Abstract
Body capacitance change is an interesting signal for a variety of body sensor network applications in activity recognition. Although many promising applications have been published, capacitive on body sensing is much less understood than more dominant wearable sensing modalities such as IMUs and has been primarily studied in individual, constrained applications. This paper aims to go from such individual-specific application to a systemic analysis of how much the body capacitance is influenced by what type of factors and how does it vary from person to person. The idea is to provide a basic form which other researchers can decide if and in what form capacitive sensing is suitable for their specific applications. To this end, we present a design of a low power, small form factor measurement device and use it to measure the capacitance of the human body in various settings relevant for wearable activity recognition. We also demonstrate on simple examples how those measurements can be translated into use cases such as ground type recognition, exact step counting and gait partitioning.
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References
Skeldon, K.D., Reid, L.M., McInally, V., Dougan, B., Fulton, C.: Physics of the Theremin. Am. J. Phys. 66(11), 945–955 (1998)
Fritz, T.: ThereminVision-II instruction manual (2004)
Arshad, A., Khan, S., Alam, A.H.M.Z., Kadir, K.A., Tasnim, R., Ismail, A.F.: A capacitive proximity sensing scheme for human motion detection. In: 2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), pp. 1–5. IEEE (2017)
Hirsch, M., Cheng, J., Reiss, A., Sundholm, M., Lukowicz, P., Amft, O.: Hands-free gesture control with a capacitive textile neckband. In: Proceedings of the 2014 ACM International Symposium on Wearable Computers, pp. 55–58 (2014)
Bian, S., Lukowicz, P.: Capacitive sensing based on-board hand gesture recognition with TinyML. In: UbiComp-ISWC 2021 Adjunct: Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers, Virtual, USA. ACM, September 2021
Cohn, G., Morris, D., Patel, S., Tan, D.: Humantenna: using the body as an antenna for real-time whole-body interaction. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1901–1910 (2012)
Aliau Bonet, C., Pallàs Areny, R.: A fast method to estimate body capacitance to ground. In: Proceedings of XX IMEKO World Congress 2012, Busan, South Korea, 9–14 September 2019, pp. 1–4 (2012)
Aliau-Bonet, C., Pallas-Areny, R.: A novel method to estimate body capacitance to ground at mid frequencies. IEEE Trans. Instrum. Meas. 62(9), 2519–2525 (2013)
Buller, W., Wilson, B.: Measurement and modeling mutual capacitance of electrical wiring and humans. IEEE Trans. Instrum. Meas. 55(5), 1519–1522 (2006)
Forster, I.C.: Measurement of patient body capacitance and a method of patient isolation in mains environments. Med. Biol. Eng. 12(5), 730–732 (1974). https://doi.org/10.1007/BF02477239
Greason, W.D.: Quasi-static analysis of electrostatic discharge (ESD) and the human body using a capacitance model. J. Electrostat. 35(4), 349–371 (1995)
Huang, J., Wu, Z., Liu, S.: Why the human body capacitance is so large. In: Proceedings Electrical Overstress/Electrostatic Discharge Symposium, pp. 135–138. IEEE (1997)
Pallas-Areny, R., Colominas, J.: Simple, fast method for patient body capacitance and power-line electric interference measurement. Med. Biol. Eng. Comput. 29(5), 561–563 (1991). https://doi.org/10.1007/BF02442332
Sălceanu, A., Neacşu, O., David, V., Luncă, E.: Measurements upon human body capacitance: theory and experimental setup (2004)
Jonassen, N.: Human body capacitance: static or dynamic concept? [ESD]. In: Electrical Overstress/Electrostatic Discharge Symposium Proceedings 1998 (Cat. No. 98TH8347), pp. 111–117. IEEE (1998)
Fujiwara, O., Ikawa, T.: Numerical calculation of human-body capacitance by surface charge method. Electron. Commun. Jpn (Part I: Commun.) 85(12), 38–44 (2002)
