Pub Date : 2021-12-24DOI: 10.1080/24725838.2021.2018372
Vernnaliz Carrasquillo, T. Armstrong, S. J. Hu
OCCUPATIONAL APPLICATIONS Motion analysis of three workers at a large hospital kitchen was conducted using video recordings as part of this case study. Workers were observed during both a high-demand period and a low-demand period to evaluate their exposure to physical risk factors for work-related musculoskeletal disorders. On average, workers’ reaching posture did not change significantly with customer demand. However, recovery time decreased by 18% and hand activity level (HAL) increased by 27% when customer demand increased. On an individual basis, the only worker whose work pace was constrained by processing (cooking) time and the availability of materials to complete the tasks had the most recovery time and did not show an increase in HAL even with an increase in demand. These results suggest the importance of designing tasks that are paced externally (e.g., cooking time) in a self-paced operation to limit the reduction in recovery time and increase in HAL as demand increases.
{"title":"Field Observation of Hospital Food Service Workers and the Relationship between Customer Demand and Biomechanical Stress: A Case Study","authors":"Vernnaliz Carrasquillo, T. Armstrong, S. J. Hu","doi":"10.1080/24725838.2021.2018372","DOIUrl":"https://doi.org/10.1080/24725838.2021.2018372","url":null,"abstract":"OCCUPATIONAL APPLICATIONS Motion analysis of three workers at a large hospital kitchen was conducted using video recordings as part of this case study. Workers were observed during both a high-demand period and a low-demand period to evaluate their exposure to physical risk factors for work-related musculoskeletal disorders. On average, workers’ reaching posture did not change significantly with customer demand. However, recovery time decreased by 18% and hand activity level (HAL) increased by 27% when customer demand increased. On an individual basis, the only worker whose work pace was constrained by processing (cooking) time and the availability of materials to complete the tasks had the most recovery time and did not show an increase in HAL even with an increase in demand. These results suggest the importance of designing tasks that are paced externally (e.g., cooking time) in a self-paced operation to limit the reduction in recovery time and increase in HAL as demand increases.","PeriodicalId":73332,"journal":{"name":"IISE transactions on occupational ergonomics and human factors","volume":"10 1","pages":"47 - 58"},"PeriodicalIF":0.0,"publicationDate":"2021-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49070760","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}
Pub Date : 2021-12-07DOI: 10.1080/24725838.2021.2015642
Vanessa J. Ramirez, B. Bazrgari, F. Gao, M. Samaan
OCCUPATIONAL APPLICATIONS Heavy deadlifting is used as a screening tool or training protocol for recruitment and retention in physically-demanding occupations, especially in the military. Spinal loads experienced during heavy deadlifts, particularly shearing forces, are well above recommended thresholds for lumbar spine injury in occupational settings. Although members of the noted occupation likely have stronger musculoskeletal systems compared to the general population, experiencing shearing forces that are 2 to 4 times larger than the threshold of injury, particularly under repetitive deadlift, may transform a screening tool or training protocol to an occupationally-harmful physical activity. TECHNICAL ABSTRACT Background: Low back pain is a significant problem and one of the primary musculoskeletal conditions affecting active duty service members. There is a need to comprehensively assess the effects of repetitive deadlifts as a physical training modality on lumbar spine loads and the potential mechanisms involved in lumbosacral injuries among soldiers. Purpose: The purpose of this narrative review is to summarize studies of low back biomechanics during repetitive deadlifts as used in training programs to improve lifting capacity. Methods: PubMed and Google Scholar were searched for studies of lifting that met our inclusion and exclusion criteria. Only full text articles in English were included, and their reference lists were further searched. Results: Heavy deadlifts can result in large compressive and shearing spinal loads that range from 5 − 18 kN, and 1.3 − 3.2 kN, respectively. No studies of lower back biomechanics during repetitive deadlifts were found. However, findings of studies that investigated lower back biomechanics during other types of repetitive lifting suggest a high likelihood for adverse changes in lower back biomechanics that can increase risk of lower back injury. Conclusion: Repetitive deadlifting is increasingly implemented as a training modality to develop maximal lifting capacities required in military occupations. Further research is needed to understand the effects of such a training modality on lower back biomechanics and risk of injury.
