Human pose estimation based in-home lower body rehabilitation system

In this paper, we design, develop and evaluate an in-home lower body rehabilitation system based on a novel lightweight human pose estimation model. To achieve that, we first create a lower body rehabilitation dataset of 500,000 images with each image annotated with the ground truth joint point locations. The dataset consists of 31 different types of lower body rehabilitation activities from twenty volunteers. After that, we design a lightweight but powerful neural network model, which runs on a smartphone, to estimate human pose. Furthermore, we develop a series of principles for evaluating in-home rehabilitation activities of patients in terms of the range of motion and duration of activities. For the concern of privacy, all the data collected from patients are encrypted, stored and processed locally on patients’ own smartphones. Only the sanitized evaluation reports are uploaded and shared with the patients’ primary doctors. Our model achieves 70.8 in AP score on the COCO val2017 set with only 4.7M parameters and 1.0 GFLOPs. Using our system, patients can perform lower body rehabilitation activities at home and obtain evaluation report without the presence of physical therapists. We believe our system can greatly facilitate in-home rehabilitation and reduce the cost for patients.