Efficient Fall Detection for a Healthcare Robot System Based on 3-Axis Accelerometer and Depth Sensor Fusion with LSTM Networks

Abstract

Fall detection is one of the most important functions for a healthcare robot system because falls are very dangerous for older people and might lead to death if failed to provide prompt and adequate treatment. In this paper, we propose an efficient fall detection method based on 3-axis accelerometer and depth sensor fusion. LSTM networks are applied to handle temporal information. Simple low-level motion and pose features are obtained from each sensor data, and then fed into the LSTM networks that can learn high-level feature representations to classify falls from other daily life activities. Also, various learning tricks are combined to improve the performance. Experimental results show that the proposed fall detection method outperforms existing methods.