.1109/OnlineGreenCom.2016.7805402. [44] Shi JJ, Huang XD. Electrostatic energy harvester based on charge-trapping nonvolatile memory structure. In: 2016 IEEE International Conference on Electron Devices and Solid-State Circuits (EDSSC), Hong Kong, Hong Kong. p. 153–6. https://doi.org/10.1109/EDSSC.2016.7785232. [45] Wang Z, Zhang W, Jin D, Xie H, Xiaoqiang L. A full-wave RF energy harvester based on new configurable diode connected MOSFETs. In: 2016 IEEE International Conference on Microwave and Millimeter Wave Technology (ICMMT), Beijing. p. 117–9. https://doi.org/10.1109/ICMMT.2016.7761695. [46] Broutas P, Contopanagos H, Tsoukalas D, Chatzandroulis S. A RF power harvester with integrated antenna capable of operating near ground planes. Sens Actuat, A 2012;186:284–8. https://doi.org/10.1016/j. sna.2012.05.040. [47] Di Marco P, Leoni A, Pantoli L, Stornelli V, Ferri G. Remote sensor networks with efficient energy harvesting architecture. In: 2016 12th Conference on Ph. D. Research in Microelectronics and Electronics (PRIME), Lisbon. p. 1–4. https://doi.org/10.1109/PRIME.2016.7519524. [48] Di Marco P, Stornelli V, Ferri G, Pantoli L, Leoni A. Dual band harvester architecture for autonomous remote sensors. Sens Actuat, A 2016;247:598–603. https://doi.org/10.1016/j.sna.2016.06.040. [49] Pantoli L, Leoni A, Stornelli V, Ferri G. An IC architecture for RF energy harvesting systems. J Commun Software Syst 2017;13(2):96. , https://doi.org/ 10.24138/jcomss.v13i2.377.
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[50] Pantoli L, Leoni A, Stornelli V, Ferri G. Energy harvester for remote sensors systems. In: 2016 International Multidisciplinary Conference on Computer and Energy Science (SpliTech). https://doi.org/10.1109/splitech.2016.7555933. [51] Leoni A, Pantoli L, Stornelli V, Ferri G, Solic P, Russo M. A Combined 90/900 MHz IC Architecture for for power-assisting in IoT applications. J Commun Software Syst 2018;vol. 14(no. 1). https://doi.org/10.24138/jcomss.v14i1.451. [52] Leoni A, Pantoli L, Stornelli V, Ferri G, Russo M, Solic P. 90/900 MHz IC architecture for autonomous systems Article number 8019240. Electronic ISBN: 978-953-290-071-2. In: 2017 2nd International Multidisciplinary Conference on Computer and Energy Science (SpliTech), Split. p. 1–4. [53] Stornelli V, Leoni A, Ferri G, Errico V, Ricci M, Pallotti A. A multi-source energy harvesting sensory glove electronic architecture Article number 8448343. Electronic ISBN: 978-953-290-083-5. 2018 3rd International Conference on Smart and Sustainable Technologies, SpliTech 2018, 27 August 2018. [54] Leoni A, Stornelli V, Ferri G, Errico V, Ricci M, Pallotti A, et al. human body powered sensory glove system based on multisource energy harvester. In: PRIME 2018–14th Conf PhD Res Microelectron Electron. p. 113–6. https://doi. org/10.1109/PRIME.2018.8430362. [55] Dementyev A et al. Power consumption analysis of bluetooth low energy, ZigBee and ANT sensor nodes in a cyclic sleep scenario. In: IEEE International Wireless Symposium (IWS). p. 1–4. https://doi.org/10.1109/IEEEIWS.2013.6616827.
[56] Saad C et al. Comparative performance analysis of wireless communication protocols for intelligent sensors and their applications. Int J Adv Comp Sci Appl (IJACSA) 5.4 2014,;5(4):76–85. [57] Lee JS, Dong Ming-Feng, Sun Yuan-Heng. A preliminary study of low power wireless technologies: ZigBee and bluetooth low energy. In: 2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA). IEEE. p. 135–9. https://doi.org/10.1109/ICIEA.2015.7334098. [58] Lee J-S, Su YW, Chung-Chou S. A comparative study of wireless protocols: bluetooth, UWB, ZigBee, and Wi-Fi. In: The 33rd Annual Conference of the IEEE Industrial Electronics Society (IECON). IEEE. p. 46–51. https://doi.org/10.1109/ IECON.2007.4460126. [59] Shahzad K, Oelmann B. A comparative study of in-sensor processing vs. raw data transmission using ZigBee, BLE and Wi-Fi for data intensive monitoring applications. In: 11th International Symposium on Wireless Communications Systems (ISWCS). IEEE. p. 519–24. https://doi.org/10.1109/ISWCS.2014.6933409. [60] Mikhaylov Konstantin, Plevritakis Nikolaos, Tervonen Jouni. Performance analysis and comparison of bluetooth low energy with IEEE 802.15.4 and SimpliciTI. JSAN 2013;2(3):589–613. https://doi.org/10.3390/jsan2030589. [61] Siekkinen M et al. How low energy is bluetooth low energy? Comparative measurements with ZigBee, 802.15.4. In: 2012 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) IEEE. 2012. p. 232–7. https://doi.org/10.1109/WCNCW.2012.6215496.
