Musculoskeletal Injury Recovery Assessment using Gait Analysis with Ground Reaction Force Sensor

Abstract

Patients with musculoskeletal injuries are required to exhibit specific tests for clinicians to monitor the recovery progress during the rehabilitation period. An automated system tracking the progress of an injured patient is essential for emerging applications in the healthcare domain. In this study, we propose a long short-term memory cell-based auto-encoder model (LSTM-AE) for the recovery assessment using data collected during walking. A recently published ground reaction force (GRF) dataset of musculoskeletal impairments, GaitRec, is used for this study. The model is trained to learn the gait pattern of healthy individuals. The model is then tested with GRF signals collected during different sessions of rehabilitation from injured patients. The reconstruction losses generated using the GRF signals are assessed to indicate the recovery status of the patient. The results analysis suggests that the reconstruction loss of the LSTM-AE model gradually reduces towards the ending phase of recovery. Similar trend is observed for both legs in all categories of impairments indicating gradual improvement in patients.

Keywords: Musculoskeletal injuries; auto-encoder; gait analysis; ground reaction force; long short-term memory network; recovery prediction.