Analysis Tools for Automated Rehabilitation Assessment and Progress Tracking

In this project, Cardon Rehabilitation and Medical Equipment (CRME) and the University of Waterloo (UW) will test the Automated Rehabilitation System (ARS) for lower body physiotherapy in a clinical environment. ARS provides the ability to measure the human pose during the performance of rehabilitation exercises and provide real time feedback. CRME and UW are interested in developing algorithms to automatically assess the changes in patient recovery rate.

Human kinematic optimal control learning and wearable inertial measurement unit alignment for rehabilitation

Physiotherapy is a type of rehabilitation that aims to restore a patient's quality of life after an injury, surgery, or stroke by improving their mobility. Through prescribed exercises and specialized equipment, physiotherapy helps the patient to regain their muscle strength, range of motion, and natural movement. Unfortunately, only rudimentary tools are available to the therapists for assessment and monitoring of patients. Our work focuses on developing wearable technologies that can help therapists with patient assessment and progress tracking.

Development and validation of analysis tools and interfaces for automated rehabilitation systems

Automated physiotherapy motion tracking system may improve clinical outcomes by providing subjective measures and continuous monitoring. A study into different metrics that PTs may find useful for diagnosis and a user interface study assessing the current usability of the Automated Rehabilitation System, a system being developed by Cardon rehabilitation, will be conducted. A method to model the central nervous system using controls will be investigated to see if fatigue can be detected, which is a useful metric to provide both patients and physiotherapists.

Human Motion Inverse Optimal Control Constraint Learning and Inertial Measurement Unit Sensor Design for Rehabilitation

During physiotherapy a continuous assessment and progress tracking of a patientÂ’s performance is of clinical interest. In this project, based on the promising results from the initial prototype, we will redesign the wearable sensors to improve tracking accuracy, communication speed and robustness, incorporate onboard data storage and computation, and minimize cost and size. Furthermore, we will develop automated algorithms for the analysis of the measured data to help physiotherapists identify the causes of changes to the patients' movement profile.