An IoT and machine learning enhanced framework for real-time digital human modeling and motion simulation

Article Chemistry, Analytical

Using Digital Human Modelling to Evaluate the Risk of Musculoskeletal Injury for Workers in the Healthcare Industry

Xiaoxu Ji et al.

Summary: This research study presents a new technique for evaluating the risk of injuries for healthcare workers using unobtrusive wearable devices and digital human technology. By continuously monitoring the movement of healthcare workers, potential inappropriate postures can be identified and adjusted to reduce the risk of lower back injuries.

SENSORS (2023)

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Review Computer Science, Cybernetics

Digital Human Modeling: A Review and Reappraisal of Origins, Present, and Expected Future Methods for Representing Humans Computationally

H. Onan Demirel et al.

Summary: The paper highlights the significance and implementation of digital human modeling (DHM) in modern product development, emphasizing both the advantages and challenges of DHM. It discusses the evolving trends and roles of DHM in the design field.

INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION (2022)

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Article Chemistry, Multidisciplinary

Virtual Reality and Digital Human Modeling for Ergonomic Assessment in Industrial Product Development: A Patent and Literature Review

Adailton Goncalves da Silva et al.

Summary: The late detection of ergonomic component assembly issues during manufacturing processes has negative impacts on operator well-being and productivity, as well as high correction costs. This study aims to analyze the application of virtual reality and digital human modeling for physical ergonomics assessment during product development in the industry. Despite a relative consensus on the benefits of integrating these technologies, their combination is rarely seen in the same analysis and most cases focus on pre-designed production processes.

APPLIED SCIENCES-BASEL (2022)

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Article Engineering, Manufacturing

Biomechanical analysis of the upper body during overhead industrial tasks using electromyography and motion capture integrated with digital human models

Dario Panariello et al.

Summary: In this paper, a biomechanical analysis of the upper body during overhead industrial tasks is presented. The analysis provides a comprehensive description of human movement and internal load state by evaluating joint angles, joint torques, and muscle activations. The results of the analysis have implications for optimizing the configuration of workers in industrial workplaces and developing assistance devices to reduce physical overloading.

INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM (2022)

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Article Engineering, Industrial

Ergonomic evaluation of work table for waste sorting tasks using digital human modelling

Francis J. Emmatty et al.

Summary: This study investigates ergonomic design aspects of a cost-effective intervention, a work table for waste sorting in developing countries, to reduce musculoskeletal disorders among workers. Evaluation is done through simulating postures involved in sorting tasks and biomechanical assessment, determining appropriate table height and hopper position for optimal physical workload.

INTERNATIONAL JOURNAL OF INDUSTRIAL ERGONOMICS (2021)

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Article Engineering, Multidisciplinary

Design and Experimental Research of Knee Joint Prosthesis Based on Gait Acquisition Technology

Yonghong Zhang et al.

Summary: The study focuses on the biomimetic performance of the knee joint in lower limb prostheses, using an optical 3D motion capture system to collect motion data and designing a prosthetic knee joint with good biomimetic performance. The results show that the prosthetic knee joint can help amputees complete various gaits and provide important data for prosthetic designers, suggesting potential wide applications in prosthetic design and other fields.

BIOMIMETICS (2021)

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Article Computer Science, Information Systems

Development and Validation of a Deep Learning Algorithm and Open-Source Platform for the Automatic Labelling of Motion Capture Markers

Allison L. Clouthier et al.

Summary: The study developed a deep learning-based algorithm for labeling motion capture data that can be trained on measured or simulated marker trajectories, utilizing the Hungarian algorithm and a generic marker set. The algorithm showed high accuracy and could be further improved through transfer learning. It can accurately label motion capture data with missing or extraneous markers, and as more data is collected, the accuracy will continue to improve.

IEEE ACCESS (2021)

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Review Engineering, Industrial