has been gained regarding new treatment regimes for a growing number of chronic diseases and traumas, but with consequential increases in social and economic costs [2]. It is well-known that rehabilitation forms an important part of a typical overall treatment plan, which can be delivered, for instance, by utilizing therapeutic exercise (physiotherapy). The performance of physical activity has many advantages in older people with dementia, and can positively affect the preservation of cognitive abilities [3]. Stroke patents may also benefit from physical activities, which can result in improved recovery rates. However, the success of rehabilitation largely depends on keeping the patient interested and motivated in the continuation of treatment. Factors influencing adherence to the continuation of physical education depend on whether people continue to receive professional assistance and counselling after the completion of the initial training [4]. Among the main reasons for the termination of continued professional assistance and counselling are forgetfulness, a lack of further supervision and motivation, and time restraints (for example: attending the rehabilitation center). The use of exercise therapy delivered remotely using posture recognition and interactive content may have a positive impact on enabling patients to perform exercise, as well as their willingness to continue training and rehabilitation programs [5]. Events such as the recent Covid-19 pandemic reinforce the need for remote exercise therapy with feedback from a doctor, which would be very beneficial for many patients with different disabilities. Traditionally, exercise therapy consists of demonstrating exercises, observation and evaluation by a health professional, which in turn requires special training and significant face-to-face contact with a patient. However, modern computer and sensor technologies could be utilized to augment (or where appropriate, replace) direct intervention by health professionals. Such technologies that can capture specific postures will be able to determine whether or not the exercise regimes provided to the patient are proving the beneficial postural changes over time, with reference to those obtained from healthy adults. With the capabilities of motion capture systems advancing significantly in recent years, and with motion capture systems being more accessible and effective, they allow the kinematics of the human body to be measured and recorded with sufficient accuracy in real time, even using web cameras. Two main types of motion capture systems are widely used: those which use markers, and those which estimate joint and limb segment parameters based on neural network training from marker systems. The first requires use of a special suit, or a removable system of sensors (active or passive markers) attached to the human body. The second type, such as those provided by Microsoft Kinect, Intel RealSense, Structure Core and others, use color and depth data, as well as image recognition algorithms, to retrieve the data. These systems can record kinematic data and perform analysis of the human body's movements in real time. In addition, the development and availability of these sensors opens more opportunities, as it makes it possible to create bespoke courses of rehabilitation, and to monitor their implementation [6; 7; 8; 9; 10; 11]. Similar applications have been developed for different patient groups, but the most widely represented software has been designed for post-stroke patients [12; 13; 14; 15; 16]. Software has also been designed for people with neurological diseases [17], including cerebral palsy [18], multiple sclerosis [19] and traumatic brain injuries [20]. However, the algorithms used by these systems to estimate the accuracy of execution of movements by such patients are not fully described in the literature. Two of those algorithms can, however, be distinguished by their differing mode of operation.