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International Journal of Electronics and Communications (AEÜ) journal homepage: www.elsevier.com/locate/aeue
Regular paper
A low-cost energy-harvesting sensory headwear useful for tetraplegic people to drive home automation Giuseppe Piscitelli a, Vito Errico a,⇑, Mariachiara Ricci a, Franco Giannini a, Giovanni Saggio a, Alfiero Leoni b, Vincenzo Stornelli b, Giuseppe Ferri b, Leonardo Pantoli b, Iolanda Ulisse b a b
University of Rome ‘‘Tor Vergata”, Dept. of Electronic Engineering, Rome, Italy University of L’Aquila, Dept. of Industrial and Information Engineering and Economics, L’Aquila, Italy
a r t i c l e
i n f o
Article history: Received 4 March 2019 Accepted 10 May 2019
Keywords: Energy harvesting Sensory headwear IMU Home automation
a b s t r a c t Tetraplegic people need continuous assistance in every daily activity. Assistive technologies can improve, to a certain degree, their quality of life allowing partial autonomy with powering their residual capability of movements. In this work, we propose a novel wire-free low-cost user-friendly battery-operated sensory headwear, which allows home automation controlled by head movements. The headwear is equipped with an inertial measurement unit (IMU), a low power microcontroller and a transmission module to measure, condition and wireless transmit data related to head movements. Such a sensory headwear allows the subject, simply by head movements, either to select one computer icon among an ensemble or to select one actuator, among a number of others. Each icon and each actuator drive a specific physical action in a home or work environment. We devoted particular efforts to increase the battery autonomy, by means of radio frequency energy harvesting solutions, for lasting operational mode. The harvester, based on commercial chipsets, was optimized in the 2.4–2.5 GHz range to exploit headwear itself radiated energy and environmental energy, in particular from Wi-Fi and Bluetooth surrounding devices. An average efficiency, calculated as output to input power ratio, of around 60% at 5dBm input power level has been obtained. Ó 2019 Elsevier GmbH. All rights reserved.
1. Introduction Tetraplegia is a condition of partial or total paralysis of the four limbs and torso. It can result either from traumatic events involving spinal cord injury (SCI), with looseness of four limbs motor capabilities in the 32% of cases [1,2] or from progressive pathologies such as muscular dystrophies, amyotrophic lateral sclerosis and multiple sclerosis [3,4]. Tetraplegia can result from congenital diseases too, such as infantile cerebral palsy and some specific forms of muscular dystrophy, involving cognitive disorders and epileptic crisis predisposition too, even in young age [3–7]. Because of hard motor impairments, tetraplegic people need continuous assistance even for every-day actions, as it is for opening a window or turning on the lights. The assistive technology can improve their quality of life, helping them to act autonomously in home or work environment. We can mention the hands-free human machine
⇑ Corresponding author at: DIE-Dipartimento di Ingegneria Elettronica, Università degli Studi di ‘‘Roma – Tor Vergata”, Via del Politecnico, 1, 00133 Roma, Italy. E-mail addresses: [email protected] (V. Errico), [email protected] (G. Saggio), [email protected] (A. Leoni). https://doi.org/10.1016/j.aeue.2019.05.015 1434-8411/Ó 2019 Elsevier GmbH. All rights reserved.
