Convergence of Gamification and Machine Learning: A Systematic Literature Review

Recent developments in human–computer interaction technologies raised the attention towards gamification techniques, that can be defined as using game elements in a non-gaming context. Furthermore, advancement in machine learning (ML) methods and its potential to enhance other technologies, resulted in the inception of a new era where ML and gamification are combined. This new direction thrilled us to conduct a systematic literature review in order to investigate the current literature in the field, to explore the convergence of these two technologies, highlighting their influence on one another, and the reported benefits and challenges. The results of the study reflect the various usage of this confluence, mainly in, learning and educational activities, personalizing gamification to the users, behavioral change efforts, adapting the gamification context and optimizing the gamification tasks. Adding to that, data collection for machine learning by gamification technology and teaching machine learning with the help of gamification were identified. Finally, we point out their benefits and challenges towards streamlining future research endeavors.publishedVersio

Exploring Design Opportunities for Technology-Supported Yoga Practices at Home

Department of Human Factors EngineeringYoga is a discipline that integrates mind and bodily exercises practiced for a number of health benefits. Although physical and mental health benefits from practicing yoga are well-known, people address time and cost as the primary barrier to incorporating yoga practices on a regular basis. A cost-effective solution to these limiting factors is adopting at-home practices. However, starting at-home yoga practices is difficult, especially for beginners, due to the lack of feedback on practitioners??? performance. To tackle this challenge, we explore design opportunities for an interactive artifact that can effectively support yoga practices at home that can potentially replace professional personal trainers. Our approach for exploring this design space begins with a user study with a group of yoga practitioners in order to identify design requirements in a yoga practice environment. Based on the results from the user study, we provide some design insights for developing a feedback-based artifact for yoga practice in the home environment. Then, we exemplify how suggested implications can be applied to design with an illustration of a biofeedback-based mat for yoga breathing exercises. Beyond this, we inspect how the mechanism of biofeedback for breathing can be implemented by building a low-cost respiration phase detector to evaluate the quality of breath. The results from the study on the development of phase detector show per-user classifiers can identify respiration phases with mean F-scores of 0.69 for all poses and 0.78 for the baseline pose. This is an acceptable result acknowledging numerous momentary judgments are made to identify each breathing phase. Moreover, per-user classifiers for identifying three yoga poses show promising results, which can expand the application areas of the breathing phase detector. Through this series of context-driven exploratory studies, we demonstrate approaches to investigate design opportunities for technology-supported at-home yoga.clos