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This research aims to quantify human walking patterns through depth camerasto (1) detect walking pattern changes of a person with and without amotion-restricting device or a walking aid, and to (2) identify distinctwalking patterns from different persons of similar physical attributes.Microsoft Kinect devices, often used for video games, were used to provide andtrack coordinates of 25 different joints of people over time to form a humanskeleton. Then multiple machine learning (ML) models were applied to the SEdatasets from ten college-age subjects - five males and five females. Inparticular, ML models were applied to classify subjects into two categories:normal walking and abnormal walking (i.e. with motion-restricting devices). Thebest ML model (K-nearest neighborhood) was able to predict 97.3% accuracy using10-fold cross-validation. Finally, ML models were applied to classify five gaitconditions: walking normally, walking while wearing the ankle brace, walkingwhile wearing the ACL brace, walking while using a cane, and walking whileusing a walker. The best ML model was again the K-nearest neighborhoodperforming at 98.7% accuracy rate.

Exploratory studies of human gait changes using depth cameras and considering measurement errors