# Serious Games and Mixed Reality Applications for Healthcare
###### Abstract
It features a real-time tracking system of the body position, relying on the use of a webcam and a built-in motion recognition algorithm based on a neural network, for controlling the game and for analyzing the patient's activity. Acquired data may be subject to medical secrecy rules and will not be available to anyone other than the treating physician and the patient to monitor the outcome and progress of the rehabilitation process for different degrees and types of musculoskeletal disorders. All the data obtained can be visualized and examined at any time, thanks to a cloud interface where all the information is duplicated. [PERSON] et al. [1] propose the use of a serious game for early detection of mild cognitive impairment (MCI), which is an indicative precursor of Alzheimer's disease. More specifically, their work intends to test whether performance data collected during sessions of a serious game, specifically conceived for cognitive assessment and training of elderly people, can be used to create machine learning (ML) models to classify the patient's cognitive status. Investigating the deterioration of cognitive functions is also the goal of the research work by [PERSON] et al. [5]. Their serious game was designed in collaboration with psychologists and clinicians to be as simple as possible, given that the target patients are elderly and that most of them have no experience in playing video games. The aim of the game is to measure the subjects' performance, in terms of the reaction time and accuracy rate in reaching a target, to classify their cognitive abilities and distinguish normal subjects from MCI subjects and subjects with dementia. Traditional sensing devices employed for serious games in rehabilitation include optical and inertial sensors. According to [2], the Microsoft Kinect (a 3D sensor based on time-of-flight technology and the intensity modulation technique [8]) is usually the preferred choice to track full-body movements, although accuracy drops when complex movements are executed. In [PERSON] et al. [3], the main sensor required is a simple USB webcam: this makes the platform more affordable from the financial standpoint, and easy to install and use. One work in this Special Issue by [PERSON] et al. [2] employs two inertial sensors (one for each user foot) to simplify the setup and reduce costs. This serious game is designed to support personalized and multicomponent clinical interventions for older adults, and, more specifically, the goal is to train strength, balance, cognition, and the pelvic floor muscle, in one single session. In this work, reducing the system complexity was preferred over the use of additional sensors for full-body movement evaluation. Recent studies have also integrated biosensors into games designed for physical rehabilitation and for the treatment of cognitive and neurological disorders. Biosensors indeed can be integrated into games to study the biological data of the participants during play as well as making games adaptive based on information extracted from the patient's bio-signals [9]. In this Special Issue, [PERSON] et al. [6] hypothesized the possibility of developing a \"Neurofeedback assisted self-regulation machine\" combining the technical, behavioral, psychological, emotional, and electrophysiological components of brain-computer interfaces (BCIs) based on electroencephalography (EEG), neurofeedback training (NFT), mindfulness meditation, and self-regulation of mental states in a single framework. The work investigates \"how\" a priming intervention right before NFT affects NFT performance and the emotional state, acquired using qualitative emotional state self-reports and quantitative emotional state biomarkers (i.e., galvanic skin response and heart rate variability). The proposed framework could potentially improve the efficacy of self-regulation serious games for therapeutic, performance, or entertainment purposes.