A deep learning-enhanced Digital Twin framework for improving safety and reliability in human–robot collaborative manufacturing

Wang, Shenglin; Zhang, Jingqiong; Wang, Peng; Law, James; Călinescu, Radu; Mihaylova, Lyudmila

doi:10.1016/j.rcim.2023.102608

Cited by 37 publications

(4 citation statements)

References 45 publications

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“…Having a human digital twin of operators in industrial settings can provide useful information on safety concerns with how the operator interacts with the present machines [ 82 , 92 , 93 , 94 , 95 ]. This has also been used to simulate human–robot interactions in factories as well [ 96 , 97 , 98 , 99 , 100 ]. Human digital twins are also an area of interest due to increasing research into virtual and augmented reality [ 101 ], where digital twins can serve as virtual avatars [ 91 , 102 ].…”

Section: Discussionmentioning

confidence: 99%

Digital Twins for Healthcare Using Wearables

Johnson,

Saikia

2024

Bioengineering

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Digital twins are a relatively new form of digital modeling that has been gaining popularity in recent years. This is in large part due to their ability to update in real time to their physical counterparts and connect across multiple devices. As a result, much interest has been directed towards using digital twins in the healthcare industry. Recent advancements in smart wearable technologies have allowed for the utilization of human digital twins in healthcare. Human digital twins can be generated using biometric data from the patient gathered from wearables. These data can then be used to enhance patient care through a variety of means, such as simulated clinical trials, disease prediction, and monitoring treatment progression remotely. This revolutionary method of patient care is still in its infancy, and as such, there is limited research on using wearables to generate human digital twins for healthcare applications. This paper reviews the literature pertaining to human digital twins, including methods, applications, and challenges. The paper also presents a conceptual method for creating human body digital twins using wearable sensors.

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Section: Discussionmentioning

confidence: 99%

Digital Twins for Healthcare Using Wearables

Johnson,

Saikia

2024

Bioengineering

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“…Additionally, the authors of [8] discussed the implementation of flexible manufacturing cells under the ISO 23247 standard [9][10][11][12], emphasizing resilience and adaptability in production environments. The study by [13] also addressed safety and reliability in human-robot collaborations through advanced digital twin frameworks, which thus improved the predictive maintenance and interaction dynamics. Moreover, the authors of [14] focused on data-driven approaches to optimize control processes in manufacturing systems, integrating continuous feedback mechanisms for system enhancements.…”

Section: Related Work 21 Autonomous Manufacturingmentioning

confidence: 99%

Enhancing Industrial Process Control: Integrating Intelligent Digital Twin Technology with Proportional-Integral-Derivative Regulators

Dapkute,

Siozinys,

Jonaitis

et al. 2024

Machines

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This paper explores the integration of intelligent digital twin technology with PID regulators in industrial process control utilizing smart meter data. It presents a novel approach involving the creation of mathematical models to simulate real-time system behavior, thereby enhancing the PID control loop. The focus is on the development of specialized IT infrastructure to support this integration, which includes data acquisition, processing, and control optimization. This integration aims to not only improve control system efficiency but also introduce a robust predictive maintenance framework, offering significant benefits across a wide range of industrial applications.

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“…Recent advancements in technology, particularly the integration of computer vision and deep learning, have ushered in a new era in exercise monitoring [2][3][4]. Leveraging these technological strides, this paper introduces a groundbreaking framework utilizing a PoseNet-enabled deep neural network, primarily aimed at real-time exercise monitoring of physical culture students [5].…”

Section: Introductionmentioning

confidence: 99%

Development of Deep Learning Enabled Augmented Reality Framework for Monitoring the Physical Quality Training of Future Trainers-Teachers

Tileubay,

Yerekeshova,

Baiganova

et al. 2024

IJACSA

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The fusion of augmented reality (AR) and deep learning technologies has ushered in a transformative era in the realm of real-time physical activity monitoring. This research paper introduces a system that harnesses the capabilities of PoseNet-based skeletal keypoint extraction and deep neural networks to achieve unparalleled accuracy and real-time functionality in the identification and classification of a wide spectrum of physical activities. With an impressive accuracy rate of 98% within 100 training epochs, the system proves its mettle in precise activity recognition, making it invaluable in domains such as fitness training, physical education, sports coaching, and home-based fitness. The system's real-time feedback mechanism, bolstered by AR technology, not only enhances user engagement but also motivates users to optimize their exercise routines. This paper not only elucidates the system's architecture and functionality but also highlights its potential applications across diverse fields. Furthermore, it delineates the trajectory of future research avenues, including the development of advanced feedback mechanisms, exploration of multi-modal sensing techniques, personalization for users, assessment of long-term impacts, and endeavors to ensure accessibility, inclusivity, and data privacy. In essence, this research sets the stage for the evolution of real-time physical activity monitoring, offering a compelling framework to improve fitness, physical education, and athletic training while promoting healthier lifestyles and the overall well-being of individuals worldwide.

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A deep learning-enhanced Digital Twin framework for improving safety and reliability in human–robot collaborative manufacturing

Cited by 37 publications

References 45 publications

Digital Twins for Healthcare Using Wearables

Digital Twins for Healthcare Using Wearables

Enhancing Industrial Process Control: Integrating Intelligent Digital Twin Technology with Proportional-Integral-Derivative Regulators

Development of Deep Learning Enabled Augmented Reality Framework for Monitoring the Physical Quality Training of Future Trainers-Teachers

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