Anomalous Gait Feature Classification From 3-D Motion Capture Data.

Abstract

The gait kinematics of an individual is affected by various factors, including age, anthropometry, gender, and disease. Detecting anomalous gait features aids in the diagnosis and treatment of gait-related diseases. The objective of this study was to develop a machine learning method for automatically classifying five anomalous gait features, i.e., toe-out, genu varum, pes planus, hindfoot valgus, and forward head posture features, from three-dimensional data on gait kinematics. Gait data and gait feature labels of 488 subjects were acquired. The orientations of the human body segments during a gait cycle were mapped to a low-dimensional latent gait vector using a variational autoencoder. A two-layer neural network was trained to classify five gait features using logistic regression and calculate an anomalous gait feature vector (AGFV). The proposed network showed balanced accuracies of 82.8% for a toe-out, 85.9% for hindfoot valgus, 80.2% for pes planus, 73.2% for genu varum, and 92.9% for forward head posture when the AGFV was rounded to the nearest zero or 1. Multiple anomalous gait features were detectable using the proposed method, which has a practical advantage over current gait indices, including the gait deviation index with a single value. The overall results confirmed the feasibility of using the proposed method for screening subjects with anomalous gait features using three-dimensional motion capture data.