Engagement is a key factor in gaming. Especially, in gamification applications, users' engagement levels have to be assessed in order to determine the usability of the developed games. The authors first present computer vision-based game design for physical exercise. All games are played with gesture controls. The authors conduct user studies in order to evaluate the perception of the games using a game engagement questionnaire. Participants state that the games are interesting and they want to play them again. Next, as a use case, the authors integrate one of these games into a robot-assisted rehabilitation system. The authors perform additional user studies by employing self-assessment manikin to assess the difficulty levels that can range from boredom to excitement. The authors observe that with the increasing difficulty level, users' arousal increases. Additionally, the authors perform psychophysiological signal analysis of the participants during the execution of the game under two distinctive difficulty levels. The authors derive features from the signals obtained from blood volume pulse (BVP), skin conductance, and skin temperature sensors. As a result of analysis of variance and sequential forward selection, the authors find that changes in the temperature and frequency content of BVP provide useful information to estimate the players' engagement.

Task engagement is a key factor in sustaining patients’ participation in rehabilitation. Adaptive task difficulty level adjustment techniques are designed to determine the appropriate exercise difficulty level in where subjects are appropriately challenged and engaged without causing any distress. Such adaptive difficulty adjustment within rehabilitation tasks has the potential to individualise training. In this study, the authors have compared two dynamic difficulty level adjustment algorithms, partially ordered set master (POSM) and increment/decrement one level (IDOL), those change the difficulty levels for each individual adaptively based on his/her performance. These two algorithms are integrated into the robot‐assisted rehabilitation system, RehabRoby, and their functionality is explored via a small user study with 20 healthy subjects. The subjects were asked to perform a computer‐based fruit picker game using RehabRoby under these two algorithms. The impacts of the adaptation algorithms are evaluated in terms of engagement of the subjects by looking at their physiological signals, performance (score), and survey results. Experiments show that although POSM on the average suggests easier difficulty levels to the subjects than IDOL, the subjects experience a wider range of difficulty levels in POSM that may help them to become more engaged in the game, which can also be observed by lower skin temperatures of the subjects.

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