Algorithms used in the articles reviewed.

Algorithms used in the articles reviewed.

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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 pr...

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... we can see in Table 3, an SVM was the most used model and widely applied in different fields. In the rehabilitation field, an SVM was commonly adopted in analyzing signals inside the human body. ...

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There is a growing body of research on positive tactile communication and its impact on athlete performance and team dynamics. The purpose of the present study was to examine the profile and perceived impact of positive tactile communication as a coaching strategy in a high-performance team sport setting. Participants were members of a successful A...
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Globally, COVID-19-related movement restrictions have caused significant disruption to athlete’s training and sporting competitions. ‘Quarantine’ camps are one approach to maintain sport-specific training, whilst minimising the risk of COVID-19 transmission between athletes and society. This cross-sectional study investigated the effects of a ‘quar...

Citations

... Gamez Diaz et al., 2020 [54] The DT ecosystem shows the interactions and flow of information and the concept of a DT ecosystem for tracking team sports athletes. ...
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Artificial intelligence (AI)-based digital patient twins have the potential to make breakthroughs in research and clinical practices in rehabilitation. They make it possible to personalise treatment plans by simulating different rehabilitation scenarios and predicting patient-specific outcomes. DTs can continuously monitor a patient’s progress, adjusting therapy in real time to optimise recovery. They also facilitate remote rehabilitation by providing virtual models that therapists can use to guide patients without having to be physically present. Digital twins (DTs) can help identify potential complications or failures at an early stage, enabling proactive interventions. They also support the training of rehabilitation professionals by offering realistic simulations of different patient conditions. They can also increase patient engagement by visualising progress and potential future outcomes, motivating adherence to therapy. They enable the integration of multidisciplinary care, providing a common platform for different professionals to collaborate and improve rehabilitation strategies. The article aims to trace the current state of knowledge, research priorities, and research gaps in order to properly guide further research and shape decision support in rehabilitation.
... Finally, "Digital Twin Coaching for Physical Activities: A Survey" [14] provides a comprehensive review of digital twin technology in the context of physical activity coaching. Highlighting current research, interactivity, and privacy concerns, this survey sets the stage for future developments in digital coaching, underlining the significance of DTs in promoting health and wellness. ...
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The Internet of Things (IoT) stands as one of the most transformative technologies of our era, significantly enhancing the living conditions and operational efficiencies across various domains [...]
... No. Focus Note [29] '20 General 74** Pose estimation 2D [30] '21 General 81** Pose estimation 3D, Markerless [31] '21 General 150** Pose estimation 3D, Deep learning-based [32] '21 General 124** Pose estimation Deep learning-based [33] '23 General 361** Pose estimation Deep learning-based [34] '23 General 206** Pose estimation Deep learning-based, 2D [5] ' 21 Training assistance 8 Pose estimation [6] ' 21 Human Health and Performance 53* Pose estimation Assessment as a part of improvement [7] '21 Sports and physical exercise 20 Pose estimation Camera-based [28] '22 Rehabilitation 62** Pose estimation Computer vision and IMU-based [35] '20 Motor learning 72** Movement assessment Machine learning-based [36] '20 Sports training 109 Movement assessment Intelligent data analysis methods [37] ' 22 Rehabilitation, sports, wellbeing 40* Movement assessment Machine learning-based [38] '20 Healthcare 41 Feedback presentation Real-time feedback [39] '14 Exercise 52** Feedback presentation [40] ' 21 Physical education 23 Feedback presentation [41] '21 Physical education 11 Feedback presentation Video-based visual feedback dog pose to do a V-shape instead. With automated pose estimation, a computer system could assess user movement and provide augmented feedback. ...
... They discussed the challenges of gathering data sets, working with coaches and players, and applying knowledge to practical situations. On the other hand, Gámez Díaz et al. [37] focused on digital twin coaching, which collects user data and provides personalized feedback. They categorized the works into sports, well-being, and rehabilitation domains. ...
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Pose estimation has various applications in analyzing human body movement and behavior, including providing feedback to users about their movements so they can adjust and improve their movement skills. To investigate the current research status and possible gaps, we searched Scopus and Web of Science for articles that (1) human ‘body’ pose estimation is used and (2) user movement is assessed and communicated. We used either a bottom-up or top-down approach to analyze 45 articles for methods used to estimate human body pose, assess movement, provide feedback to users, as well as methods to evaluate them. Our review found that pose estimation systems typically used CNNs while movement assessment methods varied from mathematical formulas or models, rule-based approaches, to machine learning. Feedback was primarily presented visually in verbal forms and nonverbal forms. The experiments to evaluate each part ranged from the use of public datasets to human participants. We found that pose estimation libraries play an important role in the advancement of this field. Nevertheless, the effectiveness and factors for choosing movement assessment methods for a new context are still unclear. In the end, we suggest that studies about feedback prioritization and erroneous feedback are needed.
