A Study on Gait Quality Assessment for Cerebral Palsy Using Unsupervised Deep Learning Model

Abstract

This study aims to establish a method with ordinary videos (without special equipment) for gait quality assessment. The treatment for Cerebral Palsy, which is a movement disorder, requires gait quality assessment routinely. However, the current assessment methods need expensive equipment and high technical knowledge of rehabilitation. This paper aims to develop a system to evaluate a patient’s gait without special equipment. We propose a method to estimate the gait quality using off-the-shelf human pose estimation and Auto-Encoder. Evaluation experiments using actual patients’ data were conducted to discuss the effectiveness of the proposed method. The correlation coefficient between the proposed method and the typical gait pathological index suggests that the proposed method has enough capability to estimate the patient’s gait abnormality.