  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.
