IISE transactions on occupational ergonomics and human factors最新文献

{"title":"A Passive Back-Support Exoskeleton for Manual Materials Handling: Reduction of Low Back Loading and Metabolic Effort during Repetitive Lifting","authors":"T. Schmalz, Anja Colienne, Emily A. Bywater, L. Fritzsche, C. Gärtner, M. Bellmann, Samuel M. F. Reimer, M. Ernst","doi":"10.1080/24725838.2021.2005720","DOIUrl":"https://doi.org/10.1080/24725838.2021.2005720","url":null,"abstract":"OCCUPATIONAL APPLICATIONS Globalization and eCommerce continue to fuel unprecedented growth in the logistics and warehousing markets. Simultaneously, the biggest bottleneck for these industries is their human capital. Where automation and robotic solutions fail to deliver a return on investment, humans frequently take over handling tasks that place harmful loads and strains on the body. Occupational exoskeletons can reduce fatigue and strain by supporting the lower spine and are designed to prevent work-related musculoskeletal disorders and other injuries. They are a mid- to long-term investment for industries to improve ergonomic conditions in workplaces, with the potential for reducing absences from work, sick days logged, and workers compensation claims. To examine the effectiveness of the newly introduced Paexo Back exoskeleton, a study was completed with 10 participants who completed manual load handling tasks with and without the exoskeleton. Key findings include significant reductions in metabolic effort and low back loading when the exoskeleton is worn. TECHNICAL ABSTRACT Background: Work-related low back pain is a major threat to workers and society. Some new commercial and prototype exoskeletons are designed to specifically control the development of such disorders. Some beneficial effects of these exoskeletons have been reported earlier. Purpose: Determine the potential benefits of a newly introduced exoskeleton, Paexo Back, which is designed to reduce low back loading during lifting tasks. Methods: Ten healthy subjects participated in this study. To replicate a typical workplace situation, a repetitive lifting task with and without the exoskeleton was performed. For 5-min periods, the participants repeatedly lifted a 10-kg box from the floor onto a table and then placed it back on the floor. Effects of exoskeleton use were assessed using a diverse set of outcomes. Oxygen uptake and heart rate were measured using a wireless spiroergometry system. Activation levels of back, abdominal, and thigh muscles were also measured using a wireless electromyographic system. Kinematic data were recorded using an optoelectronic device, and ground reaction forces were measured with two force plates. Joint compression forces in the lower spine (L4/L5 and L5/S1) were estimated using the AnyBody™ Modeling System during the upward lifting portion of the lifting task (bringing the box to the table). Results: Using the exoskeleton resulted in significant reductions in oxygen rate (9%), activation of the back and thigh muscles (up to 18%), and peak and mean compression forces at L4/L5 (21%) and L5/S1 (20%). Conclusions: These results show that using the tested exoskeleton for a lifting task contributes to an increased metabolic efficiency, a reduction in the back muscle activation required to conduct the task, and a reduction in low back loading.","PeriodicalId":73332,"journal":{"name":"IISE transactions on occupational ergonomics and human factors","volume":"10 1","pages":"7 - 20"},"PeriodicalIF":0.0,"publicationDate":"2021-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49613297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Collision Avoidance Algorithm for Human Motion Prediction Based on Perceived Risk of Collision: Part 2-Application","authors":"James Yang, Brad M. Howard, Juan Baus","doi":"10.1080/24725838.2021.2004265","DOIUrl":"https://doi.org/10.1080/24725838.2021.2004265","url":null,"abstract":"Occupational Application Digital human models have been widely used for occupational assessments to reduce potential injury risk, such as automotive assembly lines, box lifting, and in the mining industry. Human motion prediction is one of the important capabilities in digital human models, and collision avoidance is involved in human motion prediction. An algorithm proposed earlier was implemented for human motion prediction, and simulated results were found to have a good correlation with the experimental studies. Use of this algorithm can help ensure that human motion is predicted realistically, and thus can impact the accuracy of injury risk assessments. TECHNICAL ABSTRACT Background: With any type of human movement, there is the potential for a collision with other objects. In addition to the objects presented in the environment surrounding one’s body and surrounding the objects to be manipulated, one's own body can become an obstacle. Therefore, consideration of the methods available for avoiding obstacles is necessary to comprehensively describe the way human movements are planned. Purpose: This paper evaluates a collision avoidance algorithm for human motion prediction based on the perceived risk of collision, specifically the application to human motion prediction. Method: Human motion prediction is formulated as an optimization problem with dynamic effort as the cost function, and the perceived risk of collision