Abstract
In hospitals and nursing homes, older people usually fall due to weakness and disease. Standing or walking for a long time can be two of the many reasons for falling. One of the better ways for fall prevention is to monitor patient movement. A new kind of batteryless light sensors is providing us with new opportunities for activity prediction, where the inconspicuous nature of such sensors makes them very suitable for monitoring the elderly. In our study, we analyze such sensors known as radio-frequency identification (RFID) tags to predict the movements. We try to study a dataset obtained from 14 healthy old people between 66 and 86 years of age who were asked to wear RFID sensors attached with accelerometers over their clothes and were asked to perform a set of pre-specified activities. This study illustrates that the RFID sensor platform can be successfully used in activity recognition of healthy older people.
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Acknowledgments
The authors would like to thank the faculty members of the Computer Science & Engineering Department of Delhi Technological University for the guidance provided during the course of the research. The authors would also like to thank the University of Adelaide, Australia and UCI for giving us access to such an amazing dataset, and they feel very grateful to have got the opportunity to explore different possibilities where the dataset can help us.
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Shambharkar, P.G., Kansotia, S., Sharma, S., Doja, M.N. (2021). Activity Prediction for Elderly Using Radio-Frequency Identification Sensors. In: Suma, V., Bouhmala, N., Wang, H. (eds) Evolutionary Computing and Mobile Sustainable Networks. Lecture Notes on Data Engineering and Communications Technologies, vol 53. Springer, Singapore. https://doi.org/10.1007/978-981-15-5258-8_15
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DOI: https://doi.org/10.1007/978-981-15-5258-8_15
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