Stroke patient daily activity observation system

Stroke is a leading cause of long-term adult disability. Stroke patients can recover through rehabilitation programs prescribed by occupational therapists (OT); however, an individualized rehabilitation program can reduce recovery times compared to traditional ones. In this paper, we propose a daily activity observation system (DAOS) that uses a Kinect v2 sensor to collect and retrieve motion data. The DAOS has a robust interface to extract depth and skeleton data, and supports data collection in an unstructured kitchen environment. Depth data are used to perform action recognition and track problematic movements, while skeleton data are used to calculate mean velocities of hand joints, max extensions, symmetry of hand movements, and other assessment metrics for therapists. Histogram of oriented 4D normals is used for action recognition. The action recognition accuracy is 97% on a multi-class kitchen action dataset. Through action recognition and accurate assessment, we present a novel system that can assist therapists and their ability to provide quality care to stroke patients.