is considered as one constraint among other constraints. Performance using the new formulation was compared to observed performance from an experiment. Result: Based on the results, the new formulation can account for the suboptimal behavior observed in real subjects while still optimizing biomechanical cost. The predicted motion is much more realistic compared with that from purely biomechanically optimized formulation. Application: The developed collision avoidance algorithm can be applied to optimization-based manual movement prediction in which obstacles need to be navigated.","PeriodicalId":73332,"journal":{"name":"IISE transactions on occupational ergonomics and human factors","volume":"9 1","pages":"211 - 222"},"PeriodicalIF":0.0,"publicationDate":"2021-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49413162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization of Productivity and Worker Well-Being by Using a Multi-Objective Optimization Framework","authors":"Aitor Iriondo Pascual, D. Högberg, Dan Lämkull, E. Perez Luque, Anna Syberfeldt, L. Hanson","doi":"10.1080/24725838.2021.1997834","DOIUrl":"https://doi.org/10.1080/24725838.2021.1997834","url":null,"abstract":"OCCUPATIONAL APPLICATIONS Worker well-being and overall system performance are important elements in the design of production lines. However, studies of industry practice show that current design tools are unable to consider concurrently both productivity aspects (e.g., line balancing and cycle time) and worker well-being related aspects (e.g., the risk of musculoskeletal disorders). Current practice also fails to account for anthropometric diversity in the workforce and does not use the potential of multi-objective simulation-based optimization techniques. Accordingly, a framework consisting of a workflow and a digital tool was designed to assist in the proactive design of workstations to accommodate worker well-being and productivity. This framework uses state-of-the-art optimization techniques to make it easier and quicker for designers to find successful workplace design solutions. A case study to demonstrate the framework is provided. TECHNICAL ABSTRACT Rationale: Simulation technologies are used widely in industry as they enable efficient creation, testing, and optimization of the design of products and production systems in virtual worlds. Simulations of productivity and ergonomics help companies to find optimized solutions that maintain profitability, output, quality, and worker well-being. However, these two types of simulations are typically carried out using separate tools, by persons with different roles, with different objectives. Silo effects can result, leading to slow development processes and suboptimal solutions. Purpose: This research is related to the realization of a framework that enables the concurrent optimization of worker well-being and productivity. The framework demonstrates how digital human modeling can contribute to Ergonomics 4.0 and support a human factors centered approach in Industry 4.0. The framework also facilitates consideration of anthropometric diversity in the user group. Methods: Design and creation methodology was used to create a framework that was applied to a case study, formulated together with industry partners, to demonstrate the functionality of the noted framework. Results: The framework workflow has three parts: (1) Problem definition and creation of the optimization model; (2) Optimization process; and (3) Presentation and selection of results. The case study shows how the framework was used to find a workstation design optimized for both productivity and worker well-being for a diverse group of workers. Conclusions: The framework presented allows for multi-objective optimizations of both worker well-being and productivity and was successfully applied in a welding gun use case.","PeriodicalId":73332,"journal":{"name":"IISE transactions on occupational ergonomics and human factors","volume":"9 1","pages":"143 - 153"},"PeriodicalIF":0.0,"publicationDate":"2021-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49558097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Introduction to the Special Issue on Digital Human Modeling (DHM) in Ergonomics 4.0","authors":"Gunther Paul, Xuguang Wang, James Yang","doi":"10.1080/24725838.2021.2027508","DOIUrl":"https://doi.org/10.1080/24725838.2021.2027508","url":null,"abstract":"Welcome to this special issue of the IISE Transactions on Occupational Ergonomics and Human Factors! Our primary motivations in developing this issue were the emerging concepts of Ergonomics 4.0 and 5.0 in Human Factors and Ergonomics within the wider frameworks of Industry 4.0 and 5.0; specifically, clarifying their paradigms and contributing to the understanding of how, and if, digital human modeling plays a role in these concepts. Papers for this special issue mostly originated in the Digital Human Modeling and Simulation (DHMS) track at the International Ergonomics Association (IEA) triennial world congress in Vancouver, Canada (IEA2021). The aim of the DHMS sessions at this congress was to present the latest developments in DHM with a focus on the conference theme, “HFE in a Connected World – L’ergonomie 4.0.” Participants at IEA2021 were able to make shortened submissions to the conference in view of an expression of