A hardware framework for fall detection using inertial sensors and compressed sensing

Abstract

Falls are the leading cause of sudden and accidental injuries in the elderly. With the aging of the world’s population, one concern of the elderly increase as well. Notably, the rate of fall accidents and hospitalizations related to falls is increasing year after year. Monitoring human activity to detect falls using intelligent portable systems is an efficient and economical solution that enables faster intervention and immediate action. This article proposes a hardware framework for fall detection using accelerometer and gyroscope data implemented on a Zedboard FPGA (Field Programmable Gate Array). Hardware components are designed, tested and simulated using the Xilinx Vivado tool. On this proposed design, the innovation lies in the insertion and integration of the compressed sensing technique (CS) in the fall detection system and human activities to reduce a number of samples and thus reduce energy consumption. To this end, four hardware blocks (compression, recovery, feature extraction and prediction) are designed and tested and validated against the software implementation. In the same system, we implemented the design by exploiting pipeline optimization, achieving very low latency compared to the unoptimized configuration.