Upper body motion impairment is a common after-effect of a stroke. We are developing a novel multisensory therapy system that makes use of augmented feedback and is expected to enhance rehabilitation outcomes. The first phase of the project involved developing algorithms to automatically differentiate between normal and impaired upper body motions. Seven healthy subjects performed two types scripted actions: (a) elbow flexion and extension and (b) reaching over via elbow flexion and shoulder flexion and adduction. Each action was repeated 10 times as each participant felt most comfortable and also 10 times simulating a common post-stroke impaired motion according to the literature. Principal component analysis was applied to the upper body trajectories during each action, as observed with a Kinect sensor, to extract the dominant modes of motion. Three classification algorithms and up to three motion modes were examined in order to distinguish between normal and impaired motions. A statistical analysis of the Kinect skeletal tracking data vs. manual annotation confirmed a significant bias in the tracking of an elevated shoulder joint. Despite this bias, leave-one-subject-out cross validation experiments confirmed the effectiveness of the proposed methods in detecting impaired motions (accuracy >95%), even when the impairment involved elevating a shoulder. A single, most dominant motion mode provided sufficient information for the binary classification task. The high accuracy rates validate the use of vision-based pose tracking technologies in identifying motion deficiencies.
This work was partially supported through a MITACS NCE Strategic Project post-doctoral fellowship. The authors would also like to thank the Toronto Rehabilitation Institute.
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