# Single Camera-Based Remote Physical Therapy: Verification on a Large Video Dataset
## 1 Introduction  
The general concept of remote rehabilitation using motion capture (MoCap) systems has undergone a turbulent change in recent years, as there are several tools for three-dimensional assessments, including sophisticated automation technologies and algorithms, often costing time, expensive equipment and inapplicable inconvenience to the daily practice [1]. Telerehabilitation, or remote physical therapy, is one of the most common types of complex distance medicine that is applied in practice [2]. During recent years, a large number of MoCap systems detecting the pose of a human using a \"marketless\" approach have emerged [3], these systems work without the necessity of placement of any markers on the human body [4].  
This approach reduces the technical and financial requirements and complexity of arrangement [5] and therefore it can be found in the context of distance medicine, not only in specialized clinics but also in the home environment [6].  
Considering the application of distance medicine in the home environment, the most promising systems seem to be systems for the evaluation of body movements from commonly used standard video (RGB) records [7].  
In this case, we only need a regular camera, which is currently integrated into most common electronic devices, such as smartphones or laptops, or smart TVs.  
These systems have great potential for use mainly due to the reduced financial costs of purchasing these systems. These systems have reached such technical levels that they could be used in specific cases as alternatives to costly systems in clinics. However, these systems must also use special camera data processing software [8].  
The most commonly used software tools for pose detection are OpenPose [9], Mask R-CNN [10], Google's Media Pipe [11], Alpha-Pose [12] all available as open source.  
The time to the advent of markerless-based systems using neural networks is described in detail in [PERSON]'s review [13]. All the systems mentioned above were operated only under laboratory conditions or used special HW.  
All these software tools use artificial intelligence methods, namely neural networks (NN) trained on annotated images [14]. The datasets contain general static images of people in undefined positions, according to which NN learns to recognize anatomical points on the body. Existing benchmarks compare the speed and accuracy of detection using the above algorithms based on NN [14]. A shortcoming that limits wider use of low-cost MoCap systems and the mentioned software is the absence of evaluation of the validity of the data provided by these systems. This raises doubts about the use of telemedicine where it is necessary to know relatively accurately the information about the movement of specific anatomical points that physiotherapists need to monitor and modulate the rehabilitation intervention [15]. Thus, the main aim of our study is to determine whether the systems are sufficient to be used for home rehabilitation and under what specific conditions. We focus on the evaluation of the motion capture of different exercise positions and by different camera views, i.e., camera position relative to the subject. To achieve this aim, it is not possible to rely on existing benchmarks, but it is necessary to evaluate the efficiency of software use on video recordings of people moving while exercising. Thus, our study aims to validate the application of markerless systems using only one, generally positioned camera and thus applicable to home telerehabilitation.