7. The Human-Digital Twin in the manufacturing industry: current perspectives and a glimpse of future

Montini, Elias; Bonomi, Niko; Daniele, Fabio; Bettoni, Andrea; Pedrazzoli, Paolo; Carpanzano, Emanuele; Rocco, Paolo

doi:10.1561/9781680838770.ch7

Cited by 6 publications

(5 citation statements)

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“…Commonly the human factor is related to ergonomic evaluations and it is a necessary link for worker wellbeing in monitoring, production planning and human-robot collaboration. Conventionally, all these evaluations are performed offline and the model cannot adapt to a dynamic environment (Montini et al, 2021). Human-robot collaboration and ergonomic assessments for adaptive automation are two interesting subjects in the use of controlling mannequins.…”

Section: Mannequin Controlmentioning

confidence: 99%

Automation of unstructured production environment by applying reinforcement learning

Nambiar

Wiberg²,

Tarkian³

2023

Front. Manuf. Technol.

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Implementation of Machine Learning (ML) to improve product and production development processes poses a significant opportunity for manufacturing industries. ML has the capability to calibrate models with considerable adaptability and high accuracy. This capability is specifically promising for applications where classical production automation is too expensive, e.g., for mass customization cases where the production environment is uncertain and unstructured. To cope with the diversity in production systems and working environments, Reinforcement Learning (RL) in combination with lightweight game engines can be used from initial stages of a product and production development process. However, there are multiple challenges such as collecting observations in a virtual environment which can interact similar to a physical environment. This project focuses on setting up RL methodologies to perform path-finding and collision detection in varying environments. One case study is human assembly evaluation method in the automobile industry which is currently manual intensive to investigate digitally. For this case, a mannequin is trained to perform pick and place operations in varying environments and thus automating assembly validation process in early design phases. The next application is path-finding of mobile robots including an articulated arm to perform pick and place operations. This application is expensive to setup with classical methods and thus RL enables an automated approach for this task as well.

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Section: Mannequin Controlmentioning

confidence: 99%

Automation of unstructured production environment by applying reinforcement learning

Nambiar

Wiberg²,

Tarkian³

2023

Front. Manuf. Technol.

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“…Sensing: Three types of sensing modalities have been reported in previous studies: physiological, behavioral, and environmental sensing. Physiological sensing was related to human or tissue function, for example, heartbeats (e.g., [23]) and skin conductance data (e.g., [27]). Behavioral sensing was used for human behavior tracking, including motion sensor signals (e.g., [13]).…”

Section: Overall System Workflow Of Dtmentioning

confidence: 99%

“…Environmental sensing was classified into social and physical sensing. Physical sensing was used for the spatial environment, such as room temperature (e.g., [27]), while social sensing was related to activities such as social media (e.g., [23]). In addition, general medical records such as Personal Health Records (PHR) [20], [23] or Computed Tomography (CT) scanned images (e.g., Cross-sectional images) [30] were used as indirect sensing data to capture various biometric signals related to the body in the medical field.…”

Section: Overall System Workflow Of Dtmentioning

confidence: 99%

Human Digital Twins for Pervasive Healthcare: A Scoping Review (Preprint)

Park,

Lee

et al. 2023

Interactive Journal of Medical Research

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Human digital twins replicate humans in virtual worlds with real-time sensing and machine learning, enabling various services such as visualization, simulation, and prediction. The concept of human digital twins has recently been applied in diverse domains such as manufacturing and healthcare. However, there is a lack of systematic reviews and discussions on the key components of human digital twins and their applications in mental healthcare contexts. This article first offers a scoping review of the purposes of human digital twins and their models and thereafter charts how human digital twins with sensing, mapping, and acting capabilities can serve as an enabling technology for a data-driven, patient-centric, systems approach to pervasive mental healthcare. This article used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for selecting 24 papers from the last 10 years in PubMed, Web of Science, Scopus, and Google Scholar.

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“…However, most studies in this field, according to Du et al [15], focus on the system level in modeling information processing rather than modeling behaviors at the personal level. As a result, the Human Digital Twin (HDT) was proposed to integrate human workers in the I4.0 field, which supported data collection, scheduling, communication handling, and so on [20], [21]. HDT is the cyberphase of the human entity, which is fed by dynamic real-time parameters to represent the human in the physical phase.…”

Section: Introductionmentioning

confidence: 99%

“…HDT is the cyberphase of the human entity, which is fed by dynamic real-time parameters to represent the human in the physical phase. These parameters include but are not limited to workers' characteristics, behaviors [22], geospatial and psychophysical conditions, contextual parameters, intentions, cognitions, emotional state, food income [23], motion recognition [10], and other biological parameters such as electromyogram (EMG), heart rate, heart rhythm, respiration, blood pressure, Galvanic Skin Response (GSR) [20], [21], [24], [25]. Despite the rising number of papers that talk about HDTs and the possible influence they might have in the future, there is no existing agreement on exactly how to design these kinds of systems [26].…”

Section: Introductionmentioning

confidence: 99%

Extension of HAAS for the Management of Cognitive Load

Eesee,

Jaskó,

Eigner

et al. 2024

IEEE Access

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The rapid advancement of technology related to Industry 4.0 has brought about a paradigm shift in the way we interact with assets across various domains. This progress has led to the emergence of the concept of a Human Digital Twin (HDT), a virtual representation of an individual's cognitive, psychological, and behavioral characteristics. The HDT has demonstrated potential as a strategic tool for enhancing productivity, safety, and collaboration within the framework of Industry 5.0. In response to this challenge, this paper outlines a process for tracking human cognitive load using the galvanic skin response as a physiological marker and proposes a novel method for managing cognitive load based on the extended Human Asset Administration Shell (HAAS). The proposed HAAS framework integrates real-time data streams from wearable sensors, user skills, contextual information, task specifics, and environmental and surrounding conditions to deliver a comprehensive understanding of an individual's cognitive state, physical wellness, and skill set. Through the incorporation of skills set, physical, physiological, and psychological variables, and task parameters, the developed HAAS framework enables the identification, management, and development of individual capabilities, thereby facilitating individualized training and knowledge exchange. The applicability of the developed framework is proved by an experiments in the Operator 4.0 laboratory with the detailed HAAS parameters. INDEX TERMS Cognitive load , GSR , Human Asset Administration Shell Implementing I4.0 and incorporating human-in-the-loop control systems showed that the cognitive load on operators in

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7. The Human-Digital Twin in the manufacturing industry: current perspectives and a glimpse of future

Cited by 6 publications

References 0 publications

Automation of unstructured production environment by applying reinforcement learning

Automation of unstructured production environment by applying reinforcement learning

Human Digital Twins for Pervasive Healthcare: A Scoping Review (Preprint)

Extension of HAAS for the Management of Cognitive Load

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