Serrano, R.E., Gasulla, M., Casas, O., Pallàs-Areny, R.: Power line interference in ambulatory biopotential measurements. In: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No. 03CH37439), vol. 4, pp. 3024–3027. IEEE (2003)
Haberman, M., Cassino, A., Spinelli, E.: Estimation of stray coupling capacitances in biopotential measurements. Med. Biol. Eng. Comput. 49(9) (2011). Article number: 1067. https://doi.org/10.1007/s11517-011-0811-6
Fish, R.M., Geddes, L.A.: Conduction of electrical current to and through the human body: a review. Eplasty 9, e44 (2009)
TI: Texas Instrument LMC555, June 2016. http://www.ti.com/lit/ds/symlink/lmc555.pdf
TI: How do i design a-stable timer, oscillator, circuits using LMC555, TLC555, LM555, NA555, NE555, SA555, or SE555. https://e2e.ti.com/support/clock-andtiming/f/48/t/879112?tisearch=e2e-sitesearchamp;keymatch=lmc555
PJRC: Teensy 3.6. https://www.pjrc.com/store/teensy36.html
Grosse-Puppendahl, T.: Capacitive sensing and communication for ubiquitous interaction and environmental perception. Ph.D. thesis, Technische Universität (2015)
Cheng, J., Amft, O., Bahle, G., Lukowicz, P.: Designing sensitive wearable capacitive sensors for activity recognition. IEEE Sens. J. 13(10), 3935–3947 (2013)
Castle, G.S.P.: Contact charging between insulators. J. Electrostat. 40, 13–20 (1997)
Electrostatic-Discharge-Association: Handbook for the Development of an Electrostatic Discharge Control Program for the Protection of Electronic Parts, Assemblies, and Equipment. TR20.20-2016
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)
Kuhn, M., Johnson, K.: Feature Engineering and Selection: A Practical Approach for Predictive Models. CRC Press, Boca Raton (2019)
Foster, R.C., et al.: Precision and accuracy of an ankle-worn accelerometer-based pedometer in step counting and energy expenditure. Prev. Med. 41(3–4), 778–783 (2005)
Pan, M.-S., Lin, H.-W.: A step counting algorithm for smartphone users: design and implementation. IEEE Sens. J. 15(4), 2296–2305 (2014)
Rhudy, M.B., Mahoney, J.M.: A comprehensive comparison of simple step counting techniques using wrist-and ankle-mounted accelerometer and gyroscope signals. J. Med. Eng. Technol. 42(3), 236–243 (2018)
Do, T.-N., Liu, R., Yuen, C., Tan, U.-X.: Design of an infrastructureless in-door localization device using an IMU sensor. In: 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 2115–2120. IEEE (2015)
Ashkar, R., Romanovas, M., Goridko, V., Schwaab, M., Traechtler, M., Manoli, Y.: A low-cost shoe-mounted inertial navigation system with magnetic disturbance compensation. In: International Conference on Indoor Positioning and Indoor Navigation, pp. 1–10. IEEE (2013)
Mariani, B., Hoskovec, C., Rochat, S., Büla, C., Penders, J., Aminian, K.: 3D gait assessment in young and elderly subjects using foot-worn inertial sensors. J. Biomech. 43(15), 2999–3006 (2010)
Virtanen, P., et al.: SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020)
Sofuwa, O., Nieuwboer, A., Desloovere, K., Willems, A.-M., Chavret, F., Jonkers, I.: Quantitative gait analysis in Parkinson’s disease: comparison with a healthy control group. Arch. Phys. Med. Rehabil. 86(5), 1007–1013 (2005)
Salarian, A., et al.: Gait assessment in Parkinson’s disease: toward an ambulatory system for long-term monitoring. IEEE Trans. Biomed. Eng. 51(8), 1434–1443 (2004)
Pedersen, S.W., Oberg, B., Larsson, L.E., Lindval, B.: Gait analysis, isokinetic muscle strength measurement in patients with Parkinson’s disease. Scand. J. Rehabil. Med. 29(2), 67–74 (1997)
Givon, U., Zeilig, G., Achiron, A.: Gait analysis in multiple sclerosis: characterization of temporal-spatial parameters using GAITRite functional ambulation system. Gait Posture 29(1), 138–142 (2009)
Benedetti, M.G., Piperno, R., Simoncini, L., Bonato, P., Tonini, A., Giannini, S.: Gait abnormalities in minimally impaired multiple sclerosis patients. Multiple Sclerosis J. 5(5), 363–368 (1999)
