Vision-based game design and assessment for physical exercise in a robot-assisted rehabilitation system

Date

2018-02-01

Author

Erdogan, Huseyin
Palaska, Yunus
MAŞAZADE, ENGİN
EROL BARKANA, DUYGUN
Ekenel, Hazım Kemal

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

Robot vision, Vision based game design, Physical exercise, Robot assisted rehabilitation system, Gamification applications, Computer vision, Game design, Gesture controls, Robot-assisted rehabilitation system, Self-assessment manikin, Blood volume pulse, BVP, Skin conductance, Skin temperature sensors, Sequential forward selection, Game theory

URI

https://hdl.handle.net/11511/67932

Collections

Graduate School of Informatics, Article