Touhid

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.

Maintaining independence in daily activities and mobility is critical for healthy aging. Older adults who are losing the ability to care for themselves or ambulate are at a high risk of adverse health outcomes and decreased quality of life. It is essential to monitor daily activities and mobility routinely and capture early decline before a clinical symptom arises. Existing solutions use self-reports, or technology-based solutions that depend on cameras or wearables to track daily activities; however, these solutions have different issues (e.g., bias, privacy, burden to carry/recharge them) and do not fit well for seniors. In this study, we discuss a non-invasive, and low-cost wireless sensing-based solution to track the daily activities of low-income older adults. The proposed sensing solution relies on a deep learning-based fine-grained analysis of ambient WiFi signals and it is non-invasive compared to video or wearable-based existing solutions. We deployed this system in real senior housing settings for a week and evaluated its performance. Our initial results show that we can detect a variety of daily activities of the participants with this low-cost system with an accuracy of up to 76.90%.