Abstract
As the average age of the population increases worldwide, automated tools for remote monitoring of activity are increasingly necessary and valuable. This chapter highlights embedded systems for activity recognition that provide privacy, do not require major infrastructure,and are easy to configure. The strengths and weaknesses of popular sensing modes that include RFID, motion, pressure, acceleration, and machine vision are discussed. A new activity detection system is also described for high privacy area like the bathroomand bedroom environment.
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References
Helal S, Mann W, El-Zabadani et al, The Gator Tech Smart House: A Programmable Pervasive Space, Computer, vol. 38, no. 3, pp. 50–60, (2005)
Sajal K, Das and Diane J, Cook, Designing Smart Environments: A Paradigm Based on Learning and Prediction, Lecture Notes in Computer Science: Pattern Recognition and Machine Intelligence, vol. 3776, pp. 80–90, (2005).
Weiming Hu, Tieniu Tan, LiangWang, and Steve Maybank, A Survey on Visual Surveillance of Object Motion and Behaviors, IEEE Transactions on Systems, Man, and Cybernetics-Part C:Applications and Reviews, vol. 34, no. 3, pp. 334–352, (2004).
G. Diraco, A. Leone, and P. Siciliano, An Active Vision System for Fall Detection and Posture Recognition in Elderly Healthcare, Design, Automation, and Test in Europe Conference and Exhibition, pp. 1536–1541, (2010).
Jezekiel Ben-Arie, Zhiqian Wang, Purvin Pandit, and Shyamsundar Rajaram, Human Activity Recognition Using Multidimensional Indexing, IEEE Transactions On Pattern Analysis And Machine Intelligence, vol. 24, no. 8, pp. 1091–1104, (2002).
RFID Handbook by Klaus Finkenzeller, Second Edition, Published by John Wiley and Sons, Chapter 3, 4, and 9, (2003).
Fishkin, K.P., Philipose, M., Rea, A., Hands-on RFID: wireless wearables for detecting use of objects, Ninth IEEE International Symposium on Wearable Computers, pp. 38–41, (2005)
Joshua R. Smith, Kenneth P. Fishkin, Bing Jiang, Alexander Mamishev, Matthai Philipose, Adam D. Rea, Sumit Roy, and Kishore Sundara-Rajan, RFID-Based Techniques For Human Activity Detection, Communications of the ACM, vol. 48, no. 9, pp.39–44, (2005).
Donald J. Patterson, Dieter Fox, Henry Kautz, and Matthai Philipose, Fine-Grained Activity Recognition by Aggregating Abstract Object Usage, Ninth IEEE International Symposium on Wearable Computing, pp. 44–51, (2005).
Tomohito Inomata, Futoshi Naya, Noriaki Kuwahara, Fumio Hattori, Kiyoshi Kogure, Activity Recognition from Interactions with Objects using Dynamic Bayesian Network, Proceedings of the 3rd ACM International Workshop on Context-Awareness for Self-Managing Systems, pp. 39–42, (2009).
S. Srinivasa Rao, E. G. Rajan, and K. Lalkishore, Human Activity Tracking using RFID Tags,International Journal of Computer Science and Network Security, vol. 9, no. 1, pp. 387–394, (2009).
RFID handbook: application, technology, security, and privacy, Chapter 14, WISP: A Passively Powered UHF RFID Tag with Sensing and Computation, editors, Syed Ahson, and Mohammad Ilyas, CRC Press, (2008).
JianxinWu, Adebola Osuntogun, Tanzeem Choudhury, Matthai Philipose, and James M. Rehg, A Scalable Approach to Activity Recognition based on Object Use, IEEE 11th International Conference on Computer Vision, pp. 1–8, 2007.
Sangho Park and Henry Kautz, Privacy-preserving Recognition of Activities in Daily Living from Multi-view Silhouettes and RFID-based Training, AAAI Fall 2008 Symposium on AI in Eldercare:New Solutions to Old Problems, (2008).
Ann Cavoukian, Angus Fisher, Scott Killen, and David A. Hoffman, Remote home health care tehnologies: how to ensure privacy? Build it in: Privacy by Design, Identity in the Information Society, Springer, published online 22 May, 2010.
Y. Kaddourah, J. King, and A. Helal, Cost-precision tradeoffs in unencumbered floor-based indoor location tracking, Proc.3rd. Int. Conf. Smart Homes Health Telematics, 2005.
Danilo De Rossi, Federico Carpi, Federico Lorussi, Alberto Mazzoldi, Rita Paradiso, Enzo Pasquale Scilingo, and Alessandro Tognetti, Electroactive Fabrics and Wearable Biomonitoring Devices, AUTEX Research Journal, vol. 3, no. 4, pp. 180–185, (2003).
G. Williams, K. Doughty, K. Cameron and D.A. Bradley, A smartfall and activity monitor for telecare applications, Proc. 20th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society, vol. 3, pp. 1151–1154, (1998).
K. Doughty, R. Lewis, and A. McIntosh, The design of a practical and reliable fall detector for community and institutional telecare, J. Telemed. Telecare, vol. 6, pp. 150–154, (2000).
M. Mathie, J. Basilakis, and B.G. Celler, A system for monitoring posture and physical activity using accelerometers, 23rd Annual Int. Conf. IEEE Engineering in Medicine and Biology Society, vol. 4, pp. 3654–3657, (2001).
R. Salleh, D. MacKenzie, M. Mathie, and B.G. Celler, Low power tri-axial ambulatory falls monitor, Proc. 10th Int. Conf. on Biomedical Engineering, (2000).
Henry J. Montoye, Han C. G. Kemper,Wim H. M. Saris, and Richard A.Washburn, Measuring physical activity and energy expenditure, Human Kinetics, pp.72–96, (1996).
Quang Li, John A. Stankovic, Mark A. Hanson, Adam T. Barth, John Lach, and Gang Zhou, Accurate, Fast Fall Detection using Gyroscopes and Accelerometer-Derived Posture Information, Body Sensor Networks, (2009).
Yoshifumi Nishida, Shin’ichi Murakami, Toshio Hori, and Hiroshi Mizoguchi, Minimally Privacy-Violative Human Location Sensor by Ultrasonic Radar Embedded on Ceiling, Proceedings of IEEE Sensors, vol. 1, pp. 433–436, (2004).
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Shah, N., Kapuria, M., Newman, K. (2011). Embedded Activity Monitoring Methods. In: Chen, L., Nugent, C., Biswas, J., Hoey, J. (eds) Activity Recognition in Pervasive Intelligent Environments. Atlantis Ambient and Pervasive Intelligence, vol 4. Atlantis Press. https://doi.org/10.2991/978-94-91216-05-3_13
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