Automation for individualization of Kinect-based quantitative progressive exercise regimen

The Smart Health paradigm has opened up immense possibilities for designing cyber-physical systems with integrated sensing and analysis for data-driven healthcare decision-making. Clinical motor-rehabilitation has traditionally tended to entail labor-intensive approaches with limited quantitative methods and numerous logistics deployment challenges. We believe such labor-intensive rehabilitation procedures offer a fertile application field for robotics and automation technologies. We seek to concretize this Smart Health paradigm in the context of alleviating knee osteoarthritis (OA). Our long-term goal is the creation, analysis and validation of a low-cost cyber-physical framework for individualized but quantitative motor-rehabilitation. We seek build upon parameterized exercise-protocols, low-cost data-acquisition capabilities of the Kinect sensor and appropriate statistical data-processing to aid individualized-assessment and close the quantitative feedback-loop. Specifically, in this paper, we focus our attention on quantitative evaluation of a clinically-relevant deep-squatting exercise. Data for multiple trials with multiple of squatting motions were captured by Kinect system and examined to aid our individualization goals. Principal Component Analysis (PCA) approaches facilitated both dimension-reduction and filtering of the noisy-data while the K-Nearest Neighbors (K-NN) method was adapted for subject classification. Our preliminary deployment of this approach with 5 subjects achieved 95.6% classification accuracy.