The long-term goal of this project is to develop a commercial system for evaluation of physical therapy and rehabilitation exercises performed by patients in a home-based setting. The system will employ a machine learning approach for evaluation of patient movements, based on input data related to the trajectories of the body parts of a patient captured with a Kinect vision sensor.
The objective is to develop the necessary methodology for mathematical modeling and evaluation of physical therapy movements. The three specific aims of this project are:
- Create the first comprehensive dataset of human movements related to physical therapy;
- Develop a novel methodology for mathematical modeling of therapy movements based on deep neural networks; and
- Define a set of metrics for therapy performance evaluation.
The academic novelty of the research is related to utilizing deep artificial neural networks for mathematical modeling of therapy movement at multiple levels of abstraction. This will be achieved by employing a network architecture with multiple layers of convolutional computational units for encoding data patterns at different levels of abstraction, combined with layers of recurrent computational units for encoding the temporal correlations of the extracted data features. Such mathematical models furnish a potential for robust algorithmic evaluation of movement sequences performed by patients with respect to a prescribed model of the movements. The project team will also create a comprehensive dataset of therapy movements recorded with multiple sensory systems, which will be made available to the public, and can serve as a benchmark for similar future research.
The significance of the project is in proposing a cost-effective tool to benefit millions of patients undertaking physical therapy by continuously monitoring their progress toward recovery, encouraging their treatment plan compliance, and stimulating their engagement in the rehabilitation effort.