... Additionally, digital twins employ strategies such as genomic analysis, clinical trial simulation, and continuous health monitoring to support proactive healthcare interventions and preventive measures [116], which are all crucial for chronic disease management. They also provide virtual health coaching, offering personalized lifestyle guidance based on individual health data and goals, and facilitate risk assessment to identify potential health issues early, enabling timely intervention [117]. ...
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... Movement as a dynamic body change is based on the movement of a few key parts to demonstrate different teaching movements, so the piecing together and tracking of joint parts is a good way to depict the general work [1]. The design of a movement system to aid training will therefore allow the trainer to learn independently with less time and space constraints, and the trainer will be able to adjust their training plan and intensity in real time based on the training data. ...
Article
The poor application effect of traditional sports training methods and the difficulty of recording data due to the time and space constraints of sports make it difficult for trainers to improve their learning outcomes. Based on this, the study proposes to apply human posture recognition in sports teaching design, and use VGGNet-19 as a feature extractor and OpenCV open source software to capture posture movements, and introduce the concepts of joint angle and movement similarity to design a sports assessment system for physical education based on the geometric spatial feature variability analysis of posture based on limb angle information. The testing outcomes demonstrate that the study’s improved gesture recognition algorithm has a recognition rate of more than 90% on gesture movements, and the maximum recognition error value (0.010) is smaller than that of the dynamic time-regularised gesture algorithm (0.014) and the convolutional neural network algorithm (0.017). The assessment system is also better able to improve students’ professional performance and satisfaction, with its average professional score and satisfaction reaching 86 and 92%, which is significantly better than other comparative algorithms. The method is effective in providing trainers with data-based training scenarios and helping them to improve their learning in sport.
... In particular, the process of ingesting the data and establishment of the connector between the digital twin and the physical twin. As the devil lies in the details, while these have the potential to significantly impact advanced surgical and medical care processes, these are fraught with limitations and risks, in particular, ethical issues associated with HDTs need to be carefully considered, to which we turn now [27][28][29][30][31][32][33][34][35][36][37]. ...
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Human digital twins (“HDT”) are one-on-one digital replicas of human beings, organs such as the heart and lungs, or pathophysiological processes such as immune systems, where the digital replicas and the physical counterparts are tethered with each other. Critical to the HDT is a connector (“bridge”) that links the human and digital counterparts. Sensors on human bodies obtain real-time pathophysiological data and pass them through the bridge to the digital twin. The digital twin runs artificial intelligence/machine learning (“AI/ML”) algorithms on this input and the resulting output is passed via the bridge to the connected human being. This combination of a connected human being, a digital counterpart and the bridge is unique to HDTs distinguishing them from simulations, clones, and digital assistants. HDTs are the prime drivers of precision medicine and personalised care. While the most common clinical uses of HDTs are as yet in cardiology and surgery, as this technology will evolve, new uses of HDT will be explored and will bring about a paradigm shift in medical care. In this chapter we have discussed the technology of HDTs, principles, methods of construction, and use of HDTs. We also discuss key limitations and human ethics related to the HDTs.
... DTs integrate data from various sensors (e.g., IoT devices, wearable sensors) to monitor biometrics such as heart rate, oxygen levels, and muscle activity [4,19,40]. These data allows highly personalized training programs that adapt to the athlete's needs and conditions in real-time [39,43]. ...
... As many authors have stated, DTs enable detailed analysis of an athlete's biomechanics, help to identify potential injury risks, and correct his/her wrong techniques. For example, the use of sensors to capture detailed motion data and assess techniques, can pinpoint areas that need improvement and prevent injuries [19,33,40]. ...
... DTs assist rehabilitation by monitoring recovery progress and adjusting training loads accordingly. They can simulate recovery processes and design optimal rehabilitation protocols to ensure athletes return to their peak performance safely [40,19,51,33]. ...
Preprint
Digital twins belong to ten of the strategic technology trends according to the Gartner list from 2019, and have encountered a big expansion, especially with the introduction of Industry 4.0. Sport, on the other hand, has become a constant companion of the modern human suffering a lack of a healthy way of life. The application of digital twins in sport has brought dramatic changes not only in the domain of sport training, but also in managing athletes during competitions, searching for strategical solutions before and tactical solutions during the games by coaches. In this paper, the domain of digital twins in sport is reviewed based on papers which have emerged in this area. At first, the concept of a digital twin is discussed in general. Then, taxonomies of digital twins are appointed. According to these taxonomies, the collection of relevant papers is analyzed, and some real examples of digital twins are exposed. The review finishes with a discussion about how the digital twins affect changes in the modern sport disciplines, and what challenges and opportunities await the digital twins in the future.
... For instance, DT models have been used for fitness management, healthcare, and physical activities. The Singapore Project studies urban planning and policy-making with "city digital twins", while DTs as human workers are also discussed [4][5][6]. ...
