This paper describes an avenue for artificial and computational intelligence techniques applied within games research to be deployed for purposes of physical therapy. We provide an overview of prototypical research focussed on the application of motion sensor input devices and virtual reality equipment for rehabilitation of motor impairment: an issue typical of patients of traumatic brain injuries. We highlight how advances in procedural content generation and player modelling can stimulate development in this area by improving quality of rehabilitation programmes and measuring patient performance.
In many respects the ideas of rehabilitation-driven games share similarities with that of dynamic difficulty adjustment. However, as noted in the likes of @cite_7 , the challenges faced are more nuanced given the physical challenges faced by patients. A notable example can be found in the Intelligence Game Engine for Rehabilitation (IGER) detailed in @cite_11 @cite_15 which is focussed on the recording and management of physiotherapy for stroke patients. This system adopts an adaptive fuzzy-driven approach to catering for the patients rehabilitation process. This is achieved while working with a variety of small mini-games that utilise peripherals such as the Microsoft Kinect and Nintendo Wii Fit board. This system places emphasis on ensuring that the parameters of the current game are such that it should not result in activities that player might find painful.
@cite_15 @cite_7 @cite_11
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Abstract This paper proposes the use of a non-immersive virtual reality rehabilitation system "ReHabgame" developed using Microsoft Kinect™ and the Thalmic™ Labs Myo gesture control armband. The ReHabgame was developed based on two third-person video games that provide a feasible possibility of assessing postural control and functional reach tests. It accurately quantifies specific postural control mechanisms including timed standing balance, functional reach tests using real-time anatomical landmark orientation, joint velocity, and acceleration while end trajectories were calculated using an inverse kinematics algorithm. The game was designed to help patients with neurological impairment to be subjected to physiotherapy activity and practice postures of daily activities. The subjective experience of the ReHabgame was studied through the development of an Engagement Questionnaire (EQ) for qualitative, quantitative and Rasch model. The Monte-Carlo Tree Search (MCTS) and Random object (ROG) generator algorithms were used to adapt the physical and gameplay intensity in the ReHabgame based on the Motor Assessment Scale (MAS) and Hierarchical Scoring System (HSS). Rasch analysis was conducted to assess the psychometric characteristics of the ReHabgame and to identify if these are any misfitting items in the game. Rasch rating scale model (RSM) was used to assess the engagement of players in the ReHabgame and evaluate the effectiveness and attractiveness of the game. The results showed that the scales assessing the rehabilitation process met Rasch expectations of reliability, and unidimensionality. Infit and outfit mean squares values are in the range of (0.68–1.52) for all considered 16 items. The Root Mean Square Residual (RMSR) and the person separation reliability were acceptable. The item person map showed that the persons and items were clustered symmetrically. Computer games are a promising tool to support rehabilitation at home. It is widely recognized that rehabilitation games should (i) be nicely integrated in general-purpose rehabilitation stations, (ii) adhere to the constraints posed by the clinical protocols, (iii) involve movements that are functional to reach the rehabilitation goal, and (iv) adapt to the patients' current status and progress. However, the vast majority of existing rehabilitation games are stand-alone applications (not integrated in a patient station), that rarely adapt to the patients' condition. In this paper, we present the first prototype of the patient rehabilitation station we developed that integrates video games for rehabilitation with methods of computational intelligence both for on-line monitoring the movements' execution during the games and for adapting the gameplay to the patients' status. The station employs a fuzzy system to monitor the exercises execution, on-line, according to the clinical constraints defined by the therapist at configuration time, and to provide direct feedback to the patients. At the same time, it applies real-time adaptation (using the Quest Bayesian adaptive approach) to modify the gameplay according both (i) to the patient current performance and progress and (ii) to the exercise plan specified by the therapist. Finally, we present one of the games available in our patient stations (designed in tight cooperation with therapists) that integrates monitoring functionalities with in-game self-adaptation to provide the best support possible to patients during their routine. Rehabilitation for chronic pain follows a multidisciplinary approach, which despite the effort, often lacks the long term success and patients often fail to translate the skills learned in therapy to every day life. Serious games are hypothesized to support patients to self manage their complaints and keep training their physical functions by themselves, especially, when the game is controlled by the patient's own body performance. In this paper we present the implementation of a system providing multimodal input, including our own full body motion capture system, a low cost motion capture system (Microsoft Kinect) and biosignal acquisition devices to a game engine. In addition, a workflow has been established, that enables the use of the acquired multimodal data for serious games in a medical environment. Finally, a serious game has been implemented, targeting rehabilitation of patients with chronic pain of the lower back and neck. The focus of this work is on the multimodal input and how it is used in a game to support rehabilitation of chronic pain patients. A brief comparison of a marker-based full body MoCap system and Microsoft's Kinect is included. Preliminary results of tests currently underway are provided. Abstract Objective: The aim of this article is to describe a game engine that has all the characteristics needed to support rehabilitation at home. The low-cost tracking devices recently introduced in the entertainment market allow measuring reliably at home, in real time, players' motion with a hands-free approach. Such systems have also become a source of inspiration for researchers working in rehabilitation. Computer games appear suited to guide rehabilitation because of their ability to engage the users. However, commercial videogames and game engines lack the peculiar functionalities required in rehabilitation: Games should be adapted to each patient's functional status, and monitoring the patient's motion is mandatory to avoid maladaptation. Feedback on performance and progression of the exercises should be provided. Lastly, several tracking devices should be considered, according to the patient's pathology and rehabilitation aims. Subjects and Methods: We have analyzed the needs of the clinicians a...