Time series techniques for return to play predictions using wearable sensor data
PhD Projects Short Description Injuries are extremely common in any sport. When an athlete is injured, a decision must be made on when they are ready to return to play, which is crucial in managing the risk of re-injury. Wearable sensors such as Inertial Measurement Units (IMU’s) allow the motion characteristics to be captured during motor tasks such as lunging, squatting, walking etc. This is giving rise to an emerging field of ‘digital biomarkers’ where digitally captured biomechanical features are being used to train models to characterize the various stages of recovery and help identify when the athlete is fit to return to normal activity.
In this research we will attempt to develop models that will enable us to identify the potential role that such digital biomarkers could have in the sports injury field. In particular, motion data from functional motor tasks will be used to interrogate the potential role of digital biomarkers in recovery following anterior cruciate ligament injury (ACL) and subsequent corrective surgery.
One of the challenges in implementing this study will be the limited data availability and more particularly the difficulty in obtaining labelled data. Whilst working with wearable sensor data on athletes obtaining labelled data can be difficult due to the time-consuming nature and domain specific knowledge that is required for the labelling. To overcome this limitation, methods such as transfer learning and semi-supervised learning can be investigated. Transfer learning provides some potential to leverage the knowledge from previously trained models either from the same or a different domain. Transfer learning has recently been successfully implemented on images and text but there has been less work in this area for sensor data (time series data) and hence gives scope for novel methods to be developed.
This study aims to investigate new data driven approaches that can be used to aid clinicians in making informed decisions on when an athlete is fit to return to play. It has been planned to use Pre-seasonal digital data (IMU Sensor data) and analog measures (physical measures such as reach distance) from athletes who have sustained ACL injury.