Article
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One of the central social challenges of the 21st century is society’s aging. AI provides numerous possibilities for meeting this challenge. In this context, the concept of digital twins, based on Cyber-Physical Systems, offers an exciting prospect. The e-VITA project, in which a virtual coaching system for elderly people is being created, allows the same to be assessed as a model for development. This white paper collects and presents relevant findings from research areas around digital twin technologies. Furthermore, we address ethical issues. This paper shows that the concept of digital twins can be usefully applied to older adults. However, it also shows that the required technologies must be further developed and that ethical issues must be discussed in an appropriate framework. Finally, the paper explains how the e-VITA project could pave the way towards developing a Digital Twin for Ageing.
... Further, several papers suggest initial approaches to using HDTs for improving employees' well-being (e.g., [59,60,61]). In the sports domain, HDTs are solely used for virtual coaching purposes (e. g. [62,63,64]), whereas in the Smart home and personal assistance domain, HDTs are implemented as virtual assistants to improve users' general well-being (e.g., [65]) and align users' needs with other smart devices (e.g., [66]). As illustrated in Figure 5, even within the HDT literature, several different HDT applications exist, each with different requirements and challenges for HDT design, demanding a closer examination of the fundamental elements of HDTs. ...
... In 2018, the definition of DTs was extended to include the replication of living or nonliving physical entities [62]. This paved the way for the development of HDTs, which in literature-depending on the respective application-are also referred to as Personal DT (e.g., [67]) or DT for Healthcare (e.g., [13]), among others. ...
... First, data sensitivity is a major challenge in any application working with human data, especially in the medical domain. Data theft or misuse can lead to severe consequences for the users [1,62]. [74], for example, reveals serious concerns of users about their personal data's security, highlighting that most users would only want to share data with medical staff and stakeholders directly relevant to the therapy. ...
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Human Digital Twins (HDTs) are a fast-emerging technology with significant potential in fields ranging from healthcare to sports. HDTs extend the traditional understanding of Digital Twins by representing humans as the underlying physical entity. This has introduced several significant challenges, including ambiguity in the definition of HDTs and a lack of guidance for their design. This survey brings together the recent advances in the field of HDTs to guide future developers by proposing a first cross-domain definition of HDTs based on their characteristics, as well as eleven key design considerations that emerge from the associated challenges.
... However, the use of IIoT devices also brings new challenges to the security of the network, with the possibility of attacks from malicious entities. In particular, Digital Twins (DTs) [20,13,9,15], as virtual replicas of physical devices, have become an essential component of IIoTs, providing the ability to simulate and optimize physical systems in a cost-effective manner [36,12,22,8]. However, the sensitive data generated by DTs and the potential for attacks on these virtual devices have led to concerns over their security [43,44]. ...
... As a digital technology, digital twins have greatly promoted the development of the Industrial Internet of Things [37]. On the one hand, a digital twin is a virtual representation of the entire life cycle of an object or system, updated based on real-time data, and using simulation, machine learning, and inference to assist decision-making [13,36,34]. In addition, digital twins have high requirements on the integrity of data information such as label information, especially in the context of machine learning [51]. ...
Article
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Introduction In recent years, the proliferation of Industrial Internet of Things (IIoT) devices has resulted in a substantial increase in data generation across various domains, including the nascent 6G networks. Digital Twins (DTs), serving as virtual replicas of physical entities, have gained popularity within the realm of IoT due to their capacity to simulate and optimize physical systems in a cost-effective manner. Nonetheless, the security of DTs and the safeguarding of the sensitive data they generate have emerged as paramount concerns. Fortunately, the Federated Fearning (FL) system has emerged as a promising solution to address the challenge of data privacy within DTs. Nonetheless, the requisite acquisition of a significant volume of labeled data for training purposes poses a formidable challenge, particularly in a DT environment that blends real and virtual data. Objectives To tackle this challenge, this study presents an innovative Semi-supervised FL (SSFL) framework designed to overcome the scarcity of labeled data through the strategic utilization of pseudo-labels. Methods Specifically, our proposed SSFL algorithm, named SSFL-MBE, introduces a novel approach by combining Mix data augmentation and Bayesian Estimation consistency regularization loss, thereby integrating robust augmentation techniques to enhance model generalization. Furthermore, we introduce a Bayesian-estimated pseudo-label loss that leverages prior probabilistic knowledge to enhance model performance. Our investigation focuses particularly on a demanding scenario where labeled and unlabeled data are segregated across disparate locations, specifically, the server and various clients. Results Comprehensive evaluations conducted on CIFAR-10 and MNIST datasets conclusively demonstrate that our proposed algorithm consistently surpasses mainstream SSFL baseline models, exhibiting an enhancement in model performance ranging from 0.5% to 1.5%. Conclusion Overall, this work contributes to the development of more efficient and secure approaches for model training in DT-empowered FL settings, which is crucial for the deployment of IIoTs in 6G-enabled environments.