The toronto rehab stroke pose dataset to detect compensation during stroke rehabilitation therapy

Abstract

Stroke often leads to upper limb movement impairments. To accommodate new constraints, movement patterns are sometimes altered by stroke survivors to use stronger or unaffected joints and muscles. If used during rehabilitation exercises, however, such compensatory motions may result in ineffective outcomes. A system that can automatically detect compensatory motions would be useful in coaching stroke survivors to use proper positioning. Towards the development of such an automated tool, we present a dataset of clinically relevant motions during robotic rehabilitation exercises. The dataset is captured with a Microsoft Kinect sensor and contains two groups of participants -- 10 healthy and 9 stroke survivors - performing a series of seated motions using an upper-limb rehabilitation robot. Healthy participants performed additional sets of scripted motions to simulate common post-stroke compensatory movements. The dataset also includes common clinical assessment scores. Compensatory motions of both healthy and stroke participants were annotated by two experts and are included in the dataset. We also present a preliminary evaluation of the dataset in terms of its sensitivity and specificity in detecting compensatory movements for selected tasks. This dataset is valuable because it includes clinically relevant motions in a clinical setting using a cost-effective, portable, and convenient sensor.