The first is based on the use of dynamic time warping (DTW), along with fuzzy logic [7], and the other is based on the recognition of different body segment postures and trajectories [21]. However, the use of a home-based system, using virtual rehabilitation and offering the possibility of communication with a doctor, is more convenient for the patient, and also allows the course of rehabilitation to be altered by adding new exercises, if necessary DTW is, however, difficult to apply when compared to posture estimation algorithms. [PERSON] et al. utilized the recognition of postures together with trajectories, which resulted in an accuracy of posture estimation of 91.9%, and detection of movements of 95.16% [21]. Recent advances in machine learning have led to the use of machine learning algorithms in many studies, including posture classification [22; 23]. The objective of these studies is to classify the sitting postures via conventional algorithms and deep learning-based algorithms using the body pressure distribution data from pressure sensors [22]. After classifying the sitting postures using several classifiers, average and maximum classification rates of 97.20% and 97.94%, respectively, were obtained from nine subjects with a support vector machine using the radial basis function kernel. Through a comparison of the application of the convolutional neural network (CNN) and conventional machine learning algorithms, the effectiveness of an approach [23] wherein the CNN algorithm is applied was shown (average value of accuracy = 0.953). However, machine learning-based algorithms have problems with a computational complexity that lead to an inability of real-time implementation (in reference [22], the authors stressed this point) and the need for resources for training. These examples of previous research in the use of posture recognition algorithms provide strong arguments for the continued research and development of such algorithms. The aim of this research was to develop simpler and more efficient identification algorithms for posture and exercise classification within healthy participants, as well as to evaluate these using Kinect V2. The main contributions of our work can be summarized as follows. Three algorithms for the classification of different postures were developed and evaluated. The effectiveness of these algorithms was based on a total error of vector lengths and a total error of angles, and the multiplication of these two parameters was proved. To compare the effectiveness of classification algorithms, a database was created from the descriptions of the 573 known postures, as well as 903 postures which were not related to them. It was shown that the algorithms presented in this study were demonstrated to be reasonably accurate, and could potentially form the basis for developing a simple system for the remote monitoring of rehabilitation involving exercise therapy. The remainder of this paper is organized as follows. In Section 2, we describe the Microsoft Kinect V2-based approach to the automatic classification of human exercise movement and present three algorithms for posture classifications. In Section 3, we compare the effectiveness of the three developed classification algorithms by means of a database that was created from the descriptions of the 573 known postures and 903 postures which were not correctly performed. In Section 4, we discuss the results and how they can be interpreted from the perspective of previous studies, and of the working hypotheses. Future research directions also are highlighted. Finally, we present the conclusions in Section 5.