interfaces (HMI), which include eye-movement tracking (Tobii Technology, in Sweden or The Eye Tribe in Denmark) [8], voice recognition (Alexa by Google, USA, Cortana by Microsoft, USA) [9,10], mouth-stick systems [11,12] and brain computer interfaces (BCI) [13–17]. Those HMI present meaningful advantages but some drawbacks too, such as wearisome and cumbersome eyemovement tracking, user-unfriendly mouth-stick systems, and high-demanding setup and computational time BCI. In recent years, the hands-free HMI technologies enriched the portfolio with inertial sensor-based devices. Integrated inertial sensors can include 3-axes accelerometer, gyroscopes and magnetoresistances, so to implement compact, light, low-cost inertial measurement units (IMUs). Mandel and Röfen realized an IMU-based headwear to drive a wheelchair by means of head movements [18–21]. Bureau at al. realized an inertial HMI to drive a wheelchair and to command movements of a PC pointer [22]. Joseph and Nguyen used a wireless IMU module to fed data to a processing unit to define neural network-based commands [23–25], but with a computational delay ranging within 948–1624 ms. Recently, Samket and Shikhar realized a wireless inertial HMI based system, integrated with a rechargeable battery, a microcontroller, an
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accelerometer, an ADC converter and a wireless ZigBee module. This system, together with an eye-blink sensor integrated on glasses, allowed interaction with a wheelchair model, a personal computer, and a scale-model of home automation system [26]. Wearable electronics devices have been gaining more and more importance for assessing body motion capabilities of able people [27–30] or for improving residual motion capabilities of disabled people. This is the case of the sensory headwear, reported in our previous work [31], useful to help tetraplegic people. The realized inertial HMI for home automation facilities [31] is controlled by means of the sensory headwear, which integrated IMU, microcontroller and radio frequency transmission module, all powered by rechargeable battery. The head movements were converted into home automation commands in domestic or work environments by feeding data wirelessly to a USB dongle, connected to a personal computer, on which an ad-hoc realized graphical interface unit (GUI) runs to drive a home automation system. A drawback of the previously proposed system was the battery lifetime and dimensions. Consequently, here we propose an extended battery lifetime architecture taking advantage from harvesting technique, gathering environmental radio frequency (RF) energy, based on commercial components. Energy recovery from environmental power sources is an innovative way to capture and store energy. Energy Harvesting (EH) systems are particularly suitable for low voltage and/or low power circuits, in wireless sensor network (WSN) systems [32–41], and wearable electronics. By means of EH systems, the ambient energy can be converted into other useful forms [42–54]. In particular, EH systems can gather RF energy and convert it to DC energy with good efficiency. Furthermore, they gain in performance improvement with wide dynamic and spectrum range handling. In particular, the harvester is designed to gather radiated power in the 2.4–2.5 GHz frequency band of the headwear circuitry itself and from the surrounding radiated environmental energies, as those from Wi-Fi and Bluetooth devices. Within such a frame, our work focused on improving a sensory headwear system developed for increasing the autonomy of tetraplegic people. The improvements regard compactness, lightness, costs and power consumption, with optimized hardware solution, especially concerning EH techniques. 2. The designed systems Fig. 1 shows the architectural block scheme of the overall system. The headwear allows the user to drive, simply by head movements, home actuators that can be selected, directly or indirectly, by choosing self-explaining icons on a screen.
Fig. 1. Headwear system: data gathered from the head movements are wireless sent to a receiver and converted into home automation commands.
The headwear hosts motion tracking sensors, a power supply battery, a RF wireless transmission module and an energy harvesting circuitry. Fig. 2 depicts the architectural block scheme of the system. The IMU gather motion data, further processed by a microcontroller (MCU), and wirelessly transmitted by a system-on-chip (SoC). The Li-Po battery, the charge management system (CMS) and the buck regulator provide a stable source voltage. The USB/ UART IC allows programming microcontroller directly on board through the USB connector. The data elaboration core is the pico-power 8-bit microcontroller (MCU) ATmega328PB (by Microchip), an updated version of the previously adopted ATmega328P [31], which integrates additional serial peripheral interfaces and time counter modules. The wireless trans-receiver system-on-chip (SoC) and the IMU are the nRF8001 BLE (by Nordic Semiconductor) and the BMI055 IMU unit (by Bosch), respectively. MCU, IMU and SoC have a serial peripheral interface (SPI) to convey data flow. The nRF8001 transceiver is impedance matched to a 2.4 GHz ceramic SMD antenna (W3008C by Pulse Electronics) by means of a balanced-unbalanced (balun) transformer (2450BM14A0002 by Jhoanson Technology). One-cell 190mAh Li-Po battery (HPL402323-2C) provides the source, which is step-down DC-DC converted by the TPS622317 buck switching regulator (by Texas Instruments). A charge management system (CMS), based on pass-through technology, is realized by means of a BQ24232 device (by Texas Instruments). An USB-to-UART converter, the FT230XS (by FTDI), realizes the communication with an external USB 2.0 full speed port through a micro-b USB connector, allowing programming the microcontroller’s firmware. Selected ICs and adopted mastering PCB process rules were designed to reduce costs, size and power consumption of the system, as further detailed. SMD capacitors and resistors have standard 0805 package of 2 1.2 mm2; the battery dimensions are 23.5 24.5 4.2 mm3. Table 1 reports size and average costs of ICs. Particular attention was paid to the wireless communication protocol. Differently from the wireless local area network (WLAN) previously adopted [31], here we utilized a wireless personal area network (WPAN), optimized for low power consumption and harvesting applications. In particular, we adopted the Bluetooth low energy (BLE) protocol that ensures higher transmission efficiency with lower payload size [55–61], with respect to Wi-Fi, Bluetooth or ZigBee protocols. The working average current is as low as 936 mA, estimated considering a 20 ms (connIntervall) periodic transmission of 25 Byte payload (Npl), an idle mode of BLE module for no-data availability, and timing and current absorption of each nRF8001 sub-routine as reported in Table 2. Eq. (1) represents the average absorbed current (Iav), considering 10 Byte of data frames forming data (Novhd):
Fig. 2. Architectural block scheme of the system headwear. Continuous and dotted lines represent power and data paths, respectively.