interest for a full paper submission to this special issue. The IEA DHMS scientific committee members then invited selected authors to make such a submission to this special issue. A formal review process was conducted for all submissions, consistent with policies and procedures employed by the IISE Transactions on Occupational Ergonomics and Human Factors. Since two of the current guest editors were involved in one or more of the papers submitted, we adopted specific procedures employed that ensured a fair review process. First, editors were not involved in any aspect of the review process or decisions for the papers on which they were an author. Second, we relied in large part on the authors of submitted papers to IEA2021 to review other submissions. Third, and given the relatively small DHMS community, we were careful to ensure that reviewers were independent of the authors/teams involved in the papers they reviewed. Finally, no reviewers were solicited from among employees of DHM developers to avoid potential conflicts of interest.","PeriodicalId":73332,"journal":{"name":"IISE transactions on occupational ergonomics and human factors","volume":"9 1","pages":"107 - 110"},"PeriodicalIF":0.0,"publicationDate":"2021-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43759701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Minimizing Low Back Cumulative Loading during Design of Manual Material Handling Tasks: An Optimization Approach","authors":"S. Almosnino, Jessica Cappelletto","doi":"10.1080/24725838.2021.2021458","DOIUrl":"https://doi.org/10.1080/24725838.2021.2021458","url":null,"abstract":"OCCUPATIONAL APPLICATIONS We present a practical method for minimizing low-back cumulative loading that leverages digital human modeling capabilities and optimization using an evolutionary algorithm. We demonstrate use of the method in a simulated lifting task. Our results show that this method is robust to different routines for calculating cumulative loading. The proposed method can aid ergonomics engineers in addressing a potential risk factor early in the design stage, even in the absence of an established threshold limit value, and it provides a time saving by eliminating the need to adjust workplace parameters across many design possibilities. TECHNICAL ABSTRACT Background Excessive exposure to low-back cumulative loading (LBCL) has been implicated as a risk factor for developing pain or injury during manual material handling (MMH) tasks. However, addressing LBCL during conceptual work design is challenging because of a lack of an established and widely accepted LBCL threshold limit value. We therefore formulate the design challenge using an optimization framework aided by digital human modeling (DHM). Methods We constructed a hypothetical MMH task requiring lifting, carrying, and placement of boxes into 16 storage locations. External loads were composed of four different mass categories handled 250 times, with four different relative handling frequencies. Resulting low back compressive force time series were integrated according to four suggested methods. Subsequently, we defined our objective function and constraints, and obtained a solution using an evolutionary algorithm. Results The percentage agreement between the four different relative handling frequencies and integration methods ranged between 89.5% and 100%. Kendall’s coefficient of concordance values ranged between 0.74 and 1.0, indicating good to perfect agreement among the solutions. Conclusion There is consensus is that minimizing LBCL exposure is beneficial, particularly during task design phases. Our results show that, irrespective of the theoretical background pertaining to LBCL quantification, the method proposed produces a robust and largely similar solution, at least for the MMH scenarios we simulated. Our proposed approach takes advantage of DHM capabilities to simulate diverse MMH scenarios and provides solution estimates at the conceptual design phase. The proposed method can be expanded using multi-objective optimizations schemes and additional constraints to provide a solution that addresses multiple injury and fatigue pathways.","PeriodicalId":73332,"journal":{"name":"IISE transactions on occupational ergonomics and human factors","volume":"9 1","pages":"124 - 133"},"PeriodicalIF":0.0,"publicationDate":"2021-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48642200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ergonomic Risk Identification for Spacesuit Movements Using Factorial Analysis","authors":"Linh Q. Vu, K. H. Kim, S. Rajulu","doi":"10.1080/24725838.2021.1972056","DOIUrl":"https://doi.org/10.1080/24725838.2021.1972056","url":null,"abstract":"OCCUPATIONAL APPLICATIONS Biomechanical risk factors associated with spacesuit manual material handling tasks were evaluated using the singular value decomposition (SVD) technique. SVD analysis decomposed each lifting tasks into primitive motion patterns called eigenposture progression (EP) that contributed to the overall task. Biomechanical metrics, such as total joint displacement, were calculated for each EP. The first EP (a simultaneous knee, hip, and waist movement) had greater biomechanical demands than other EPs. Thus, tasks such as lifting from the floor were identified as “riskier” by having a greater composition of the first EP. The results