Background: Post-stroke recovery benefits from structured, intense, challenging, and repetitive therapy. Exergames have emerged as promising to achieve sustained therapy practice and patient motivation. This study assessed the usability and effects of exergames on balance and gait. Subjects and Methods: Sixteen elderly participants were provided with the study intervention based on five newly developed exergames. The participants were required to attend 36 training sessions; lasting for 20 minutes each. Adherence, attrition and acceptance were assessed together with (1) Berg Balance Scale, (2) 7-m Timed Up and Go, (3) Short Physical Performance Battery, (4) force platform stance tests, and (5) gait analysis. Results: Thirteen participants completed the study (18.8 percent attrition), without missing a single training session (100 percent adherence). Participants showed high acceptance of the intervention. Only minor adaptations in the program were needed based on the users' feedback. No changes in center of pressure area during quiet stance on both stable and unstable surfaces and no changes of walking parameters were detected. Scores for the Berg Balance Scale (P = 0.007; r = 0.51), the 7-m Timed Up and Go (P = 0.002; r = 0.56), and the Short Physical Performance Battery (P = 0.013; r = 0.48) increased significantly with moderate to large effect sizes. Conclusion: Participants evaluated the usability of the virtual reality training intervention positively. Results indicate that the intervention improves gait-and balance-related physical performance measures in untrained elderly. The present results warrant a clinical explorative study investigating the usability and effectiveness of the exergame-based program in stroke patients.
Abstract-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.
We show here how integrating novel natural user interfaces, like Microsoft Kinect, with a fully adappatient's current statustive game engine, a system that can be used for rehabilitation at home can be built. A wide variety of game scenarios, a balanced scoring system, quantitative and qualitative exercise evaluation, automatic gameplay level adaptation to patient's current status, and audiovisual feed-back are all implemented inside the Intelligent Game Engine for Rehabilitation here introduced, and are aimed at maximizing patient's motivation and rehabilitation effectiveness. The system is integrated into a multi-level platform that provides continuous monitoring by the hospital and it has been developed inside the framework of the EU funded Rewire project
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