Design and implementaiton of a fall detection system using compressive sensing and shimmer technology

Falls are a major problem in older adults worldwide with an estimated 30% of elderly adults over 65 years of age falling each year. Timely detection of medical emergencies can reduce treatment latency and significantly improve healthcare services. This paper presents the design and implementation of a fall detection system using compressive sensing. The proposed system is built around Shimmer technology and deploys advanced data compression technique using the orthogonal matching pursuit (OMP) algorithm. The detection approach is based on the manipulation of three axial accelerometer data acquired from the Shimmer wireless medical device and reconstructed using the OMP algorithm. The main system building blocks have been simulated and implemented on the Virtex-5 and Zynq 7 field programmable gate array (FPGA) using Vivado high level synthesis tool for system evaluation and IP core generation, area, power and computation time estimation. Results obtained have shown promising results for the detection of falls with different scenarios.