of this work can be used to improve a task as well as spacesuit design by minimizing riskier movement patterns as shown in this case study. This methodology can be applied in civilian occupational settings to analyze open-ended tasks (e.g., complex product assembly and construction) for ergonomics assessments. Using this method, worker task strategies can be evaluated quantitatively, compared, and redesigned when necessary. TECHNICAL ABSTRACT Background Astronauts will perform manual materials handling tasks during future Lunar and Martian exploration missions. Wearing a spacesuit will change lifting kinematics, which could lead to increased musculoskeletal stresses. Thus, it is important to understand how suited motion patterns affect injury risk. Purpose The objective of this study was to use the singular value decomposition (SVD) technique to assess movement differences between lifting techniques in a spacesuit with respect to biomechanical risk factors. Methods Joint angles were derived from motion capture data of lifting tasks performed in the MK-III spacesuit. SVD was performed on the joint angles, extracting the common patterns (“eigenposture progressions”) across each task and their weightings as a function of time. Biomechanical risk factors such as total joint displacement, moments at the low back waist joint, and stability metrics were calculated for each eigenposture progression (EP). These metrics were related back to each task and compared. Results The resulting EPs represented characteristic motions that composed each task. For example, the first eigenposture progression (EP1) was identified as waist, hip, and knee motions and the second eigenposture progression (EP2) was described as arm motions. EPs were coupled with different levels of biomechanical stresses, such that EP1 resulted in the greatest amount of joint displacement and low back moment compared to the other EPs. Tasks such as lifting from the floor were identified as “riskier” due to a higher composition of EP1. Differences in EP weightings were also observed across subjects with varying levels of suited experience. Conclusions The linear factorial analysis, combined with biomechanical stress variables, demonstrated an easy and consistent approach to assess injury risk by relating risk to derived EPs and motions. As shown ","PeriodicalId":73332,"journal":{"name":"IISE transactions on occupational ergonomics and human factors","volume":"9 1","pages":"134 - 142"},"PeriodicalIF":0.0,"publicationDate":"2021-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44939006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Collision Avoidance Algorithm for Human Motion Prediction Based on Perceived Risk of Collision: Part 1-Model Development","authors":"Jie Yang, Brad M. Howard, Juan Baus","doi":"10.1080/24725838.2021.1973613","DOIUrl":"https://doi.org/10.1080/24725838.2021.1973613","url":null,"abstract":"OCCUPATIONAL APPLICATIONS Digital human models have been widely used in occupational biomechanics assessments to prevent potential injury risks, such as automotive assembly lines, box lifting, patient repositioning, and the mining industry. Motion prediction is one of the important capabilities in digital human models, and collision avoidance is involved in human motion prediction. We propose an algorithm that will ensure human motions are predicted realistically, and finally, use of this algorithm could help enhance the accuracy of injury risk assessments using digital human models. TECHNICAL ABSTRACT Background: Humans perform daily tasks such as reaching around an obstacle with ease, even though the complexities of such behavior are largely hidden from those performing them. Optimization-based motion prediction has employed numerical methods in order to predict human movements. However, these movements are heavily constrained, such that the planning of the motion is explicitly provided in the optimization formulation of the problem. This implies that for each task a unique optimization formulation is needed, which relies heavily on the experience of the code developer to provide these constraints. Purpose: Cognitive psychology has focused on the reasoning or motivation behind the planning of movements and provides an opportunity for digital human modeling to adopt these theories to provide a more general or versatile motion prediction framework. Humans tend to overestimate the risk associated with colliding with objects during movement. We present the formulation of a collision avoidance algorithm that considers the perceived risk, for future use in a human motion prediction application. Methods: An experiment was completed to evaluate human performance when avoiding obstacles during movement. Using Bayesian inference, perceived risk was modeled and minimized for use in human motion prediction. Results: The experimental results were used to derive a formula in which the perceived risk associated with the task could be quantified in a gain/loss context. Overestimation of risk by a subject was modeled using the observed behavior and the results of simulations based on the parameterized risk model are presented. Conclusions: The algorithm presented, based on the perceived risk of collision, can be integrated into human motion prediction to generate realistic human motion considering collision