G. Piscitelli et al. / Int. J. Electron. Commun. (AEÜ) 107 (2019) 9–14 Table 1 Average price and dimensions of adopted ICs (January 2019). IC
Package size (mm mm)
prize of minimum 5000 pcs (€)
ATmega328PB BMI055 BQ24232 FT230XS nRF8001 TPS622317
7 7 (TQFP) 3 4.5 (QFN) 3 3 (VQFN) 4 5 (SSOP) 5 5 (QFN) 1 1.45 (SON)
0.922 2.079 1.109 1.300 1.630 0.452
Table 2 Timing and current absorption of each sequentially sub-routine executed during one data frame transmission by nRF8001. Sub-routine
Current absorbed (mA)
Timing
Wake-up and preprocessing Reception IFS (Inter Frame Space) Transmission (0 dBm) Post-processing
I1 = 3.5 IRx = 14.6 I2 = 7 ITx = 12.7 I3 = 5
T1 = 1 ms s = 8 ms/byte T2 = 150 ms s = 8 ms/byte T3 = 2 ms
X 1 ðT i Ii Þ þ sIRx Nov hd þ sIRx ðNov hd þ Npl Þ i connInterv all i ¼ 1; 2; 3
I av ¼
ð1Þ
The adopted IMU contributes to maintain a low power absorption too, the integrated gyroscopes and accelerometers being set to idle mode when data transmission is not necessary. In particular, the gyroscopes current requirement varies within 1.667 mA and 5 mA, while the accelerometers require a current of 19.2 mA at 125 sample/sec and 55.25 Hz output data rate. The peak current absorptions for gyroscope and accelerometer modules are 5.1 mA and 130 mA, respectively. Moreover, we set the TPS622317 to work in power save configuration, so that it runs in pulse frequency modulation (PFM) until a high input voltage or low load current is imposed or drained, respectively, and in pulse width modulation (PWM) mode otherwise. The bias absorbed current depends on operational mode and varies from 25mA in PFM to 3 mA in PWM, respectively. Concerning the battery powered circuitry and harvesting system, the block architecture is shown in Fig. 3. The storage capacitor, C, gathers the energy converted from the incoming RF signal to DC by the rectifier block and charges up to a selected threshold voltage. When VC reaches its maximum value, a digital line VINT is set high by the detector block and the boost converter turns on, so VDC rises up to the DC-DC converter pre-settled output voltage. The storage capacitor then discharges until VC reaches its minimum value, and detector then sets VINT low, shutting off the boost converter until VC charges back up to its maximum value. This strategy allows first to decouple the harvester from the load and,
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successively, to decouple the load from the harvester, giving to the voltage regulator enough energy to be manipulated. Generally, the conversion efficiency is determined for a defined input power signal level, at a given frequency, once the load impedance is specified. In order to have good values of this characteristic, the input RF power matching is one of the more delicate aspects in the design of the antenna-rectifier circuitry. Concerning the RF to DC block, the Powercast PCC110 was used while the PCC210, always from Powercast, was selected for the DC-DC converter block. Finally, the battery charger controller is necessary when the harvester charges the battery. The switch limits the current draw on the battery from the harvester itself to a few nA. This ensures that the designed circuitry does not discharge the battery during the periods of non-harvesting.
3. Experimental results The electronics of the sensory headwear resides on a dual-layer board hosting surface mounting devices (SMDs). Fig. 4a and b shows front and back view, respectively, of the electronic circuitry, 51.92 30.23 mm2 in dimensions. Table 3 reports peak and average currents of the circuitry in fully operating mode. The peak current value is compatible with the maximum output current deliverable from TPS622317 (500 mA). Moreover, the buck regulator absorbs an instant maximum current of 343.3 mA in PWM mode when coupled with a 1 ± 20% mH inductor, with a load current of 5.149 mA. The irradiated power at BLE bandwidth central frequency (2.48 GHz) can be set at a maximum of 2.7dBm, related to the output transmission power of the nRF8001 IC (max 4dBm) and depending on the antenna efficiency. Taking into account the average operative current of the circuitry in fulloperational mode and the nominal 190mAh value of the Li-Po battery, the worst-case time duration is about 37 h without recharging and no harvester applied. The complete hybrid prototype of the harvester circuit was made and assembled in our laboratory on a FR4 substrate. Fig. 5 shows the rendering of the harvester PCB. Being the large and small signal parameter of the rectifier components not available, a circuit optimization technique using a dedicated test bench was performed for sizing the passive components for circuit optimization (in terms of conversion efficiency) in the desired 2.4– 2.5 GHz frequency band. A commercial compact GW26.0112 antenna from Touglas was used in the test bench. Fig. 6 shows the measured results of RF-to-DC energy conversion efficiency, defined as circuit output power to input power ratio. The structure reaches its maximum conversion efficiencies of 65% for an input power of about 5dBm while performs about 50% in the 5 to 0 dBm range, that is the expected radiated output power from the headwear transmitter. In general, the power efficiency is quite con-
Fig. 3. Block scheme of the battery powered circuitry and harvesting system.
