Depth Sensor-Based In-Home Daily Activity Recognition and Assessment System for Stroke Rehabilitation

Zoe Moore
Dr. Robert Matthews, Faculty Mentor

Stroke is a leading cause of long-term adult disability. Many stroke patients participate in rehabilitation programs that rely on assessments which limit an occupational therapist’s ability to monitor how patients perform outside of a clinical setting. Our Daily Activity Recognition and Assessment System collects depth and skeletal data passively from within the patient’s home to assess long-term recovery and provide metrics to an occupational therapist to allow for more individualized rehabilitation plans. Using novel data from a wall-mounted depth sensor installed in a patient’s kitchen, we adapt a hierarchical co-occurrence network to identify actions from pre-segmented skeletal data. We then perform assessments on the classified actions to track key recovery metrics and introduce novel filters to identify high quality data for analysis. We achieved 90.1% accuracy by replicating the results of the NTU-RGB-D data set and a maximum of 59.6% accuracy on our kitchen data set.

Keywords: Action Recognition, Stroke Rehabilitation, Depth Data, Skeletal Data

Topic(s):Computer Science

Presentation Type: Oral Presentation

Session: TBA
Location: TBA
Time: TBA