Serious games are growing rapidly as a gaming industry as well as a field of academic research. There are many surveys in the field of digital serious games; however, most surveys are specific to a particular area such as education or health. So far, there has been little work done to survey digital serious games in general, which is the main goal of this paper. Hence, we discuss relevant work on serious games in different application areas including education, well-being, advertisement, cultural heritage, interpersonal communication, and health care. We also propose a taxonomy for digital serious games, and we suggest a classification of reviewed serious games applications from the literature against the defined taxonomy. Finally, the paper provides guidelines, drawn from the literature, for the design and development of successful serious games, as well as discussing research perspectives in this domain.

The use of the digital twin has been quickly adopted in industry in recent years and continues to gain momentum. The recent redefinition of the digital twin from the digital replica of a physical asset to the replica of a living or nonliving entity has increased its potential. The digital twin not only disrupts industrial processes, but also expands the domain of health and well-being towards fostering smart healthcare services in smart cities. In this paper, we propose an ISO/IEEE 11073 standardized digital twin framework architecture for health and well-being. This framework encompasses the process of data collection from personal health devices, the analysis of this data, and conveying the feedback to the user in a loop cycle. The framework proposes a solution to include not only X73 compliant devices, but also noncompliant health devices, by interfacing them with an X73 wrapper module as we explain in this paper. Besides, we propose a configurable X73 mobile application, designed to work with any X73 compliant device. We designed and implemented the proposed framework, and the X73 mobile app, and conducted an experiment as a proof of concept of the digital twin in the domain of health and well-being in smart cities. The experiment shows promising results and the potential of benefiting from the proposed framework, by gaining insights on the health and well-being of individuals, and providing feedback to the individual and caregiver.

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Executive function and motor control deficits adversely affect gait performance with age, but the neural correlates underlying this interaction during stair climbing remains unclear. Twenty older adults (72.7 ± 6.9 years) completed single tasks: standing and responding to a response time task (SC), ascending or descending stairs (SMup, SMdown); and a dual-task: responding while ascending or descending stairs (DTup, DTdown). Prefrontal hemodynamic response changes (∆HbO2, ∆HbR) were examined using functional near-infrared spectroscopy (fNIRS), gait speed was measured using in-shoe smart insoles, and vocal response time and accuracy were recorded. Findings revealed increased ∆HbO2 (p = 0.020) and slower response times (p < 0.001) during dual- versus single tasks. ∆HbR (p = 0.549), accuracy (p = 0.135) and gait speed (p = 0.475) were not significantly different between tasks or stair climbing conditions. ∆HbO2 and response time findings suggest that executive processes are less efficient during dual-tasks. These findings, in addition to gait speed and accuracy maintenance, may provide insights into the neural changes that precede performance declines. To capture the subtle differences between stair ascent and descent and extend our understanding of the neural correlates of stair climbing in older adults, future studies should examine more difficult cognitive tasks.

Digital Twin technology has been rising in popularity thanks to the popularity of machine learning in the last decade. As the life expectancy of people around the world is increasing, so is the focus on physical activity to remain healthy especially in the current times where people are staying sedentary while in quarantine. This article aims to provide a survey on the field of Digital Twin technology focusing on machine learning and coaching techniques as they have not been explored yet. We also define what Digital Twin Coaching is and categorize the work done so far in terms of the objective of the physical activity. We also list common Digital Twin Coaching characteristics found in the articles reviewed in terms of concepts such as interactivity, privacy and security and also detail future perspectives in multimodal interaction and standardization, to name a few, that can guide researchers if they choose to work in this field. Finally, we provide a diagram for the Digital Twin Ecosystem showing the interaction between relevant entities and the information flow as well as an idealization of an ideal Digital Twin Ecosystem for team sports’ athlete tracking.

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