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 and of the patients associated in performing rehabilitation at home, identifying the characteristics that the game engine should have. Results: The result of this analysis has led us to develop the Intelligent Game Engine for Rehabilitation (IGER) system, which combines the principles upon which commercial games are designed with the needs of rehabilitation. IGER is heavily based on computational intelligence: Adaptation of the difficulty level of the exercise is carried out through a Bayesian framework from the observation of the patient's success rate. Monitoring is implemented in fuzzy systems and based on rules defined for the exercises by clinicians. Several devices can be attached to IGER through an input abstraction layer, like the Nintendo ^® (Kyoto, Japan) Wii™ Balance Board™, the Microsoft ^® (Redmond, WA) Kinect, the Falcon from Novint Technologies (Albuquerque, NM), or the Tyromotion (Graz, Austria) Timo ^® plate balance board. IGER is complemented with videogames embedded in a specific taxonomy developed to support rehabilitation progression through time. Conclusions: A few games aimed at postural rehabilitation have been designed and developed to test the functionalities of the IGER system. The preliminary results of tests on normal elderly people and patients with the supervision of clinicians have shown that the IGER system indeed does feature the characteristics required to support rehabilitation at home and that it is ready for clinical pilot testing at patients' homes.

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.

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.

The integration of Ambient Assisted Living (AAL) frameworks with Socially Assistive Robots (SARs) has proven useful for monitoring and assisting older adults in their own home. However, the difficulties associated with long-term deployments in real-world complex environments are still highly under-explored. In this work, we first present the MoveCare system, an unobtrusive platform that, through the integration of a SAR into an AAL framework, aimed to monitor, assist and provide social, cognitive, and physical stimulation in the own houses of elders living alone and at risk of falling into frailty. We then focus on the evaluation and analysis of a long-term pilot campaign of more than 300 weeks of usages. We evaluated the system’s acceptability and feasibility through various questionnaires and empirically assessed the impact of the presence of an assistive robot by deploying the systemwithandwithoutit. Our results provide strong empirical evidence that Socially Assistive Robots integrated with monitoring and stimulation platforms can be successfully used for long-term support to older adults. We describe how the robot’s presence significantly incentivised the use of the system, but slightly lowered the system’s overall acceptability. Finally, we emphasise that real-world long-term deployment of SARs introduces a significant technical, organisational, and logistical overhead that should not be neglected nor underestimated in the pursuit of long-term robust systems. We hope that the findings and lessons learned from our work can bring value towards future long-term real-world and widespread use of SARs.

Background: Dementia is a major and growing health problem, and early diagnosis is key to its management. Objective: With the ultimate goal of providing a monitoring tool that could be used to support the screening for cognitive decline, this study aims to develop a supervised, digitized version of 2 neuropsychological tests: Trail Making Test and Bells Test. The system consists of a web app that implements a tablet-based version of the tests and consists of an innovative vocal assistant that acts as the virtual supervisor for the execution of the test. A replay functionality is added to allow inspection of the user’s performance after test completion. Methods: To deploy the system in a nonsupervised environment, extensive functional testing of the platform was conducted, together with a validation of the tablet-based tests. Such validation had the two-fold aim of evaluating system usability and acceptance and investigating the concurrent validity of computerized assessment compared with the corresponding paper-and-pencil counterparts. Results: The results obtained from 83 older adults showed high system acceptance, despite the patients’ low familiarity with technology. The system software was successfully validated. A concurrent validation of the system reported good ability of the digitized tests to retain the same predictive power of the corresponding paper-based tests. Conclusions: Altogether, the positive results pave the way for the deployment of the system to a nonsupervised environment, thus representing a potential efficacious and ecological solution to support clinicians in the identification of early signs of cognitive decline.