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Fig. 4. (a) Front and (b) back views of the electronic circuitry devoted to measure, acquire, condition and transmit data related to the movements of the head of the subject.
Table 3 Current consumption for principal ICs of device, estimated at 2.9 V in reported operating conditions, starting from datasheet DC characterization.
ATmega328PB BMI055 nRF8001
Operating condition
Average current (mA)
Peak current (mA)
Active Mode, 10 MHz clock speed BWacc = 62.5 Hz, BWgyro = 64 Hz, 10 ms idle interval connInterval = 20 ms, payload = 25 byte, 0 dBm power transmission
2.50 1.67 0.96
2.50 5.10 14.60
5.15
22.20
total
Fig. 5. Final harvester PCB render.
Finally, the optimized harvester total average efficiency is about 60% at 5dBm input power level. In several working condition, with nearfield Wi-Fi sources as home router, this solution was estimated to be useful to increase the duration of the system of about 10%. 4. Conclusions
Fig. 6. Measured harvester efficiency in the 20 +20 dBm input power range at different frequency.
stant within the wide input power range under test from 5 to 15dBm; in other words, the architecture offers general constant performances over a wide range of input power.
We realized a novel hands-free sensory headwear-based HMI device useful for tetraplegic people to drive autonomously home automation devices. Key elements were compactness, low-cost, and battery lasting strategies. The system is compatible with different commercial apparatuses (such as mobile phones, personal computers and tablets), because of the adopted BLE as a standard wireless communication protocol. With respect to other handsfree HMI devices, the proposed system gains in user-friendly capabilities, with a total cost as-low-as of about 75€ (even lower for a large-scale production). Declaration of Competing Interest None.
G. Piscitelli et al. / Int. J. Electron. Commun. (AEÜ) 107 (2019) 9–14
References [1] Wyndaelee M, Wyndaele J-J. Incidence, prevalence and epidemiology of spinal cord injury: what learns a worldwide literature survey? Spinal Cord 2006;44:523–9. https://doi.org/10.1038/sj.sc.3101893. [2] Cosar SNS et al. Demographic characteristics after traumatic and nontraumatic spinal cord injury: a retrospective comparison study. Spinal Cord 2010;48:862–6. https://doi.org/10.1038/sc.2010.49. [3] Pugliatti M et al. The epidemiology of multiple sclerosis in Europe. Eur J Neurol 2006;13:700–22. https://doi.org/10.1111/j.1468-1331.2006.01342.x. [4] Murati R et al. Il Bergamini di neurologia. A cura di Edizioni Libreria Cortina Torino. 2012. pp. 467–481, 641–645, 713–715. ISBN: 9788882391683. [5] Bartolozzi G. Pediatria, Principi e pratica clinica. Edra Masson; 2013. p. 1464–5. ISBN: 8821432874. [6] Mah Jean K et al. A systematic review and meta-analysis on the epidemiology of Duchenne and Becker muscular dystrophy. Neuromuscolar Disorder 2014;24:482–91. https://doi.org/10.1016/j.nmd.2014.03.008. [7] Careddu P et al. Swartz Tiene – Manuale di pediatria. A cura di Casa Editrice Ambrosiana 1993. pp. 1182-1183, 1224-1227. EAN13: 9788808084668. [8] Chandra S et al. Eye tracking based human computer interaction: applications and their uses. In: International Conference on Man and Machine Interfacing. IEEE. p. 1–5. https://doi.org/10.1109/MAMI.2015.7456615. [9] Arriany A, Mohamed Musbah S. Applying voice recognition technology for smart home networks. In: IEEE International Conference on Engineering & MIS (ICEMIS). p. 1–6. https://doi.org/10.1109/ICEMIS.2016.7745292. [10] Zhu J et al. Developing a voice control system for Zigbee-based home automation networks. In: 2nd IEEE International Conference