# Telehealth Using PoseNet-Based System for In-Home Rehabilitation
## 1 Introduction
In addition, it is also a time-consuming process and patients might experience some discomfort when many markers are attached to the body for long hours during the rehabilitation procedure [13]. Due to the high cost of the marker-based system, a more affordable system using Microsoft Kinect was introduced in 2010 [14; 10; 15; 16; 17; 18; 19; 20; 21; 22; 23]. Most importantly, this system eliminates the need to place markers on the body. After the discontinuation of Microsoft Kinect in the fall of 2017, another skeleton recognition technology, the PoseNet, which uses TensorFlow.js library, emerged for movement detection [15]. Unlike Microsoft Kinect, patients are not required to purchase the sensor or attach markers to their body. PoseNet can detect patient's movement with just an ordinary webcam attached to the laptop. In this paper, a PoseNet-based telehealth system for in-home rehabilitation is introduced. The system produces a set of statistical computations based on the patient's angular movements for comparison and evaluation by the doctors. This enables doctors to conduct in-depth analysis. On top of this, the system offers the potential to fill in the gaps by: (1) using an ordinary webcam with internet connection for rehabilitation exercises; (2) providing access for both patient and doctor to the skeleton results in a timely manner as soon as the patient has completed the rehabilitation exercises; and (3) displaying the percentage of deviation rate of the patient's skeleton results for different days, for the doctor to further analyze the patient's recovery status. Our aim is to assist the patient in performing rehabilitation exercises at home without increasing their financial burden and travelling challenges. This is especially useful for elderly patients with limited mobility. During in-home rehabilitation, the patient needs to stand in front of the laptop webcam to perform the rehabilitation exercises. PoseNet will detect the posture and record the angular movements of both elbows and knees. The patient can obtain the skeleton data after completing the rehabilitation exercises for evaluation. The patient needs to continue with the same rehabilitation exercises for the selected number of days so that the doctors can monitor their rehabilitation postures and examine the deviation rate of the patient's angular movements to determine their recovery rate. They can then receive advice or recommendations from the doctors. As a result, the patient can improve their postural movements based on the doctor's prescriptions. Hence, it is possible to implement an in-home rehabilitation procedure using the PoseNet-based telehealth system, especially for low-income patients in Malaysia. The rest of the paper is organized as follows. First, Section 2 reviews some of the related studies that deliver similar systems. Next, the proposed telehealth system is elaborated in Section 3. The methodology for the functionality test is presented in Section 4. Section 5 clarifies the discussions of the results obtained. Finally, the conclusion is discussed in the last section.