PhysiFi: WiFi Sensing for Monitoring Therapeutic Robotic Systems

Abstract

Patients recovering from limb-impairing strokes require consistent and precise physical therapy (PT) to regain mobility and functionality. Autonomous rehabilitation robots are increasingly adopted during recovery, offering a scalable solution to reduce the burden on physical therapists while assisting patients in performing prescribed exercises accurately. However, the effectiveness of these treatments often relies on professional supervision to ensure patients follow the robot’s movements properly, which could be challenging considering the ongoing shortage of physical therapists. Current PT monitoring systems primarily rely on camera-based technologies, which usually raise concerns due to potential privacy violations and high deployment costs, or wearable devices that are intrusive and uncomfortable for patients. To address these limitations, we propose PhysiFi, a novel approach that leverages ubiquitous WiFi signals available in most indoor environments, such as homes, rehabilitation centers, and assisted living facilities. By analyzing Channel State Information (CSI) from ambient WiFi signals and employing deep learning models, PhysiFi can track and recognize exercises performed by patients with rehabilitation robots. Our experiments demonstrate that PhysiFi can accurately identify prescribed exercises and evaluate whether patients are following the robot’s movements correctly, providing a non-intrusive, privacy-preserving, and costeffective alternative for monitoring physical therapy sessions.