VicoVR-Based Wireless Daily Activity Recognition and Assessment System for Stroke Rehabilitation
Mengxuan Ma, Benjamin J. Meyer, Le Lin, Rachel M. Proffitt, Marjorie Skubic · 2018
Stroke is the leading cause of long-term disability. Stroke patients can recover faster with personalized therapy treatments. This requires both clinical assessments and in-home assessments of daily activities. In this paper, we propose a daily activity recognition and assessment system for stroke patients. Our system is able to classify daily activities in real home environments and quantitatively evaluate upper body motions while preserving privacy by utilizing depth videos. Specifically, our system collects the depth videos and skeletal joint data of daily activities using a VicoVR sensor. It then recognizes and localizes clinically relevant actions from continuous untrimmed depth videos using a customized convolutional de-convolutional network. In addition, it assesses the extent of reach and speed metrics of both hands using skeletal joint data. The system has been tested on simulated cooking videos and real-life cooking videos in various kitchens with different room layouts and light conditions. The action recognition accuracies for simulated and real-life videos can reach 90.9% and 87.5%, respectively. With the valuable assessment feedback of our system, therapists can make better personalized treatments for stroke patients.