Analysis and evaluation of hemiplegic gait based on wearable sensor network

Abstract

Hemiplegia is a common symptom of acute cerebrovascular disease, and most patients with hemiplegia have abnormal gaits. Descriptive evaluation methods are commonly used in clinical for gait analysis, and outcomes are overly reliant on observation by rehabilitation physicians. The quantitative analysis of hemiplegia gait is urgently required to guide patients' rehabilitation training. This paper presents a quantitative analysis and evaluation method of hemiplegic gait based on inertial measurement units (IMUs). The wearable nodes are worn on the subjects’ waist and lower limbs to record data when they walked in a straight line. After the recorded data has been processed, the gradient descent algorithm (GDA) is used for attitude calculation, and the walking process of hemiplegic patients is reconstructed for gait analysis. Combined with kinematic analysis, three types of joint angles during walking are calculated, i.e., hip angle, knee angle, and ankle angle, and their comparison with the joint angles of normal gait is conducted. In terms of the joint angles, the phase variation of hemiplegic gait is analyzed first, then the gait difference between hemiplegic and normal subjects is measured by using a weighted dynamic time warping (WDTW) algorithm, and finally the gait distortion is evaluated quantitatively based on the WDTW distance. Experimental results demonstrate that the GDA-based gait reconstruction method and the WDTW-based gait evaluation method presented in this paper can quantify the abnormality of hemiplegic gait, and thereby monitor the rehabilitation process of patients' walking ability.