Fall Detection Using FMCW Radar to Reduce Detection Errors for the Elderly

Abstract

Fall accidents pose a significant threat of severe injuries for the elderly, who often need immediate assistance when they fall. Since the use of conventional contact sensors or cameras might be uncomfortable for the user, research on fall detection using non-contact sensors has received considerable attention. While most prior studies have relied heavily on Doppler-based velocity parameters to detect falls, using only Doppler information may lead to erroneous detection of fall-like behavior. As a result, a feature that accounts for additional information is necessary. Addressing this need, this study developed an algorithm for classifying falls by detecting human motions using frequency modulation continuous wave radar, proposing a novel feature to reduce detection errors. The suggested feature was computed using the rangevelocity map of the 2D Fourier transform and evaluated using supervised machine learning techniques, such as support vector machine and linear discriminant analysis, attaining an accuracy higher than 91%.