Exploratory studies of human gait changes using depth cameras and sample entropy

Abstract

This research aims to quantify human walking patterns through depth cameras to (1) detect walking pattern changes of a person with and without a motion-restricting device or a walking aid, and to (2) identify distinct walking patterns from different persons of similar physical attributes. Microsoft Kinect™ devices, often used for video games, were used to provide and track coordinates of 25 different joints of people over time to form a human skeleton. Two main studies were conducted. The first study aims at deciding whether motion-restricted devices such as a knee brace, an ankle brace, or walking aids – walkers or canes affect a person’s walking pattern or not. This study collects gait data from ten healthy subjects consisting of five females and five males walking a 10-foot path multiple times with and without motion-restricting devices. Their walking patterns were recorded in a form of time series via two Microsoft Kinect™ devices through frontal and sagittal planes. Two types of statistics were generated for analytic purposes. The first type is gait parameters converted from Microsoft Kinect™ coordinates of six selected joints. Then Sample Entropy (SE) measures were computed from the gait parameter values over time. The second method, on the other hand, applies the SE computations directly on the raw data derived from Microsoft Kinect™ devices in terms of (X, Y, Z) coordinates of 15 selected joints over time. The SE values were then used to compare the changes in each joint with and without motion-restricting devices. The experimental results show that both types of statistics are capable of detecting differences in walking patterns with and without motion-restricting devices for all ten subjects. The second study focuses on distinguishing two healthy persons with similar physical conditions. SE values from three gait parameters were used to distinguish one person from another via their walking patterns. The experimental results show that the proposed method using a star glyph summarizing the shape produced by the gait parameters is capable of distinguishing these two persons. Then multiple machine learning (ML) models were applied to the SE datasets from ten college-age subjects - five males and five females. In particular, ML models were applied to classify subjects into two categories: normal walking and abnormal walking (i.e. with motion-restricting devices). The best ML model (K-nearest neighborhood) was able to predict 97.3% accuracy using 10-fold cross-validation. Finally, ML models were applied to classify five gait conditions: walking normally, walking while wearing the ankle brace, walking while wearing the ACL brace, walking while using a cane, and walking while using a walker. The best ML model was again the K-nearest neighborhood performing at 98.7% accuracy rate.