PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

IoT sensing networks for gait velocity measurement

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Gait velocity has been considered the sixth vital sign. It can be used not only to estimate the survival rate of the elderly, but also to predict the tendency of falling. Unfortunately, gait velocity is usually measured on a specially designed walk path, which has to be done at clinics or health institutes. Wearable tracking services using an accelerometer or an inertial measurement unit can measure the velocity for a certain time interval, but not all the time, due to the lack of a sustainable energy source. To tackle the shortcomings of wearable sensors, this work develops a framework to measure gait velocity using distributed tracking services deployed indoors. Two major challenges are tackled in this paper. The first is to minimize the sensing errors caused by thermal noise and overlapping sensing regions. The second is to minimize the data volume to be stored or transmitted. Given numerous errors caused by remote sensing, the framework takes into account the temporal and spatial relationship among tracking services to calibrate the services systematically. Consequently, gait velocity can be measured without wearable sensors and with higher accuracy. The developed method is built on top of WuKong, which is an intelligent IoT middleware, to enable location and temporal-aware data collection. In this work, we present an iterative method to reduce the data volume collected by thermal sensors. The evaluation results show that the file size is up to 25% of that of the JPEG format when the RMSE is limited to 0.5º.
Rocznik
Strony
245--259
Opis fizyczny
Bibliogr. 27 poz., rys., tab., wykr.
Twórcy
  • Graduate Institute of Networking and Multimedia, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan
  • Graduate Institute of Networking and Multimedia, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan
  • Department of Medical Education and Bioethics, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan
  • Department of Family Medicine, National Taiwan University Hospital, No. 1, Changde St., Zhongzheng Dist., Taipei 10048, Taiwan
Bibliografia
  • [1] Administration on Aging (2015). A Profile of Older Americans: 2015, US Department of Health and Human Services, https://books.google.com.tw/books?id=B4hEnQAACAAJ.
  • [2] Alemdar, H. and Ersoy, C. (2010). Wireless sensor networks for healthcare: A survey, Computer Networks 54(15): 2688–2710.
  • [3] Bourke, A.K., Ihlen, E.A., Van de Ven, P., Nelson, J. And Helbostad, J.L. (2016). Video analysis validation of a real-time physical activity detection algorithm based on a single waist mounted tri-axial accelerometer sensor, IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA, pp. 4881–4884.
  • [4] Fortino, G., Galzarano, S., Gravina, R. and Li, W. (2015). A framework for collaborative computing and multi-sensor data fusion in body sensor networks, Information Fusion 22: 50–70.
  • [5] Gheid, Z. and Challal, Y. (2016). Novel efficient and privacy-preserving protocols for sensor-based human activity recognition, 13th International Conference on Ubiquitous Intelligence and Computing (UIC 2016), Toulouse, France.
  • [6] Gravina, R., Ma, C., Pace, P., Aloi, G., Russo, W., Li, W. and Fortino, G. (2017). Cloud-based activity-aaservice cyber-physical framework for human activity monitoring in mobility, Future Generation Computer Systems 75: 158–171.
  • [7] Guo, H. (2011). A simple algorithm for fitting a Gaussian function [DSP tips and tricks], IEEE Signal Processing Magazine 28(5): 134–137.
  • [8] Hsu, C.-Y., Liu, Y., Kabelac, Z., Hristov, R., Katabi, D. and Liu, C. (2017). Extracting gait velocity and stride length from surrounding radio signals, Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, CHI’17, Denver, CO, USA, pp. 2116–2126, DOI: 10.1145/3025453.3025937.
  • [9] Khalajmehrabadi, A., Gatsis, N., Pack, D. and Akopian, D. (2016). A joint indoor WLAN localization and outlier detection scheme using lasso and elastic-net optimization techniques, IEEE Transactions on Mobile Computing PP(99): 1–1.
  • [10] Kon, S.S.-C., Jones, S.E., Schofield, S.J., Banya, W., Dickson, M.J., Canavan, J.L., Nolan, C.M., Haselden, B.M., Polkey, M.I., Cullinan, P. and Man, W.D.-C. (2015). Gait speed and readmission following hospitalisation for acute exacerbations of COPD: A prospective study, Thorax 70(12): 1131–1137, http://thorax.bmj.com/content/early/2015/08/17/thoraxjnl-2015-207046.
  • [11] Kutner, N.G., Zhang, R., Huang, Y. and Painter, P. (2015). Gait speed and mortality, hospitalization, and functional status change among hemodialysis patients: A US renal data system special study, American Journal of Kidney Diseases 66(2): 297–304.
