Search Results (10)

University dormitories are vital spaces for students’ daily lives and informal learning, and require desks and chairs of utmost comfort. This study evaluates the desks and chairs at F University using Jack 8.01 software to optimize ergonomic design. By simulating three common sitting postures, this research identifies key issues, such as posture-related strain and limited reachability, particularly for female users. The optimized design introduces adjustable desk height (440~840 mm), chair height (250~520 mm), and tilt angle (0~60°), resulting in a 14.3% and 51.9% improvement in hip and knee joint comfort for the 5th percentile of female users, respectively, and effectively avoids the health risks caused by poor sitting posture. At the same time, based on the universal design concept, the design considerations for non-normative people are introduced. From the perspective of environmental sustainability, fewer wood-based panels used in the improved desk can reduce the greenhouse gas (GHG) footprint by approximately 135 kg CO₂ e. These enhancements highlight the critical role of digital human modeling (DHM) in developing ergonomic, “people-centered” furniture that promotes healthier and more effective learning environments, as well as the sustainable development of educational facilities. Future work will validate these findings in real-world settings and explore their applications across educational and professional spaces. Full article

The field of ergonomics has been significantly shaped by the advent of evolving technologies linked to new industrial paradigms, often referred to as Industry 4.0 (I4.0) and, more recently, Industry 5.0 (I5.0). Consequently, several studies have reviewed the integration of advanced technologies for improved ergonomics in different industry sectors. However, studies often evaluate specific technologies, such as extended reality (XR), wearables, artificial intelligence (AI), and collaborative robot (cobot), and their advantages and problems. In this sense, there is a lack of research exploring the state of the art of I4.0 and I5.0 virtual and digital technologies in evaluating work-related biomechanical risks. Addressing this research gap, this study presents a comprehensive review of 24 commercial tools and 10 academic studies focusing on work-related biomechanical risk assessment using digital and virtual technologies. The analysis reveals that AI and digital human modelling (DHM) are the most commonly utilised technologies in commercial tools, followed by motion capture (MoCap) and virtual reality (VR). Discrepancies were found between commercial tools and academic studies. However, the study acknowledges limitations, including potential biases in sample selection and search methodology. Future research directions include enhancing transparency in commercial tool validation processes, examining the broader impact of emerging technologies on ergonomics, and considering human-centred design principles in technology integration. These findings contribute to a deeper understanding of the evolving landscape of biomechanical risk assessment. Full article

This study presents an innovative approach for modernizing the garment industry through the fusion of digital human modeling (DHM), virtual modeling for fit sizing, ergonomic body-size data, and e-library resources. The integration of these elements empowers manufacturers to revolutionize their clothing design and production methods, leading to the delivery of unparalleled fit, comfort, and personalization for a wide range of body shapes and sizes. DHM, known for its precision in representing human bodies virtually and integrating anthropometric data, including ergonomic measurements, enhances the shopping experience by providing valuable insights. Consumers gain access to the knowledge necessary for making tailored clothing choices, thereby enhancing the personalization and satisfaction of their shopping experience. The incorporation of e-library resources takes the garment design approach to a data-driven and customer-centric level. Manufacturers can draw upon a wealth of information regarding body-size diversity, fashion trends, and customer preferences, all sourced from e-libraries. This knowledge supports the creation of a diverse range of sizes and styles, promoting inclusivity and relevance. Beyond improving garment fit, this comprehensive integration streamlines design and production processes by reducing the reliance on physical prototypes. This not only enhances efficiency but also contributes to environmental responsibility, fostering a more sustainable and eco-friendly future for the garment industry and embracing the future of fashion, where technology and data converge to create clothing that authentically fits, resonates with consumers, and aligns with the principles of sustainability. This study developed the mobile application integrating with the information in cloud database in order to present the best-suited garment for the user. Full article

Force-based human posture estimation (FPE) provides a valuable alternative when camera-based human motion capturing is impractical. It offers new opportunities for sensor integration in smart products for patient monitoring, ergonomic optimization and sports science. Due to the interdisciplinary research on the topic, an overview of existing methods and the required expertise for their utilization is lacking. This paper presents a systematic review by the PRISMA 2020 review process. In total, 82 studies are selected (59 machine learning (ML)-based and 23 digital human model (DHM)-based posture estimation methods). The ML-based methods use input data from hardware sensors—mostly pressure mapping sensors—and trained ML models for estimating human posture. The ML-based human posture estimation algorithms mostly reach an accuracy above 90%. DHMs, which represent the structure and kinematics of the human body, adjust posture to minimize physical stress. The required expert knowledge for the utilization of these methods and their resulting benefits are analyzed and discussed. DHM-based methods have shown their general applicability without the need for application-specific training but require expertise in human physiology. ML-based methods can be used with less domain-specific expertise, but an application-specific training of these models is necessary. Full article

Since musculoskeletal disorders are one of the most common work-related diseases for assemblers and machine operators, it is crucial to find new ways to alleviate the physical load on workers. Support systems such as exoskeletons or handheld power tools are promising technology to reduce the physical load on the humans. The development of such systems requires consideration of the interactions between human and technical systems. The physical relief effect of the exoskeleton can be demonstrated in experimental studies or by simulation with the digital human model (DHM). For the digital development of these support systems, an application-oriented representation of the workload is necessary. To facilitate digital development, an application-oriented workload model (ApOL model) of an overhead working task is presented. The ApOL model determines the load (forces, torques) onto the DHM during an overhead screw-in task using a cordless screwdriver, based on experimental data. The ApOL model is verified by comparing the simulated results to the calculated values from a mathematical model, using experimental data from three participants. The comparison demonstrates successful verification, with a maximum relative mean-absolute-error (rMAE) of the relevant load components at 11.4%. The presented ApOL model can be utilized to assess the impact of cordless screwdriver design on the human workload and facilitate a strain-based design approach for support systems e.g., exoskeletons. Full article

