Abstract
The development of human activity monitoring has allowed the creation of multiple applications, among them is the recognition of high falls risk activities of older people for the mitigation of falls occurrences. In this study, we apply a graphical model based classification technique (conditional random field) to evaluate various sliding window based techniques for the real time prediction of activities in older subjects wearing a passive (batteryless) sensor enabled RFID tag. The system achieved maximum overall real time activity prediction accuracy of \(95\,\%\) using a time weighted windowing technique to aggregate contextual information to input sensor data.
This research was supported by a grant from the Hospital Research Foundation (THRF) and the Australian Research Council (DP130104614).
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- 1.
Epoch refers to a group of RFID interrogation cycles.
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© 2014 Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Shinmoto Torres, R.L., Ranasinghe, D.C., Shi, Q. (2014). Evaluation of Wearable Sensor Tag Data Segmentation Approaches for Real Time Activity Classification in Elderly. In: Stojmenovic, I., Cheng, Z., Guo, S. (eds) Mobile and Ubiquitous Systems: Computing, Networking, and Services. MobiQuitous 2013. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 131. Springer, Cham. https://doi.org/10.1007/978-3-319-11569-6_30
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DOI: https://doi.org/10.1007/978-3-319-11569-6_30
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