avoidance.","PeriodicalId":73332,"journal":{"name":"IISE transactions on occupational ergonomics and human factors","volume":"9 1","pages":"199 - 210"},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44957919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Guidelines of the German Association of Engineers for Evaluating Human Work in the Digital Factory","authors":"G. Zülch","doi":"10.1080/24725838.2021.1966684","DOIUrl":"https://doi.org/10.1080/24725838.2021.1966684","url":null,"abstract":"OCCUPATIONAL APPLICATIONS Starting with the first part of a guideline in 2001, a group of experts and the associated guideline committee of the German Association of Engineers (VDI) began with the production-logistical evaluation of human work related to simulation procedures and Digital Factory tools. This VDI guideline 3633 part 6 dealt with the first macro-ergonomic procedures available. Published in 2015, in VDI guideline 4499 part 4, micro-ergonomic problems related to human stress from work task were added. Nowadays, the still missing micro-ergonomic evaluation of effects by the work environment is on the way to be published as VDI guideline 4499 part 5. This article deals with these developments, which are considered state of the art in Germany at least at the time of their publication. In addition, some examples illustrate proven possibilities, but existing gaps are also discussed. TECHNICAL ABSTRACT Background Nowadays, simulation procedures and Digital Factory tools represent an essential part of planning production resources. Discrete event-driven simulation mainly concentrates on their production logistics evaluation. The increasing development of Digital Human Models brings ergonomic aspects into focus. These methods take into account the stress from the work task, but not from the work environment. Purpose Using simulation methods, little attention has been paid to macro-ergonomic analyzes. Micro-ergonomic human models primarily consider anthropometric and work-physiological aspects, but hardly any work-psychological or work-sociological issues. In addition, there is a lack of software procedures for evaluating the work environment in the Digital Factory. The purpose of this article is to summarize the achievements and to show existing gaps. Methods For developing a guideline part, an expert group of the German Association of Engineers (VDI) commissions a specific guideline committee. After approval by the expert group, the editorial processing is carried out by the VDI organization. Only then does a preliminary publication take place and, after any objections have been dealt with, the final VDI guideline part will be issued. Results VDI guidelines represent the state of the art in Germany, but do not have the status of a standard. The first guideline VDI 3633 part 6 dealt with the modeling of working humans in simulation procedures. In 2015, it was followed by the guideline VDI 4499 part 4, which was dedicated to ergonomic modeling of humans in the Digital Factory. The guideline VDI 4499 part 5 is currently about to be published. Its subject is the prediction of environmental influences on working humans. Conclusions The guideline parts developed show that there are still a major number of questions that require further research. The article briefly summarizes the knowledge gained.","PeriodicalId":73332,"journal":{"name":"IISE transactions on occupational ergonomics and human factors","volume":"9 1","pages":"223 - 232"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48978234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Review and Qualitative Meta-Analysis of Digital Human Modeling and Cyber-Physical-Systems in Ergonomics 4.0","authors":"Gunther Paul, N. D. Abele, K. Kluth","doi":"10.1080/24725838.2021.1966130","DOIUrl":"https://doi.org/10.1080/24725838.2021.1966130","url":null,"abstract":"Occupational Applications Founded in an empirical case study and theoretical work, this paper reviews the scientific literature to define the role of Digital Human Modeling (DHM), Digital Twin (DT), and Cyber-Physical Systems (CPS) to inform the emerging concept of Ergonomics 4.0. We find that DHM evolved into DT is a core element in Ergonomics 4.0. A solid understanding and agreement on the nature of Ergonomics 4.0 is essential for the inclusion of ergonomic values and considerations in the larger conceptual framework of Industry 4.0. In this context, we invite Ergonomists from various disciplines to broaden their understanding and application of DHM and DT. Technical Abstract Background Industry 4.0 presents itself as an ecosystem; a collection of elements endowed with Cyber-Physical Systems and Augmented Reality/Virtual Reality devices, which are connected through the Internet of Things, and uploaded to Cloud Platforms for analysis, knowledge extraction, and diagnostics through Cognitive Computing based on a large amount of data. The concept is centered around data: managing data, analyzing data, and controlling data. Many factors influence this interconnected working environment, and for that reason planning and implementing the digital transformation implies many challenges. Industry 4.0 and Ergonomics are being integrated using a variety of tools and approaches, thus supporting the development of an Ergonomics 4.0 concept. Purpose This paper reviews studies focusing on the