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.

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.

Mental health disorder rates have increased in recent years. In this research, we aim to address the barriers of stigma, accessibility, and affordability in mental healthcare by designing and developing a dialogue system that analyses the mental status of individuals. Additionally, it gives them personalized feedback based on the severity of the mental health problem. We propose a framework based on the concept of a Digital Twin for mental health, which incorporates recent advancements in technology to assess and classify the severity of mental health problems. The chatbot framework was designed in collaboration with a clinical psychiatrist and utilizes pre-trained BERT models, fine-tuned on the E-DAIC dataset, for the detection of various severity levels. The results of this study demonstrate the potential for our method to accurately detect signs of mental health problems with 69% accuracy, and high acceptability and usability with a score of 84.75%.

Human bio-signal fusion is considered a critical technological solution that needs to be advanced to enable modern and secure digital health and well-being applications in the metaverse. To support such efforts, we propose a new data-driven digital twin (DT) system to fuse three human physiological bio-signals: heart rate (HR), breathing rate (BR), and blood oxygen saturation level (SpO2). To accomplish this goal, we design a computer vision technology based on the non-invasive photoplethysmography (PPG) technique to extract raw time-series bio-signal data from facial video frames. Then, we implement machine learning (ML) technology to model and measure the bio-signals. We accurately demonstrate the digital twin capability in the modelling and measuring of three human bio-signals, HR, BR, and SpO2, and achieve strong performance compared to the ground-truth values. This research sets the foundation and the path forward for realizing a holistic human health and well-being DT model for real-world medical applications.

Since the introduction of the ISO/IEEE 11073 Personal Health Device (X73-PHD) Standards as part of ISO/IEEE 11073 family of standards, it has been applied to many health systems developed for personal use. In this systematic literature review (SLR), we review existing literature collected using three databases: Scopus, Pub Med and Web of Science. We propose a classification for personal health systems based on the location in which they are used, the technology used to develop them, and the purpose which is determined by the targeted users. We found 51% of the devices used in such systems are standardized while approximately 40% are not and five systems did not specify the device status (9%). Various adaption techniques were used for standardization. Besides, the pulse oximeter is the most used device in such systems since it was used in 43% of them. In addition, we present the role of the X73-PHD standards in internet of things (IoT) and tele-healthcare systems, we discuss the challenges of utilizing this set of standards in health monitoring systems and we discuss converting the non-standardized devices into standardized. Finally, we propose the requirements of personal health systems based on our review of the literature.