Guner, S., Inanici, F.: Yoga therapy and ambulatory multiple sclerosis assessment of gait analysis parameters, fatigue and balance. J. Bodyw. Mov. Ther. 19(1), 72–81 (2015)
Buderath, P., et al.: Postural and gait performance in children with attention deficit/hyperactivity disorder. Gait Posture 29(2), 249–254 (2009)
Papadopoulos, N., McGinley, J.L., Bradshaw, J.L., Rinehart, N.J.: An investigation of gait in children with attention deficit hyperactivity disorder: a case controlled study. Psychiatry Res. 218(3), 319–323 (2014)
Leitner, Y., et al.: Gait in attention deficit hyperactivity disorder. J. Neurol. 254(10), 1330–1338 (2007). https://doi.org/10.1007/s00415-006-0522-3
Morris, M.E., Matyas, T.A., Iansek, R., Summers, J.J.: Temporal stability of gait in Parkinson’s disease. Phys. Ther. 76(7), 763–777 (1996)
Trojaniello, D., Ravaschio, A., Hausdorff, J.M., Cereatti, A.: Comparative assessment of different methods for the estimation of gait temporal parameters using a single inertial sensor: application to elderly, post-stroke, Parkinson’s disease and Huntington’s disease subjects. Gait Posture 42(3), 310–316 (2015)
Hori, K., et al.: Inertial measurement unit-based estimation of foot trajectory for clinical gait analysis. Front. Physiol. 10, 1530 (2019)
Hu, X., Huang, Z., Jiang, J., Qu, X.: An inertial sensor based system for real-time biomechanical analysis during running. J. Med. Bioeng. 6(1), 1–5 (2017)
Woyano, F., Lee, S., Park, S.: Evaluation and comparison of performance analysis of indoor inertial navigation system based on foot mounted IMU. In: 2016 18th International Conference on Advanced Communication Technology (ICACT), pp. 792–798. IEEE (2016)
Taborri, J., Palermo, E., Rossi, S., Cappa, P.: Gait partitioning methods: a systematic review. Sensors 16(1), 66 (2016)
Selles, R.W., Formanoy, M.A.G., Bussmann, J.B.J., Janssens, P.J., Stam, H.J.: Automated estimation of initial and terminal contact timing using accelerometers; development and validation in transtibial amputees and controls. IEEE Trans. Neural Syst. Rehabil. Eng. 13(1), 81–88 (2005)
Han, J., Jeon, H.S., Jeon, B.S., Park, K.S.: Gait detection from three dimensional acceleration signals of ankles for the patients with Parkinson’s disease. In: Proceedings of the IEEE The International Special Topic Conference on Information Technology in Biomedicine, Ioannina, Epirus, Greece, vol. 2628 (2006)
Formento, P.C., Acevedo, R., Ghoussayni, S., Ewins, D.: Gait event detection during stair walking using a rate gyroscope. Sensors 14(3), 5470–5485 (2014)
Darwin Gouwanda and Alpha Agape Gopalai: A robust real-time gait event detection using wireless gyroscope and its application on normal and altered gaits. Med. Eng. Phys. 37(2), 219–225 (2015)
Lau, H., Tong, K.: The reliability of using accelerometer and gyroscope for gait event identification on persons with dropped foot. Gait Posture 27(2), 248–257 (2008)
Kotiadis, D., Hermens, H.J., Veltink, P.H.: Inertial gait phase detection for control of a drop foot stimulator: inertial sensing for gait phase detection. Med. Eng. Phys. 32(4), 287–297 (2010)
Agostini, V., Balestra, G., Knaflitz, M.: Segmentation and classification of gait cycles. IEEE Trans. Neural Syst. Rehabil. Eng. 22(5), 946–952 (2013)
Lie, Yu., Zheng, J., Wang, Y., Song, Z., Zhan, E.: Adaptive method for real-time gait phase detection based on ground contact forces. Gait Posture 41(1), 269–275 (2015)
Kim, H., Kang, Y., Valencia, D.R., Kim, D.: An integrated system for gait analysis using FSRs and an IMU. In: 2018 Second IEEE International Conference on Robotic Computing (IRC), pp. 347–351. IEEE (2018)
Lauer, R.T., Smith, B.T., Coiro, D., Betz, R.R., McCarthy, J.: Feasibility of gait event detection using intramuscular electromyography in the child with cerebral palsy. Neuromodulation Technol. Neural Interface 7(3), 205–213 (2004)