determinants of Ergonomics 4.0, identifying the main elements and their interrelationships with a focus on Digital Human Modeling and Cyber-Physical Systems. We consider approaches such as Operator 4.0 and ‘Modeling and Simulation for Digital Twin Creation’, which aim to accelerate the decision-making and adaptation processes. We identify the leading technologies, operations, and worker-related aspects through a qualitative meta-analysis, to establish elements and interrelationships of Ergonomics 4.0 determinants. Methods Literature was selected from articles recent journal publications, and a qualitative evaluation was performed using semantic meta-analysis. The findings were then used to develop a theoretical taxonomy of determinants of Ergonomics 4.0 in Industry 4.0 based on various classifiers, which were structured and interlinked. Results The five areas categorized include: Industry 4.0 technology, Human-Cyber-Physical Systems, Operator 4.0, Human-Robot collaboration, Digital Twin and Digital Human Modeling. The proposed conceptual framework for Ergonomics 4.0 describes processes, technology, information, and structures, which occur in Industry 4.0 as Operator 4.0, Human-Robot collaboration, Digital Twin, Digital Human Modeling and eventually define Ergonomics 4.0. The concepts of Digital Twin and Digital Human Modeling are analyzed in detail, as they form the core of Ergonomics 4.0. Conclusions We propose a conceptual framework for Ergonomics 4.0 as a Cyber-Physica","PeriodicalId":73332,"journal":{"name":"IISE transactions on occupational ergonomics and human factors","volume":"9 1","pages":"111 - 123"},"PeriodicalIF":0.0,"publicationDate":"2021-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42551898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Model-Based Comparison of Passive and Active Assistance Designs in an Occupational Upper Limb Exoskeleton for Overhead Lifting","authors":"Xianlian Zhou, Liying Zheng","doi":"10.1080/24725838.2021.1954565","DOIUrl":"https://doi.org/10.1080/24725838.2021.1954565","url":null,"abstract":"OCCUPATIONAL APPLICATIONS In recent years, various upper limb exoskeletons have been developed aiming to support industrial workers for a range of tasks and reduce risks of work-related musculoskeletal disorders. Most commercially available upper limb exoskeletons are passive systems that use compliant elements such as springs or elastic components to store and release energy to assist the user’s motion. In contrast, many active exoskeletons, which are typically comprised of one or more powered actuators to provide joint assistance, are still in the research and development stages. Nevertheless, the functions and efficacy of various exoskeleton systems need to be further compared and assessed. This study presents a model-based approach to evaluate different designs of passive and active assistance and demonstrates the benefits of both assistance methods in an overhead lifting task. In addition, the modeling and simulation indicate the potential advantages of using the active assistance, based on electromyography. TECHNICAL ABSTRACT Background: In the literature, efficacy of passive upper limb exoskeletons has been demonstrated in reduced activity of involved muscles during overhead occupational tasks. However, there are fewer studies that have investigated the efficacy of active upper limb exoskeletons or compared them with their passive counterparts. Purpose: We aimed to use an approach simulating human-exoskeleton interactions to compare several passive and active assistance methods in an upper limb exoskeleton and to evaluate how different assistance types affect musculoskeletal loadings during overhead lifting. Methods: An upper-extremity musculoskeletal model was integrated with a five degree-of-freedom exoskeleton for virtual human-in-the-loop evaluation of exoskeleton design and control. Different assistance methods were evaluated, including spring-based activation zones and active control based on EMG, to examine their biomechanical effects on musculoskeletal loadings including interaction forces and moments, muscle activations, and joint moments and reaction forces. Results: Our modeling and simulation results suggest the effectiveness of the proposed passive and active assistance methods in reducing biomechanical loadings—the upper-limb exoskeletons could reduce maximum loading on the shoulder joint by up to 46% compared to the no-exoskeleton situation. Active assistance was found to outperform the passive assistance approach. Specifically, EMG-based active assistance could assist over the whole lifting range and had a larger capability to reduce deltoid muscle activation and shoulder joint reaction force. Conclusions: We used a modeling and simulation approach to virtually evaluate various exoskeleton assistance methods without testing multiple physical prototypes and to investigate the effects of these methods on musculoskeletal loadings that cannot be measured directly or noninvasively. Our findings offer new approaches for testing m","PeriodicalId":73332,"journal":{"name":"IISE transactions on occupational ergonomics and human factors","volume":"9 1","pages":"167 - 185"},"PeriodicalIF":0.0,"publicationDate":"2021-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41514135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}