A large percentage of musculoskeletal disorder cases occur in brewing companies. The aim of this research study is to evaluate the risk of injuries for workers in the local brewing industry by integrating the actual human motion, which was captured by the Xsens MVN Awinda motion tracking system, with the JACK Siemens ergonomics tools. This proposed fusion technology greatly overcomes the time-consuming issue in the traditionally full-body simulation and the posture sensitivity issue in the current digital human modelling (DHM) technology. In this study, the subjects performed a series of daily lifting tasks utilizing 72 kg kegs. The forces exerted on the lower back of brewery workers were fully analyzed. The maximum load applied on the hands for each of the tasks was also estimated to prevent workers from injuries. Additionally, the key factors that highly correlate to lower back injuries were emphasized. Due to the heavy load applied by the kegs, large spinal forces were exerted on the lower back of workers. Moreover, reducing trunk and hip flexion is also important to prevent workers from injuries. The results of this study can greatly improve the implementation of training techniques, environmental modifications, and assistive device design, which aim to eliminate injury risk and increase the productivity of workers within the breweries. Full article

The conventional reconstruction method of off-axis digital holographic microscopy (DHM) relies on computational processing that involves spatial filtering of the sample spectrum and tilt compensation between the interfering waves to accurately reconstruct the phase of a biological sample. Additional computational procedures such as numerical focusing may be needed to reconstruct free-of-distortion quantitative phase images based on the optical configuration of the DHM system. Regardless of the implementation, any DHM computational processing leads to long processing times, hampering the use of DHM for video-rate renderings of dynamic biological processes. In this study, we report on a conditional generative adversarial network (cGAN) for robust and fast quantitative phase imaging in DHM. The reconstructed phase images provided by the GAN model present stable background levels, enhancing the visualization of the specimens for different experimental conditions in which the conventional approach often fails. The proposed learning-based method was trained and validated using human red blood cells recorded on an off-axis Mach–Zehnder DHM system. After proper training, the proposed GAN yields a computationally efficient method, reconstructing DHM images seven times faster than conventional computational approaches. Full article

Due to increasing competition in the global market and to meet the need for rapid changes in product variability, it is necessary to introduce self-configurable and smart solutions within the entire process chain, including manual assembly to ensure the more efficient and ergonomic performance of the manual assembly process. This paper presents a smart assembly system including newly developed smart manual assembly workstation controlled by a smart algorithm. The smart assembly workstation is self-configurable according to the anthropometry of the individual worker, the complexity of the assembly process, the product characteristics, and the product structure. The results obtained by a case study show that is possible to organize manual assembly process with rapid adaptation of the smart assembly system to new products and workers characteristics, to achieve ergonomic working conditions through Digital Human Modelling (DHM), to minimize assembly time, and to prevent error during the assembly process. The proposed system supports the manual assembly process redesign to ensure a better working environment and aims to have an important value for applying the smart algorithms to manual assembly workstations in human-centered manufacturing systems. Full article

Digital human models (DHMs) are virtual representations of human beings. They are used to conduct, among other things, ergonomic assessments in factory layout planning. DHM software tools are challenging in their use and thus require a high amount of training for engineers. In this paper, we present a virtual reality (VR) application that enables engineers to work with DHMs easily. Since VR systems with head-mounted displays (HMDs) are less expensive than CAVE systems, HMDs can be integrated more extensively into the product development process. Our application provides a reality-based interface and allows users to conduct an assembly task in VR and thus to manipulate the virtual scene with their real hands. These manipulations are used as input for the DHM to simulate, on that basis, human ergonomics. Therefore, we introduce a software and hardware architecture, the VATS (virtual action tracking system). This paper furthermore presents the results of a user study in which the VATS was compared to the existing WIMP (Windows, Icons, Menus and Pointer) interface. The results show that the VATS system enables users to conduct tasks in a significantly faster way. Full article

As recommended by the international standards, ISO 21542, ease of wayfinding must be ensured by installing signage at all key decision points on walkways such as forks because signage greatly influences the way in which people unfamiliar with an environment navigate through it. Therefore, we aimed to develop a new system for evaluating the ease of wayfinding, which could detect spots that cause disorientation, i.e., “disorientation spots”, based on simulated three-dimensional (3D) interactions between wayfinding behaviors and signage location, visibility, legibility, noticeability, and continuity. First, an environment model reflecting detailed 3D geometry and textures of the environment, i.e., “as-is environment model”, is generated automatically using 3D laser-scanning and structure-from-motion (SfM). Then, a set of signage entities is created by the user. Thereafter, a 3D wayfinding simulation is performed in the as-is environment model using a digital human model (DHM), and disorientation spots are detected. The proposed system was tested in a virtual maze and a real two-story indoor environment. It was further validated through a comparison of the disorientation spots detected by the simulation with those of six young subjects. The comparison results revealed that the proposed system could detect disorientation spots, where the subjects lost their way, in the test environment. Full article