6LoWPAN-enabled fall detection and health monitoring system with Android smartphone

Yi, Won-Jae; Sarkar, Oishee; Gonnot, Thomas; Monsef, Ehsan; Saniie, Jafar

doi:10.1109/eit.2016.7535234

Cited by 5 publications

(1 citation statement)

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“…Afterwards, Williams introduced an autonomous belt device, which could detect the impact of the shock on the ground, and a mercury tilt switch to detect the lying position of a human body [ 5 ]. However, apart from using accelerometers, there are several different approaches, such as video processing, audio processing, exploitation of gyroscope sensor and processing Wi-Fi signals [ 7 , 8 ] to detect fall events. Compared to visual and acoustic sensors, accelerometers consume less energy and are much easier to integrate into wearable mobility monitoring devices.…”

Section: Related Workmentioning

confidence: 99%

An Energy-Efficient Multi-Tier Architecture for Fall Detection on Smartphones

Güvensan

Kansiz²,

Camgöz

et al. 2017

Sensors

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Automatic detection of fall events is vital to providing fast medical assistance to the causality, particularly when the injury causes loss of consciousness. Optimization of the energy consumption of mobile applications, especially those which run 24/7 in the background, is essential for longer use of smartphones. In order to improve energy-efficiency without compromising on the fall detection performance, we propose a novel 3-tier architecture that combines simple thresholding methods with machine learning algorithms. The proposed method is implemented on a mobile application, called uSurvive, for Android smartphones. It runs as a background service and monitors the activities of a person in daily life and automatically sends a notification to the appropriate authorities and/or user defined contacts when it detects a fall. The performance of the proposed method was evaluated in terms of fall detection performance and energy consumption. Real life performance tests conducted on two different models of smartphone demonstrate that our 3-tier architecture with feature reduction could save up to 62% of energy compared to machine learning only solutions. In addition to this energy saving, the hybrid method has a 93% of accuracy, which is superior to thresholding methods and better than machine learning only solutions.

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Section: Related Workmentioning

confidence: 99%