Wearable sensor-based remote gait analysis detects altered duty factor and phase specific quadriceps muscle activation in patients recovering from acl reconstruction surgery

This abstract describes an automated, remotely deployable gait analysis technique that leverages machine learning and data from commercially available wearable sensors. The proposed technique is used to analyse gait in daily life for patients recovering from anterior cruciate ligament (ACL) reconstruction surgery (ACLR). Preliminary results suggest that this approach is capable of detecting compensatory gait mechanics post-surgery and their improvement during rehabilitation.