on Network Infrastructure and Digital Content, IEEE. p. 737–41. https://doi.org/10.1109/ ICNIDC.2010.5657880. [11] Arshak K, Buckley D, Kaneswaran K. Review of assistive devices for electric powered wheelchairs navigation. ITB J 2006;Vol. 7(Iss. 1):13–23. https://doi. org/10.21427/D7TB37. [12] Lu L, Wen JT. Human-robot cooperative control for mobility impaired individuals. Am Control Conf. ACC 2015:447–52. https://doi.org/10.1109/ ACC.2015.7170776. [13] Rebsamen B et al. A brain-controlled wheelchair based on P300 and path guidance. In: IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics. p. 1101–6. https://doi.org/10.1109/BIOROB.2006.1639239. [14] Rebsamen B et al. Controlling a wheelchair using a BCI with low information transfer rate. In: IEEE 10th International Conference on Rehabilitation Robotics. p. 1003–8. https://doi.org/10.1109/ICORR.2007.4428546. [15] Cavrini F, Bianchi L, Quitadamo LR, Saggio G. A Fuzzy Integral Ensemble Method in Visual P300 Brain-Computer Interface. Comp Intell Neurosci 2016;2016:1–9. https://doi.org/10.1155/2016/9845980. [16] Bianchi L, Quitadamo LR, Abbafati M, Marciani MG, Saggio G. Introducing NPXLab 2010: a tool for the analysis and optimization of P300 based BrainComputer Interfaces. In: 2nd International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL2009), Bratislava, Slovak Republic, November 24-27. https://doi.org/10.1109/ISABEL.2009.5373621. [17] Zhang R et al. A BCI-Based Environmental Control System for Patients With Severe Spinal Cord Injuries. IEEE Trans Biomed Eng 2017;64(8):1959–71. https://doi.org/10.1109/TBME.2016.2628861. [18] Mandel C, R. T, Frese U. Applying a 3DOF orientation tracker as a human-robot interface for autonomous wheelchairs. In: IEEE International Conference on Rehabilitation Robotics. IEEE. p. 52–9. https://doi.org/10.1109/ ICORR.2007.4428406. [19] Mandel C, Frese U. Comparison of wheelchair user interfaces for the paralysed: head-joystick vs. verbal path selection from an offered route-set. In: Proceedings of the 3rd European Conference on Mobile Robots, EMCR 2007, September 19-21. p. 1–6. [20] Mandel Christian, Frese Udo, Röfer Thomas. In: Lecture Notes in Computer ScienceComputational and Ambient Intelligence. Berlin, Heidelberg: Springer Berlin Heidelberg; 2007. p. 1052–9. https://doi.org/10.1007/978-3-54073007-1_127. [21] Rofer T, Mandel C, Laue T. Controlling an automated wheelchair via joystick/headjoystick supported by smart driving assistance. In: IEEE 11th International Conference on Rehabilitation Robotics, Kyoto International Conference Center. IEEE. p. 743–8. https://doi.org/10.1109/ICORR.2009.5209506. [22] Bureau M et al. Non-invasive, wireless and universal interface for the control of peripheral devices by means of head movements. In: IEEE 10th International Conference on Rehabilitation Robotics. IEEE. p. 124–31. https://doi.org/ 10.1109/ICORR.2007.4428417. [23] Kguyen HT, King LM, Knight G. Real-time, head movement system and embedded linux implementation for the control of powered wheelchairs. IEEE. San Francisco, vol. 2. p. 4892–5. https://doi.org/10.1109/IEMBS.2004.1404353. [24] Taylor PB, Nguyen HT. Performance of a head-movement interface for wheelchair control. IEEE, vol. 2. p. 1590–3. https://doi.org/10.1109/ IEMBS.2003.1279669. [25] Trevor J, Hug N. Neural network control of wheelchairs using telemetric head movement. IEEE., Vol. 5.. p. 2731–3. https://doi.org/10.1109/ IEMBS.1998.745241. [26] Bagewdi Sanket S, Shikhar D. Design and Development of Smart System to Assist Quadriplegics. In: IEEE international conference on advances in engineering and technology research. IEEE. p. 1–6. https://doi.org/10.1109/ ICAETR.2014.7012869.
