Radar-based Gait Classification of Elderly Non-Fallers and Multiple Fallers Using Machine Learning

Abstract

This paper describes the application of micro-Doppler radar (MDR) to gait classification based on fall-risk related gait differences. Elderly non-fallers and multiple fallers were classified using machine-learning of the MDR data. The classification results obtained using the deep learning and gait parameter-based approaches showed that the classification accuracy achieved using a support vector machine with the gait parameters extracted from the MDR signals (Classification rate: 79 %) was better than that resulting from the deep learning of spectrogram images (Classification rate: 73 %).