Personalization of Wearable-Based Exergames with Continuous Player Modeling

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  • In recent years, sedentary behavior has been recognized as a major lifestyle-related health risk. Meanwhile, exergames and gamification of physical activities are effective tools to motivate behavior change, particularly to promote daily physical activities. Research has shown that persuasive technologies and gamification can be utilized to increase physical activity. On the other hand, studies have suggested that "one-size-fits-all" approach does not work well for persuasive game design. At the same time, player modeling and recommender systems are increasingly used for personalizing contents and services.However, there are few existing works on how to build comprehensive player models for personalizing gamified systems and recommending daily physical activities, as well as on the long-term effectiveness of gamified exercise-promoting systems. To fill these gaps, a new approach for gamified 24/7 fitness recommendation systems is introduced in this research. It uses wearable activity trackers combined with continuous player modeling to provide personalized activity recommendations and generates gamified contents targeted at individual user.Preliminary results show the feasibility of using wearable activity trackers for gamification of physical activities, as well as the effectiveness of using player modeling for generating personalized exercise recommendations. We show that personalizing recommendations using player modeling and gamification with wearables could improve users' engagement and motivation towards fitness activities over time.

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  • Copyright © 2019 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.

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  • 2019

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