13
[27] Orengo G, Lagati A, Saggio G. Modeling wearable bend sensor behavior for human motion capture. IEEE Sens J 2014;14(7):2307–16. https://doi.org/ 10.1109/JSEN.2014.2309997. [28] Saggio G, Quitadamo L, Albero L. Development and evaluation of a novel lowcost sensor-based knee flexion angle measurement system. Knee 2014;21 (5):896–901. https://doi.org/10.1016/j.knee.2014.04.014. [29] Saggio G, Sbernini L. New scenarios in human trunk posture measurements for clinical applications. In: IEEE International Symposium on Medical Measurements and Applications. p. 13–7. https://doi.org/10.1109/ MeMeA.2011.5966651. [30] Saggio G, Orengo G, Pallotti A, Errico V, Ricci M. Sensory systems for human body gesture recognition and motion capture. In: International Symposium on Networks, Computers and Communications, ISNCC 2018. https://doi.org/ 10.1109/ISNCC.2018.8531054. [31] Errico V, Ricci M, Pallotti A, Giannini F, Saggio G. Ambient assisted living for tetraplegic people by means of an electronic system based on a novel sensory headwear. In: Symposium on Medical Measuraments and Applications. IEEE. https://doi.org/10.1109/MeMeA.2018.8438638. [32] Al Agha K, Bertin M-H, Dang T, Guitton A, Minet P, Val T, et al. Which wireless technology for industrial wireless sensor networks? The development of OCARI technology. IEEE Trans Ind Electron 2009;56(10):4266–78. https://doi. org/10.1109/TIE.2009.2027253. [33] Gungor VC, Hancke GP. Industrial wireless sensor networks: challenges, design principles, and technical approaches. IEEE Trans Ind Electron 2009;56 (10):4258–65. https://doi.org/10.1109/TIE.2009.2015754. [34] Levron Y, Shmilovitz D. A power management strategy for minimization of energy storage reservoirs in wireless systems with energy harvesting. IEEE Trans Circuits Syst I Regul Pap 2010:1–11. https://doi.org/10.1109/ TCSI.2010.2073830. [35] Xiao H, Shao H, Yang K, Yang F, Wang W. Multiple timescale energy scheduling for wireless communication with energy harvesting devices ISSN=1805-9600. Radioeng J September 2012;Volume 21(Issue 3):815–22. [36] Khansalee E, Zhao Yan, Leelarasmee E, Nuanyai K. A dual-band rectifier for RF energy harvesting systems. In: 11th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and information Technology ECTI-CON (14 – 17 May 2014). p. 1–4. https://doi.org/10.1109/ ECTICon.2014.6839870. [37] Gutierrez M, Shahidi A, Berdy D, Peroulis D. Design and characterization of a low frequency 2-dimensional magnetic levitation kinetic energy harvester. ELSEVIER Sensors Actuat A: Phys 2015;236:1–10. https://doi.org/10.1016/j. sna.2015.10.009. [38] Piñuela M, Mitcheson PD, Lucyszyn S. Ambient RF energy harvesting in urban and semi-urban environments. IEEE Trans Microw Theory Tech July 2013;61 (7):2715–26. https://doi.org/10.1109/TMTT.2013.2262687. [39] Ricci M, Terribili M, Giannini F, Errico V, Pollotti A, Galasso C, Tomasell L, Sias S, Saggio G. Wearable-based electronics to objectively support diagnosis of motor impairments in school-aged children. J Biomech 2019;83:243–52. https://doi.org/10.1016/j.jbiomech.2018.12.005. [40] Saggio G, Orengo G, Pallotti A, Errico V, Ricci M. Evaluation of an integrated sensory glove at decreasing joint flexion degree. In: IEEE International Symposium on Medica Measurements and Applications. https://doi.org/ 10.1109/MeMeA.2018.8438767. [41] Leoni Alfiero, Stornelli Vincenzo, Ferri Giuseppe, Orengo Giancarlo, Errico Vito, Pallotti Antonio, Saggio Giovanni. A 10-17 DOF sensory gloves with harvesting capability for smart healthcare. JCOMSS 2019;15(2). [42] Sun M, Ranasinghe D, Al-Sarawi SF. RF energy harvester with peak power conversion efficiency tracking. In: 2016 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS), Jeju, South Korea. p. 107–10. https://doi.org/ 10.1109/APCCAS.2016.7803908. [43] Gleonec PD, Ardouin J, Gautier M, Berder O. Architecture exploration of multisource energy harvester for IoT nodes. In: 2016 IEEE Online Conference on Green Communications (OnlineGreenComm), Piscataway, NJ, USA. p. 27–32. https://doi.org/10.1109/OnlineGreenCom.2016.7805402. [44] Shi JJ, Huang XD. Electrostatic energy harvester based on charge-trapping nonvolatile memory structure. In: 2016 IEEE International Conference on Electron Devices and Solid-State Circuits (EDSSC), Hong Kong, Hong Kong. p. 153–6. https://doi.org/10.1109/EDSSC.2016.7785232. [45] Wang Z, Zhang W, Jin D, Xie H, Xiaoqiang L. A full-wave RF energy harvester based on new configurable diode connected MOSFETs. In: 2016 IEEE International Conference on Microwave and Millimeter Wave Technology (ICMMT), Beijing. p. 117–9. https://doi.org/10.1109/ICMMT.2016.7761695. [46] Broutas P, Contopanagos H, Tsoukalas D, Chatzandroulis S. A RF power harvester with integrated antenna capable of operating near ground planes. Sens Actuat, A 2012;186:284–8. https://doi.org/10.1016/j. sna.2012.05.040. [47] Di Marco P, Leoni A, Pantoli L, Stornelli V, Ferri G. Remote sensor networks with efficient energy harvesting architecture. In: 2016 12th Conference on Ph. D. Research in Microelectronics and Electronics (PRIME), Lisbon. p. 1–4. https://doi.org/10.1109/PRIME.2016.7519524. [48] Di Marco P, Stornelli V, Ferri G, Pantoli L, Leoni A. Dual band harvester architecture for autonomous remote sensors. Sens Actuat, A 2016;247:598–603. https://doi.org/10.1016/j.sna.2016.06.040. [49] Pantoli L, Leoni A, Stornelli V, Ferri G. An IC architecture for RF energy harvesting systems. J Commun Software Syst 2017;13(2):96. , https://doi.org/ 10.24138/jcomss.v13i2.377.
