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Featured Application: The serious game could be used for hand rehabilitation, since it is controlled using five hand movements: wrist extension, wrist flexion, ulnar and radial deviations, and fist movements. Moreover, it has two game modes: a non-tailored mode in which the game difficulty is set randomly, and a tailored mode in which the game difficulty is adjusted according to the participants' range of motion, determined during the hand movements. Background: Traditional physical rehabilitation involves participants performing repetitive body movements with the assistance of physiotherapists. Owing to the exercises' monotonous nature and lack of reward, participants may become disinterested and cease their recovery. Games could be used as tools to engage participants in the rehabilitation process. Consequently, participants could perform rehabilitation exercises while playing the game, receiving rewards from the experience. Maintaining the players' engagement requires regularly adjusting the game difficulty. The players' engagement can be measured using questionnaires and biosignals (e.g., electroencephalography signals—EEG). This study aims to determine whether there is a significant difference in players' engagement between two game modes with different game difficulty adjustments: non-tailored and tailored modes. Methods: We implemented two game modes which were controlled using hand movements. The features of the game rewards (position and size) were changed in the game scene; hence, the game difficulty could be modified. The non-tailored mode set the features of rewards in the game scene randomly. Conversely, the tailored mode set the features of rewards in the game scene based on the participants' range of motion using fuzzy logic. Consequently, the game difficulty was adjusted dynamically. Additionally, engagement was computed from 53 healthy participants in both game modes using two EEG sensors: Bitalino Revolution and Unicorn. Specifically, the theta (θ) and alpha (α) bands from the frontal and parietal lobes were computed from the EEG data. A questionnaire was applied to participants after finishing playing both game modes to collect their impressions on the following: their favorite game mode, the game mode that was the easiest to play, the game mode that was the least frustrating to play, the game mode that was the least boring to play, the game mode that was the most entertaining to play, and the game mode that had the fastest game response time. Results: The non-tailored game mode reported the following means of engagement: 6.297 ± 11.274 using the Unicorn sensor, and 3.616 ± 0.771 using the Bitalino sensor. The tailored game mode reported the following means of engagement: 4.408 ± 6.243 using the Unicorn sensor, and 3.619 ± 0.551 using Bitalino. The non-tailored mode reported the highest mean engagement (6.297) when the Unicorn sensor was used to collect EEG signals. Most participants selected the non-tailored game mode as their favorite, and the most entertaining mode, irrespective of the EEG sensor. Conversely, most participants chose the tailored game mode as the easiest, and the least frustrating mode to play, irrespective of the EEG sensor. Conclusions: A Wilcoxon-Signed-Rank test revealed that there was only a significant difference in engagement between game modes when the EEG signal was collected via the Unicorn sensor (p value = 0.04054). Fisher's exact tests showed significant associations between the game modes (non-tailored, tailored) and the following players' variables: ease of play using the Unicorn sensor (p value = 0.009341), and frustration using Unicorn sensor (p value = 0.0466). [ABSTRACT FROM AUTHOR]

Medical conditions and accidents might cause immobility in certain parts of the body. In order to assist people in the rehabilitation process, sensors obtaining bio-signals from the body have been merged to create assistive technology. This systematic review is focused on presenting the state-of-the-art regarding sensor fusion used in the applications for hand rehabilitation. Articles were searched in four databases: IEEE Xplore, Web of Science, ACM Digital Library, and PubMed. Moreover, PRISMA and QualSyst protocols were employed to filter the articles and assess their qualities. As a result, of the 102 articles initially retrieved, only 29 articles performed sensor fusion for hand rehabilitation. Specifically, three application areas were found: sensor fusion for detecting hand movements, sensor fusion for exoskeleton control applications, and sensor fusion on serious games for hand rehabilitation. The studies included in this review measured two key aspects for rehabilitation purposes: user’s limb strength and user’s limb position. Regarding the sensors employed in the fusion, the most widely used sensor was electromyography sensor followed by the inertial sensors. Furthermore, the studies included in this review have focused on the following hand movements used for rehabilitation: flexion, extension, pronation, supination, power grasp, radial/urnal, open hand, single-finger contractions, multi-finger contractions, pinch grip, hand at rest, and hand grip. [ABSTRACT FROM AUTHOR]