Exploratory studies of human gait changes using depth cameras and considering measurement errors
It addresses gait analysis for healthcare or rehabilitation applications, but is incremental as it applies existing methods to a new dataset.
This research used depth cameras and machine learning to quantify human walking patterns, achieving 97.3% accuracy in classifying normal vs. abnormal walking and 98.7% accuracy in distinguishing five specific gait conditions.
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. 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.