Wi-PT: Wireless Sensing based Low-cost Physical Rehabilitation Tracking

Abstract

Physical therapy (PT) exercises are critically important for the rehabilitation of patients with motor deficits. While rehabilitation exercises can be most effective when performed properly under the supervision of a physical therapist, it can be costly in terms of several aspects and may not be a viable option for all patients. At-home systems offer more accessible and less costly solutions to patients while also providing flexibility in scheduling prescribed exercises. However, current systems mostly depend on camera based solutions that have limitations (i.e., deployment cost, requiring patients to be in the sight of camera, potential privacy violations) or wearable solutions that are cumbersome and intrusive. To this end, in this paper, our goal is to leverage the WiFi infrastructure available in most indoor locations (i.e., homes, apartments, nursing homes, etc.) for tracking the exercises prescribed to patients during their rehabilitation. Our solution, Wi-PT, is based on the analysis of Channel State Information (CSI) captured from ambient WiFi signals, and uses deep learning models trained to recognize the prescribed physical therapy exercises. Through our experiments, we show that the proposed solution can successfully recognize different types of physical therapy exercises such as hand and finger movements, limb movements and movements performed with exercise equipment. Moreover, we show that our system can recognize the person performing different activities and can identify when they are at rest or actively performing an exercise.