References
- 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.
- Alemdar, H. and Ersoy, C. (2010). Wireless sensor networks for healthcare: A survey, Computer Networks54(15): 2688–2710.
- 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.
- 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 Fusion22: 50–70.
- 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.
- 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 Systems75: 158–171.
- Guo, H. (2011). A simple algorithm for fitting a Gaussian function [DSP tips and tricks], IEEE Signal Processing Magazine28(5): 134–137.
- 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: 10.1145/3025453.3025937.10.1145/3025453.3025937
- 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 ComputingPP(99): 1–1.
- 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, Thorax70(12): 1131–1137, http://thorax.bmj.com/content/early/2015/08/17/thoraxjnl-2015-207046.
- 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 Diseases66(2): 297–304.
- 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: Chemical140(2): 390–395.
- 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 Engineering6(4): 598–609.
- Middleton, A., Fritz, S.L. and Lusardi, M. (2015). Walking speed: The functional vital sign, Journal of Aging and Physical Activity23(2): 314–322.
- Milenković, A., Otto, C. and Jovanov, E. (2006). Wireless sensor networks for personal health monitoring: Issues and an implementation, Computer Communications29(13–14): 2521–2533.
- 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 Practice30(3): 183–188, DOI: 10.3109/09593985.2013.845805.10.3109/09593985.2013.845805
- 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 Failure4(4): 289–298.
- 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.
- 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.
- Shih, C.-S. (2016). WuKong Release Document 0.4, https://www.gitbook.com/book/wukongsun/wukong-release-0-4/details.
- 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.
- 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 Association305(1): 50–58.
- 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 Society51(3): 314–322, DOI: 10.1046/j.1532-5415.2003.51104.x.10.1046/j.1532-5415.2003.51104.x
- USRP N210 (2019). Software Defined Radio (SDR), Ettus Research, https://www.ettus.com/product/details/UN210-KIT.
- WuKong (2012). Software repository, https://github.com/wukong-m2m/wukong-darjeeling.
- 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.
- 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: 10.1145/2973750.2973762.10.1145/2973750.2973762