A low-power fall detection algorithm based on triaxial acceleration and barometric pressure
Changhong Wang, Michael R. Narayanan, Stephen R. Lord, Stephen James Redmond, Nigel Hamilton Lovell · 2014
This paper proposes a low-power fall detection algorithm based on triaxial accelerometry and barometric pressure signals. The algorithm dynamically adjusts the sampling rate of an accelerometer and manages data transmission between sensors and a controller to reduce power consumption. The results of simulation show that the sensitivity and specificity of the proposed fall detection algorithm are both above 96% when applied to a previously collected dataset comprising 20 young actors performing a combination of simulated falls and activities of daily living. This level of performance can be achieved despite a 10.9% reduction in power consumption.