Developing computational models for personalized ACL injury classification

With the advancement of wearable sensor technology, the use of inertial body sensors in the field of Medicine and Healthcare has increased drastically. Researchers have found that gait data is useful for identifying various motion impairments. Current research in gait analysis is incorporating the features extracted from video data, which is hard to analyze and requires expensive video capture equipment to collect data in slow motion. In this study, we utilized the ability of inertial body sensors to capture gait features of individuals with Anterior Cruciate Ligament (ACL) injury. This study also leverages the causality-based approach to find the coordination between different features of gait data. Gait data during walking, jogging, and running was collected from 131 subjects in which 109 have ACL injury. We then utilized this data to incorporate the gait assessment technique, which uses causality analysis to predict various classes of subjects based on health condition, impacted limb, and impacted limb based on gender. Performance metrics of various machine learning (ML) algorithms were compared to observe the best performing algorithm and used it to evaluate the confidence of individual subjects prediction that aids personalized classification.