Educational games are often used as teaching and learning tools, with studies showing that game-based learning is widely accepted among children and teenagers. The experience of enjoyment typically associated with playing games provides for a deeper learning experience and allows the individual to connect various concepts, skills, and knowledge, as well as sparking creativity. This paper builds upon previous studies of enjoyment in health-based gaming and aims to articulate a definition of enjoyment in gaming. Drawing on Miles’ taxonomy, the review further set out to identify and bridge gaps in our theoretical understanding of enjoyment. Three theories were found to be particularly relevant for explaining the concept of enjoyment in relation to health-based gaming: self-determination theory, flow theory, and uses and gratification theory.

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This paper will describe the concept of fall detection framework for smart home environment which will focus on elderly people. We also discuss and compared general fall detection system and fall detection framework that been implemented. The study of this paper will also help to get understanding about indoor fall detection techniques, advantages, drawbacks and the challenges to enhance near in the future.

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Most existing recommendation systems (RSs) are primarily concerned about the accuracy of rating prediction and only recommending popular items. However, other non-accuracy metrics such as novelty and diversity should not be overlooked. Existing multi-objective (MO) RSs employed collaborative filtering and combined with evolutionary algorithms to handle bi-objective optimization. Besides cold-start problem from collaborative filtering, it also vulnerable to highly sparse environment, while the evolutionary algorithm suffers from premature convergence and curse of dimensionality. These limitations have prompted this work to propose deep reinforcement learning (DRL) approaches for MO optimization in RSs. Several works in DRL are available but none has addressed MO RS problems. In this study, the performances of proposed DRL approaches that based on Deep Q-Network in MO recommendation problem were investigated. The approaches were evaluated with movie recommendation dataset by using three conflicting metrics, namely precision, novelty, and diversity. The results demonstrated that deep reinforcement learning approaches has superiority performance in MO optimization, and its capability of recommending precise item along with achieving high novelty and diversity against the benchmark that using probabilistic based multiobjective approach based on evolutionary algorithm (PMOEA). Although PMOEA algorithm secured higher average value in precision, it has lower values of novelty and diversity than the proposed DRL approaches. The DRL approaches surpassed the benchmark results in average of maximum novelty and the average of mean diversity metrics, the optimization between accuracy and non-accuracy metrics is inevitable. In addition, the experiments revealed that incorporation of user latent features enhanced the recommendation quality.