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

Abstract

Physical therapy exercises are critically important for the rehabilitation of patients with motor deficits. While these exercises can be most effective when performed properly under the supervision of a physical therapist, it may not be a viable option for all patients. Thus, there is a growing trend towards at-home physical rehabilitation tracking systems as they can be more accessible and flexible for the patients. However, existing systems mostly depend on camera and wearable based solutions which can be costly and limited. To this end, we propose a low-cost and non-intrusive end-to-end solution using IoT based wireless sensing devices. Our solution, Wi-PT-Hand , leverages Channel State Information (CSI) captured from ambient WiFi signals, and uses Bayesian optimizers and a hierarchical deep learning model trained to recognize the prescribed hand exercises. The proposed system includes (i) segmentation of the therapy time into activity and non-activity durations, (ii) recognition of the exercise performed in an activity segment, and (iii) counting of the number of repetitions of the exercise performed within that segment. Extensive experimental results show that the proposed system is robust and performs well in various real life scenarios and thanks to the lightweight design it can work on low-resource edge devices properly.