# Telehealth Using PoseNet-Based System for In-Home Rehabilitation
## 2 Related Work
The paper in [24] measured and recordedthe patients' angular movement during game play. After the patients have completed the games, an angular movement graph will be generated, enabling the physiotherapist to monitor whether the patients achieve the accuracy of the required movement. The aforementioned papers use Kinect sensors as the input device to detect the skeleton movements of the patients in their rehabilitation systems. The patients need to purchase a Kinect sensor to use these systems. Alternatively, [PERSON] et al. [25] developed a rehabilitation system, OpenPose, which uses a computer vision approach to detect the keypoints and skeleton movements of the patients. This has created an opportunity for in-home rehabilitation system using only a laptop or mobile camera, eliminating the need to purchase any additional device or sensor. When the patients perform rehabilitation exercises, they only need to stand in front of their laptop or mobile camera to allow OpenPose to detect their skeleton movements. After the patients have completed the rehabilitation exercises, the angles are computed between the body parts using the captured data of the patients' skeleton keypoints. An angular movement graph will be generated for the physiotherapist to perform manual assessment by comparing with the angular movements performed by the professional trainer to monitor the accuracy of the patients' movements. Despite its lower cost, manually examining the patients' posture through an angular movements graph may be tedious and susceptible to fatigue error. On the other hand, [PERSON] et al. [26] proposed a PoseNet-based rehabilitation system to assist patients who undergo physiotherapy exercises. This system utilizes the PoseNet to detect and extract the skeleton keypoints of patients from the 30-s live exercise video. A 3D avatar performs the exercise to guide the patient in the correct postures. After recording, the PoseNet model will extract the patient's skeleton keypoints. The score is then calculated by using the DTW approach to measure the similarity between the keypoints of the patients and the benchmark keypoints performed by the experts. Finally, the patient's score (0-100%) will be provided based on all the exercise movements along with a short feedback statement to advice on incorrect performance, such as, \"Work on your left elbow\", \"Work on your right wrist\" and \"Good job\". In summary, the existing in-home rehabilitation systems come with a few problems as follows: (1) A special-purpose hardware such as Kinect is required; (2) There is no automated comparison of the angular movements performed by patients on different days; (3) There is no visualization result and automated statistical computation to assist in the analysis and evaluation process of the patient's performance. Hence, a telehealth system using PoseNet-based system for in-home rehabilitation is proposed with the following advantages: (1) Using PoseNet with an ordinary webcam to detect the patients' angular movements incurs no additional expenditure on special-purpose equipment; (2) An automated comparison of the patients' angular movements on different days is built into the system; (3) A visualization result and statistical variables such as mean, standard deviation and the percentage deviation rate of the patients' angular movements are automatically calculated so that the doctors can analyze the patients' recovery status directly with more in-depth analysis.