  • [12] Lee, Y.-D. and Chung, W.-Y. (2009). Wireless sensor network based wearable smart shirt for ubiquitous health and activity monitoring, Sensors and Actuators B: Chemical 140(2): 390–395.
  • [13] Lu, C.H. and Fu, L.C. (2009). Robust location-aware activity recognition using wireless sensor network in an attentive home, IEEE Transactions on Automation Science and Engineering 6(4): 598–609.
  • [14] Middleton, A., Fritz, S.L. and Lusardi, M. (2015). Walking speed: The functional vital sign, Journal of Aging and Physical Activity 23(2): 314–322.
  • [15] Milenković, A., Otto, C. and Jovanov, E. (2006). Wireless sensor networks for personal health monitoring: Issues and an implementation, Computer Communications 29(13–14): 2521–2533.
  • [16] Peters, D.M., Middleton, A., Donley, J.W., Blanck, E.L. And Fritz, S.L. (2014). Concurrent validity of walking speed values calculated via the gaitrite electronic walkway and 3 meter walk test in the chronic stroke population, Physiotherapy Theory and Practice 30(3): 183–188, DOI: 10.3109/09593985.2013.845805.
  • [17] Pulignano, G., Del Sindaco, D., Di Lenarda, A., Alunni, G., Senni, M., Tarantini, L., Cioffi, G., Tinti, M., Barbati, G., Minardi, G. and Uguccioni, M. (2016). Incremental value of gait speed in predicting prognosis of older adults with heart failure: Insights from the IMAGE-HF study, JACC Heart Failure 4(4): 289–298.
  • [18] Reijers, N., Lin, K.-J., Wang, Y.-C., Shih, C.-S. and Hsu, J.Y. (2013). Design of an intelligent middleware for flexible sensor configuration in M2M systems, Proceedings of the 2nd International Conference on Sensor Networks (SENSORNETS), Barcelona, Spain, pp. 1–6.
  • [19] Reijers, N. and Shih, C.-S. (2017). Ahead-of-time compilation of stack-based JVM bytecode on resource-constrained devices, Proceedings of 2017 International Conference on Embedded Wireless Systems and Networks (EWSN), Uppsala, Sweden, pp. 1–12.
  • [20] Shih, C.-S. (2016). WuKong Release Document 0.4, https://www.gitbook.com/book/wukongsun/wukong-release-0-4/details.
  • [21] Shih, C.-S., Chou, J.-J., Chuang, C.-C., Wang, T.-Y., Chuang, Z.-Y., Lin, K.-J., Wang, W.-D. and Huang, K.-C. (2017). Collaborative sensing for privacy preserving gait tracking using IoT middleware, 2017 International Conference on Research in Adaptive and Convergent Systems (RACS 2017), Krakow, Poland, pp. 152–159.
  • [22] Studenski, S., Perera, S., Patel, K., Rosano, C., Faulkner, K., Inzitari, M., Brach, J., Chandler, J., Cawthon, P., Connor, E.B., Nevitt, M., Visser, M., Kritchevsky, S., Badinelli, S., Harris, T., Newman, A.B., Cauley, J., Ferrucci, L. and Guralnik, J. (2011). Gait speed and survival in older adults, Journal of the American Medical Association 305(1): 50–58.
  • [23] Studenski, S., Perera, S., Wallace, D., Chandler, J.M., Duncan, P.W., Rooney, E., Fox, M. and Guralnik, J.M. (2003). Physical performance measures in the clinical setting, Journal of the American Geriatrics Society 51(3): 314–322, DOI: 10.1046/j.1532-5415.2003.51104.x.
  • [24] USRP N210 (2019). Software Defined Radio (SDR), Ettus Research, https://www.ettus.com/product/details/UN210-KIT.
  • [25] WuKong (2012). Software repository, https://github.com/wukong-m2m/wukong-darjeeling.
  • [26] Yi, X., Willemson, J. and Nait-Abdesselam, F. (2013). Privacy-preserving wireless medical sensor network, 2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, Melbourne, Australia, pp. 118–125.
  • [27] Zhao, M., Adib, F. and Katabi, D. (2016). Emotion recognition using wireless signals, Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking, MobiCom’16, New York, NY, USA, pp. 95–108, DOI: 10.1145/2973750.2973762.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-025d135d-ed53-418f-be66-a0b59314d9b6
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.