# Biofeedback Applied to Interactive Serious Games to Monitor Frailty in an Elderly Population
## 5 Discussion
Unlike the Kinect technologies [46; 49], where the 3D image is built using a system of depth cameras, or the Virtual Reality system [42; 48], in which the work is based on a constant reading of the position of sensors inside the helmet and controllers in addition to building a volumetric image with the camera system, solutions based on neural networks depend quite heavily on external parameters. These include the quality of lighting, positioning of the player in front of the camera, and quality of the processed image. In this regard, and in order to obtain the most correct data, it is necessary to introduce and observe additional rules of work with systems based on neural networks [43; 51]. Bearing in mind that the main task was to make the platform as accessible and simple as possible, it was decided in this study to take the neural network algorithm as a basis. Consequently, use of the platform allows you to apply the principle of \"install-run-use\". **Medical focus and value to physicians**: The system proposed is designed to help support and rehabilitate predominantly the elderly, although it can also be applied to patients with minor musculoskeletal system problems. The focus of the choice of exercises and their direction is based on the principle of \"a little of everything\", in contrast to the systems aimed at working with a particular part of the body, such as the upper extremities [44; 46; 48] or the lower extremities [42; 50; 51]. In terms of the value of the system to medical staff, the platform acts as a tool for systematizing information, which allows the doctor to monitor progress of the patient's recovery and their overall activity indices more accurately and qualitatively. The main feature is that, unlike some similar systems [43; 48; 50], direct participation by the attending physician in the game process is not required. All the data obtained can be viewed and examined at any time, thanks to the cloud interface where this information is duplicated, as seen, for example, in the solution [43]. **Results and effectiveness**: Given the specific nature of the study presented, it is rather difficult to compare it to the results obtained from similar solutions. Although the effectiveness of the gaming platform increases the patient's outcome from 30% to 50% depending on the level and activity, the value in this case does not show the specific rehabilitative potential of the system. There are several reasons for this. Firstly, while in studies like ours, the main parameters for assessing the effectiveness of the system were precisely the rehabilitation indicators, and it was the result of rehabilitation actions that was compared and evaluated, in our case, the signals into which the system converts the player's performance are researched. Therefore, in this case, it is the indicators of the received signals, such as jitter, shimmer, amplitude-frequency response, and so on, that are important for the study presented here. It is on this comparison that the working principle of the platform presented is based. Secondly, because of the situation regarding the COVID-19 pandemic, it was not possible to conduct tests with the target audience. Bearing in mind that young people (from 21 to 42 years old) took part in the study, the results obtained from them are not entirely accurate, albeit at the same time, proving extremely useful for further research purposes. Thirdly, which follows on from the first point, based on the results obtained, it turned out to be a wrong solution to study such indicators as the Activities of Daily Living (ADLs), Cognitive Function Scale (CFS), Nottingham Health Profile (NHP) [45] score. The reason for this is that it would be incorrect to compare the performance of healthy young people to results from elderly people.