# Applying Vision-Based Pose Estimation in a Telerehabilitation Application
## 1 Introduction
Augmented reality (AR) is the term used to describe the set of technologies that allow a user to visualize part of the real world through a technological device with graphic information added by it. The device, or set of devices, adds virtual information to the already existing physical information (that is, a virtual part appears in reality). In this way, tangible physical elements are combined with virtual elements, thus creating an augmented reality in real time. These technologies can be used in different formats and make use of various devices, from mobile phones to complex sensors [25]. However, most low-cost exergames, such as those using smartphones, promote healthy lifestyles more than specific exercises [26]. There are other research works more in line with the proposal of this paper, in which augmented reality mirrors are applied. Sleeve AR [17] provides a platform for exercising the upper extremities, where the patient is shown a real-time guide to exercise by means of AR information projected on the patient's arm and on the floor. Physio@Home [27] displays visual objects such as arrows and arm traces to guide patients through exercises and movements so that they can perform physical therapy exercises at home without the presence of a therapist. Finally, the NeuroR [28] system applies an augmented reality mirror to exercise the upper limbs. The system overlays a virtual arm on top of the body of the patient and uses an electromyography device to control its movement. The problem with these approaches and proposals is that they need sensors, devices and technologies that are not easily accessible in a middle-class home. In other proposals [29; 30; 31], mobile devices are used, such as smartphones or tablets, but most of them are intended as exergames to carry out activities abroad, not thinking about telerehabilitation activities. Therefore, we think that the alternative is the development of exergames that allow the monitoring of patient movements, especially indoors and also as accessible as possible. The most accessible alternative is to be able to estimate a human pose using a single RGB camera. The current advance in artificial vision techniques and learning methods based on the convolutional neural network (CNN) has led to an improvement in the performance of the estimation of the human pose and therefore growing interest in it. According to the different dimensions of the human pose result, existing works could be classified into 2D pose detection. To achieve this goal, there are several 2D pose detection libraries [32]. Among the most popular bottom-up approaches to estimate the human pose of various people, several open-source models and deep-learning uses stand out, among which we highlight the OpenPose library [33], DensePose [34] and ImageRecognition software wrnchAI [35]. As with many bottom-up approaches, OpenPose first detects parts (key points) that belong to each person in the image, and then assigns parts to different individuals. WrnchAI is a high-performance deep-learning runtime engine, engineered and engineered to extract human motion and behavior from standard video. Dense Pose proposes DensePose-RCNN, which is a variant of Mask-RCNN, which can densely return the UV coordinates of specific parts in each region of the human body at multiple frames per second. In [36], a recent comparison of the alternatives is presented, where both cover the needs and requirements of ExerCam. After testing the proposals, we have finally decided to use OpenPose in our work, given its ease of integration with our project. What made us opt for OpenPose especially is that OpenPose is an open-source library, with a free license compared to wrnchAI that counts with an annual license of USD 5000. In this paper, an application of augmented reality mirror type based on 2D body pose detection called ExerCam is presented. ExerCam superimposes augmented information in real time on the video.