Title: Real-time energy-efficient fall detection based on SSR energy efficiency strategy

Authors: Lingmei Ren; Weisong Shi; Zhifeng Yu; Zheng Liu

Addresses: Department of Computer Science and Technology, Tongji University, Shanghai 201804, China ' Department of Computer Science, Wayne State University, Detroit, MI 48202, USA ' Mobihealth Technologies LLC, Oakland, MI 48363, USA ' Huizhou Sanhua Industrial Co., Ltd., Huizhou, Guangdong 518053, China

Abstract: Falling of the elderly has become a growing concern of the community due to the increase of the ageing population and the serious consequences caused by falling. Devising a fall detection system that is not only highly accurate and reliable, but energy efficient and durable is a challenge. In this paper, we proposed an energy efficient fall detection algorithm based on segmented sampling rates. Most of the time, the algorithm uses a low sampling rate to minimise the energy consumption, but a higher sampling rate when a possible fall is sensed. This unique design helps to increase the fall detection accuracy, while reducing the total energy consumption. Results of comprehensive performance evaluation show that the accuracy rate of the proposed fall detection algorithm is 98.33%, meanwhile, the system can save energy by 9.13% comparing to other algorithms running with a high sampling rate without an energy efficient strategy.

Keywords: fall detection; energy efficiency; segmented sampling rate; SSR; accuracy; real-time; elderly; old people; falling over; ageing population; energy consumption.

DOI: 10.1504/IJSNET.2016.076726

International Journal of Sensor Networks, 2016 Vol.20 No.4, pp.243 - 251

Available online: 23 May 2016 *

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