Efficient Compressive Sensing on the Shimmer Platform for Fall Detection

Long-time cycle wireless monitoring of patients with health concerns is highly required. The quality of care, ability of fall detection and prevention is tremendously increased through enabling continous remote human movement monitoring. The aim of this paper is twofold. Firstly, to propose a real-time energy-aware wireless fall detection system based on emerging compresive sensing (CS). Secondly, to define the best way for an efficient deployment of CS-based approach on limited hardware resources sensing platforms. In addition a compression algorithm for high accuracy fall recognition is presented. The CS-based approach is carried out on the low power Shimmer sensing platform, and is aimed to reduce the 3D acceleration data for energy efficiency improvement of the energy-hungry wireless links. The sparsity degree for an efficient representation of 3D acceleration signal and high fall detection accuracy rate is also studied. Interestingly, our results show an average power consumption of less than 38% on the Shimmer Bluetooth link, and the average 3D acceleration data compression rate is about 56%. In addition, an error between the original and reconstruted 3D acceleration signal of 7% after applying CS would yield a space savings of 56% and fall detection accuracy of 96%, for a sparsity S=77 and signal length N=512. Moreover, the proposed energy-aware fall detection system has been proven to distinguish among falls and activities of daily living, and the accuracy has been evaluated in terms of specificity and sensitivity and has shown excellent results.