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G. Piscitelli et al. / Int. J. Electron. Commun. (AEÜ) 107 (2019) 9–14
[50] Pantoli L, Leoni A, Stornelli V, Ferri G. Energy harvester for remote sensors systems. In: 2016 International Multidisciplinary Conference on Computer and Energy Science (SpliTech). https://doi.org/10.1109/splitech.2016.7555933. [51] Leoni A, Pantoli L, Stornelli V, Ferri G, Solic P, Russo M. A Combined 90/900 MHz IC Architecture for for power-assisting in IoT applications. J Commun Software Syst 2018;vol. 14(no. 1). https://doi.org/10.24138/jcomss.v14i1.451. [52] Leoni A, Pantoli L, Stornelli V, Ferri G, Russo M, Solic P. 90/900 MHz IC architecture for autonomous systems Article number 8019240. Electronic ISBN: 978-953-290-071-2. In: 2017 2nd International Multidisciplinary Conference on Computer and Energy Science (SpliTech), Split. p. 1–4. [53] Stornelli V, Leoni A, Ferri G, Errico V, Ricci M, Pallotti A. A multi-source energy harvesting sensory glove electronic architecture Article number 8448343. Electronic ISBN: 978-953-290-083-5. 2018 3rd International Conference on Smart and Sustainable Technologies, SpliTech 2018, 27 August 2018. [54] Leoni A, Stornelli V, Ferri G, Errico V, Ricci M, Pallotti A, et al. human body powered sensory glove system based on multisource energy harvester. In: PRIME 2018–14th Conf PhD Res Microelectron Electron. p. 113–6. https://doi. org/10.1109/PRIME.2018.8430362. [55] Dementyev A et al. Power consumption analysis of bluetooth low energy, ZigBee and ANT sensor nodes in a cyclic sleep scenario. In: IEEE International Wireless Symposium (IWS). p. 1–4. https://doi.org/10.1109/IEEEIWS.2013.6616827.
[56] Saad C et al. Comparative performance analysis of wireless communication protocols for intelligent sensors and their applications. Int J Adv Comp Sci Appl (IJACSA) 5.4 2014,;5(4):76–85. [57] Lee JS, Dong Ming-Feng, Sun Yuan-Heng. A preliminary study of low power wireless technologies: ZigBee and bluetooth low energy. In: 2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA). IEEE. p. 135–9. https://doi.org/10.1109/ICIEA.2015.7334098. [58] Lee J-S, Su YW, Chung-Chou S. A comparative study of wireless protocols: bluetooth, UWB, ZigBee, and Wi-Fi. In: The 33rd Annual Conference of the IEEE Industrial Electronics Society (IECON). IEEE. p. 46–51. https://doi.org/10.1109/ IECON.2007.4460126. [59] Shahzad K, Oelmann B. A comparative study of in-sensor processing vs. raw data transmission using ZigBee, BLE and Wi-Fi for data intensive monitoring applications. In: 11th International Symposium on Wireless Communications Systems (ISWCS). IEEE. p. 519–24. https://doi.org/10.1109/ISWCS.2014.6933409. [60] Mikhaylov Konstantin, Plevritakis Nikolaos, Tervonen Jouni. Performance analysis and comparison of bluetooth low energy with IEEE 802.15.4 and SimpliciTI. JSAN 2013;2(3):589–613. https://doi.org/10.3390/jsan2030589. [61] Siekkinen M et al. How low energy is bluetooth low energy? Comparative measurements with ZigBee, 802.15.4. In: 2012 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) IEEE. 2012. p. 232–7. https://doi.org/10.1109/WCNCW.2012.6215496.