Neural Networks for Pathological Gait Classification Using Wearable Motion Sensors

Abstract

Gait, as an essential feature reflecting human health status, has attracted extensive attention in research. Automatic pathological gait identification can contribute to diseases diagnosis and intervention. In this paper, an unobtrusive sensing technology with deep learning methods to discriminate healthy and pathological gaits is proposed. Two accelerometers are mounted on the left and right lower limbs to acquire the motion signals. Based on these signals, three Neural Networks, namely, BPNN (Back Propagation Neural Network), LSTM (Long Short Term Memory) and CNN (Convolutional Neural Networks) are proposed for classifying the gaits. Experimental results exhibit that the accuracy of the proposed method can reach 86%, 81%, and 93% on a database of 15 participants while using BPNN, LSTM, CNN, respectively. With the strong ability of spatial-temporal signal analysis, CNN outperforms the other two neural networks and provides a favorable result. The proposed method can be extended to an automated gait classification tool, which can be used in the diagnosis and identification of pathological gaits.