Background: Since the early 2000s, there has been a growing interest in using exercise video games (exergames) and virtual reality (VR)–based interventions as innovative methods to enhance physical rehabilitation for individuals with multiple disabilities. Over the past decade, researchers and exercise professionals have focused on developing specialized immersive exercise video games for various populations, including those who have experienced a stroke, revealing tangible benefits for upper limb rehabilitation. However, it is necessary to develop highly engaging, personalized games that can facilitate the creation of experiences aligned with the preferences, motivations, and challenges communicated by people who have had an episode of stroke. Objective: This study seeks to explore the customization potential of an exergame for individuals who have undergone a stroke, concurrently evaluating its usability as a technological tool in the realm of physical therapy and rehabilitation. Methods: We introduce a playtest methodology to enhance the design of a VR exergame developed using a user-centered approach for upper limb rehabilitation in stroke survivors. Over 4 playtesting sessions, stroke survivors interacted with initial game versions using VR headsets, providing essential feedback for refining game content and mechanics. Additionally, a pilot study involving 10 stroke survivors collected data through VR-related questionnaires to assess game design aspects such as mechanics, assistance, experience, motion sickness, and immersion. Results: The playtest methodology was beneficial for improving the exergame to align with user needs, consistently incorporating their perspectives and achieving noteworthy results. The pilot study revealed that users had a positive response. In the first scenario, a carpenter presents a game based on the flexion-extension movement of the elbow; the second scenario includes a tejo game (a traditional Colombian throwing game) designed around game mechanics related to the flexion-extension movement of the shoulder; and in the third scenario, a farmer challenges the player to perform a movement combining elbow flexion and extension with internal and external rotation of the shoulder. These findings suggest the potential of the studied exergame as a tool for the upper limb rehabilitation of individuals who have experienced a stroke. Conclusions: The inclusion of exergames in rehabilitation for stroke-induced hemiparesis has significantly benefited the recovery process by focusing on essential shoulder and elbow movements. These interactive games play a crucial role in helping users regain mobility and restore practical use of affected limbs. They also serve as valuable data sources for researchers, improving the system’s responsiveness. This iterative approach enhances game design and markedly boosts user satisfaction, suggesting exergames have promising potential as adjunctive elements in traditional therapeutic approaches.

Virtual rehabilitation has been used during decades to provide a more personalized, controlled, and enjoyable experience on upper-limb motor rehabilitation. Since novel virtual reality (VR) technologies are now accessible and highly immersive, the challenge for a wide dissemination of virtual rehabilitation in clinical scenarios has shifted from the hardware robustness to the software intelligence. A sophisticated technique that provides physiological intelligence to novel human-computer interaction (HCI) applications is biocybernetic adaptation. The concept emerges from the electrophysiological computing field, and it proposes using body signals to detect human states (e.g. workload or fatigue) and modulate the virtual activity accordingly. This paper evaluates the effects of using biocybernetic adaptation in a virtual rehabilitation game that aims to encourage users to exert at a desirable intensity level while interacting with the virtual environment. The system relies on surface-electromyography (sEMG) signals to detect fatigue levels in real-time and adapt the game challenge dynamically. Perceived fatigue levels, game user experience, and game performance parameters are assessed after playing the game, considering two different visualization modalities: non-immersive (conventional flat screen) and immersive (VR headset). Results revealed how the biocybernetic system in the immersive condition not only produced lower levels of perceived fatigue compared with the non-immersive, but also, created a more enjoyable and positive experience in a controlled experiment with 24 healthy subjects. Moreover, participants in the immersive condition showed a better performance in the virtual game and higher usability levels scored by users compared with the non-immersive condition. To conclude, we highlight the importance of combining novel immersive approaches with physiologically aware systems to enhance the benefits of virtual rehabilitation therapies.

The increasing prevalence of strokes has led to the search for innovative rehabilitation methods. Immersive virtual reality (VR), especially personalized games, offers an interactive and motivating approach to therapy adherence. The perception and acceptance of physiotherapists are crucial to its implementation and require further investigation. The objective of this research was to evaluate the attitudes and perceptions of physiotherapists regarding the feasibility and effectiveness of a personalized VR game called Motion Health VR for post-stroke rehabilitation. The methodology employed consisted of using three strategies to collect subjective data. First, a multiple-choice questionnaire was administered to 73 physicians and physiatrists during the ISPRM 2023 Conference (International Society of Physical and Rehabilitation Medicine) to collect quantitative data on the utility and feasibility of Motion Health VR. Subsequently, a focus group was conducted with four physiotherapists to obtain qualitative information on the usability, accessibility, and cost-effectiveness of the game. Finally, a feasibility and cost-effectiveness analysis were performed to evaluate the possible long-term benefits and financial implications of implementing Motion Health VR in Colombia. The results obtained were a broad acceptance of VR as a complementary tool in post-stroke rehabilitation and the recognition of personalized games as motivators for patient participation. Physiotherapists highlighted the playability and immersion of the game, although they noted limitations related to costs and spasticity of the patient. The analysis indicated that initial costs, while significant, could be justified by long-term savings and improved patient outcomes. Finally, it is concluded that Motion Health VR demonstrated significant potential to complement post-stroke rehabilitation, receiving positive feedback from physiotherapists. Key challenges include improving access, reducing costs, and providing VR training to optimize rehabilitation outcomes.

The volume of tissue activated (VTA) is a well established approach to model the effects of deep brain stimulation (DBS), and previous studies have pointed to its potential benefits in clinical applications. However, the elevated computational cost of the standard technique for VTA estimation limits its suitability for practical use. In this study we developed a novel methodology to reduce the cost of VTA estimation. Our approach combines multicompartment axon models coupled to the stimulating electric field with a Gaussian emulator. We achieved a remarkable reduction in the average time required for VTA estimation, without the loss of accuracy and other limitations entailed by alternative methods used to attain similar benefits, such as activation threshold curves.

Paper presents the evaluation and cross-validation of four pattern recognition classifiers, with the objective of finding the best one for classify surface electromyography (sEMG) signals combined with information extracted from videogame’s variables. The classifiers, a linear classifier, a quadratic classifier, a k-nearest-neighbor classifier, and a support vector machine, were computed on a data matrix created with the recorder signal collected from 12 subjects in a body interaction videogame that used a sEMG as a control strategy for upper limbs virtual rehabilitation. Although the classifiers of sEMG signal had a widespread study, there is not evidence of how to deal with the information of sEMG signals combined with game variables. The classification task is related with discern each subject as “good player” or “bad player”, looking to following the performance of the videogame’s users through the game sessions. A cross-validation of 10 iterations was computed, PCA and Relieff were used as feature extraction and selection methods. The evaluation was developed using the percentage of accuracy, defined as the well-predicted points. The best accuracy in the classification task was found using an SVM with a misclassification parameter of 400 and an RBF kernel regularization parameter of 60. Base on this result, the SVMs showed to be the appropriate classifier to be used on sEMG signal combined with videogame variables and should be implemented to follow the user’s performance.