Abstract
A real time pervasive fall detection system is a very important tool that would assist health care professionals in the event of falls of monitored elderly people, the demography among which fall is the epidemic cause of injuries and deaths. In this work, Gradient, a user centric and device friendly standalone smartphone based fall detection solution is proposed. Our solution is standalone and user centric as it is portable, cost efficient, user friendly, privacy preserving, and requires only technologies which exists in cellphones. In addition, Gradient is light weight which makes it device friendly since cellphones are constrained by energy and memory limitations. Our work is based on accelerometer sensor data and the data derived from gravity sensors, a recently available inbuilt sensor in smartphones. Through experimentation, we demonstrate that Gradient exhibits superior accuracy among other fall detection solutions.
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Bhatia, A., Kumar, S., Mago, V.K. (2015). Gradient: A User-Centric Lightweight Smartphone Based Standalone Fall Detection System. In: Pereira, F., Machado, P., Costa, E., Cardoso, A. (eds) Progress in Artificial Intelligence. EPIA 2015. Lecture Notes in Computer Science(), vol 9273. Springer, Cham. https://doi.org/10.1007/978-3-319-23485-4_7
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DOI: https://doi.org/10.1007/978-3-319-23485-4_7
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