Joshi, C.D., Lahiri, U., Thakor, N.V.: Classification of gait phases from lower limb EMG: application to exoskeleton orthosis. In: 2013 IEEE Point-of-Care Healthcare Technologies (PHT), pp. 228–231. IEEE (2013)
Qi, Y., Soh, C.B., Gunawan, E., Low, K.-S., Thomas, R.: Assessment of foot trajectory for human gait phase detection using wireless ultrasonic sensor network. IEEE Trans. Neural Syst. Rehabil. Eng. 24(1), 88–97 (2015)
Galois, L., Girard, D., Martinet, N., Delagoutte, J.P., Mainard, D.: Optoelectronic gait analysis after metatarsophalangeal arthrodesis of the hallux: fifteen cases. Rev. Chir. Orthop. Reparatrice Appar. Mot. 92(1), 52–59 (2006)
OHMITE: FSR series force sensing resistor (2020)
Panebianco, G.P., Bisi, M.C., Stagni, R., Fantozzi, S.: Analysis of the performance of 17 algorithms from a systematic review: influence of sensor position, analysed variable and computational approach in gait timing estimation from IMU measurements. Gait Posture 66, 76–82 (2018)
Catalfamo, P., Ghoussayni, S., Ewins, D.: Gait event detection on level ground and incline walking using a rate gyroscope. Sensors 10(6), 5683–5702 (2010)
McGill, R., Tukey, J.W., Larsen, W.A.: Variations of box plots. Am. Stat. 32(1), 12–16 (1978)
Braun, A., Hamisu, P.: Using the human body field as a medium for natural interaction. In: Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments, pp. 1–7 (2009)
Braun, A., Hamisu, P.: Designing a multi-purpose capacitive proximity sensing input device. In: Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments, pp. 1–8 (2011)
Bian, S., Rey, V.F., Younas, J., Lukowicz, P.: Wrist-worn capacitive sensor for activity and physical collaboration recognition. In: 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pp. 261–266. IEEE (2019)
Bian, S., Rey, V.F., Hevesi, P., Lukowicz, P.: Passive capacitive based approach for full body gym workout recognition and counting. In: 2019 IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 1–10. IEEE (2019)
Bian, S., Yuan, S., Rey, V.F., Lukowicz, P.: Using human body capacitance sensing to monitor leg motion dominated activities with a wrist worn device. In: Activity and Behavior Computing. Springer (2022)
Du, L.: An overview of mobile capacitive touch technologies trends. arXiv preprint arXiv:1612.08227 (2016)
Savage, V., Zhang, X., Hartmann, B.: Midas: fabricating custom capacitive touch sensors to prototype interactive objects. In: Proceedings of the 25th Annual ACM Symposium on User Interface Software and Technology, pp. 579–588 (2012)
Curtis, K., Perme, T.: Capacitive multibutton configurations (2007)
Leeper, A.K.: 14.2: integration of a clear capacitive touch screen with a 1/8-VGA FSTN-LCD to form and LCD-based touchpad. In: SID Symposium Digest of Technical Papers, vol. 33, pp. 187–189. Wiley Online Library (2002)
Baxter, L.K.: Capacitive Sensors: Design and Applications (1997)
AL-Khalidi, F.Q., Saatchi, R., Burke, D., Elphick, H., Tan, S.: Respiration rate monitoring methods: a review. Pediatr. Pulmonol. 46(6), 523–529 (2011)
Wang, H., et al.: Human respiration detection with commodity wifi devices: do user location and body orientation matter? In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 25–36 (2016)
Li, X., Qiao, D., Li, Y., Dai, H.: A novel through-wall respiration detection algorithm using UWB radar. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1013–1016. IEEE (2013)
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Bian, S., Lukowicz, P. (2022). A Systematic Study of the Influence of Various User Specific and Environmental Factors on Wearable Human Body Capacitance Sensing. In: Ur Rehman, M., Zoha, A. (eds) Body Area Networks. Smart IoT and Big Data for Intelligent Health Management. BODYNETS 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 420. Springer, Cham. https://doi.org/10.1007/978-3-030-95593-9_20
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