{"title":"Low Back Biomechanics during Repetitive Deadlifts: A Narrative Review","authors":"Vanessa J. Ramirez, B. Bazrgari, F. Gao, M. Samaan","doi":"10.1080/24725838.2021.2015642","DOIUrl":"https://doi.org/10.1080/24725838.2021.2015642","url":null,"abstract":"OCCUPATIONAL APPLICATIONS Heavy deadlifting is used as a screening tool or training protocol for recruitment and retention in physically-demanding occupations, especially in the military. Spinal loads experienced during heavy deadlifts, particularly shearing forces, are well above recommended thresholds for lumbar spine injury in occupational settings. Although members of the noted occupation likely have stronger musculoskeletal systems compared to the general population, experiencing shearing forces that are 2 to 4 times larger than the threshold of injury, particularly under repetitive deadlift, may transform a screening tool or training protocol to an occupationally-harmful physical activity. TECHNICAL ABSTRACT Background: Low back pain is a significant problem and one of the primary musculoskeletal conditions affecting active duty service members. There is a need to comprehensively assess the effects of repetitive deadlifts as a physical training modality on lumbar spine loads and the potential mechanisms involved in lumbosacral injuries among soldiers. Purpose: The purpose of this narrative review is to summarize studies of low back biomechanics during repetitive deadlifts as used in training programs to improve lifting capacity. Methods: PubMed and Google Scholar were searched for studies of lifting that met our inclusion and exclusion criteria. Only full text articles in English were included, and their reference lists were further searched. Results: Heavy deadlifts can result in large compressive and shearing spinal loads that range from 5 − 18 kN, and 1.3 − 3.2 kN, respectively. No studies of lower back biomechanics during repetitive deadlifts were found. However, findings of studies that investigated lower back biomechanics during other types of repetitive lifting suggest a high likelihood for adverse changes in lower back biomechanics that can increase risk of lower back injury. Conclusion: Repetitive deadlifting is increasingly implemented as a training modality to develop maximal lifting capacities required in military occupations. Further research is needed to understand the effects of such a training modality on lower back biomechanics and risk of injury.","PeriodicalId":73332,"journal":{"name":"IISE transactions on occupational ergonomics and human factors","volume":"10 1","pages":"34 - 46"},"PeriodicalIF":0.0,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60128623","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}
Pub Date : 2021-11-29DOI: 10.1080/24725838.2021.2005184
Sarah C. Griffin, Sarah L. Hemler, K. Beschorner
OCCUPATIONAL APPLICATIONS We investigated the association between shoe wear rate and several metrics describing an individual’s spatiotemporal gait characteristics (cadence, step length, and preferred walking speed). No associations were found, indicating that alternative metrics should be investigated to predict the individualized rate at which workers wear down shoe tread. TECHNICAL ABSTRACT Background Shoe wear has been associated with increased slips and falls in the workplace. People wear down shoe tread at different rates; therefore, individualized shoe replacement timelines could improve resource targeting for organizations that use time as a basis for shoe replacement. Previous work has found that the shoe-floor kinetics, such as the friction requirements of walking, correlate with shoe wear rate. The use of easily measured metrics such as cadence, step length, or preferred walking speed to predict wear has not yet been investigated despite their relationship with friction requirements. Purpose This study seeks to determine the association between shoe wear rate and gait spatiotemporal characteristics. Methods Thirteen participants completed a longitudinal shoe wear study that consisted of a gait assessment followed by prolonged shoe wear in two pairs of slip-resistant shoes. The gait assessment was comprised of dry level-ground walking trials; kinematic and kinetic data were collected through optical motion capture and force plates. The participants’ mean cadence, step length, and preferred walking speed were calculated. The participants then wore their shoes at work; the shoe wear rate was determined by measuring the periodic volumetric tread loss during this wear-at-work portion of the study. Results Three linear regression models found no significant association between the chosen gait metrics and the shoe wear rate. Conclusions The lack of an association between the spatiotemporal gait characteristics and shoe wear rate indicates that these factors may not explain the differences in wear rate between participants. This negative finding suggests that other measures such as the required coefficient of friction are better for individualizing footwear replacement guidelines.
{"title":"Investigating the Influence of Spatiotemporal Gait Characteristics on Shoe Wear Rate","authors":"Sarah C. Griffin, Sarah L. Hemler, K. Beschorner","doi":"10.1080/24725838.2021.2005184","DOIUrl":"https://doi.org/10.1080/24725838.2021.2005184","url":null,"abstract":"OCCUPATIONAL APPLICATIONS We investigated the association between shoe wear rate and several metrics describing an individual’s spatiotemporal gait characteristics (cadence, step length, and preferred walking speed). No associations were found, indicating that alternative metrics should be investigated to predict the individualized rate at which workers wear down shoe tread. TECHNICAL ABSTRACT Background Shoe wear has been associated with increased slips and falls in the workplace. People wear down shoe tread at different rates; therefore, individualized shoe replacement timelines could improve resource targeting for organizations that use time as a basis for shoe replacement. Previous work has found that the shoe-floor kinetics, such as the friction requirements of walking, correlate with shoe wear rate. The use of easily measured metrics such as cadence, step length, or preferred walking speed to predict wear has not yet been investigated despite their relationship with friction requirements. Purpose This study seeks to determine the association between shoe wear rate and gait spatiotemporal characteristics. Methods Thirteen participants completed a longitudinal shoe wear study that consisted of a gait assessment followed by prolonged shoe wear in two pairs of slip-resistant shoes. The gait assessment was comprised of dry level-ground walking trials; kinematic and kinetic data were collected through optical motion capture and force plates. The participants’ mean cadence, step length, and preferred walking speed were calculated. The participants then wore their shoes at work; the shoe wear rate was determined by measuring the periodic volumetric tread loss during this wear-at-work portion of the study. Results Three linear regression models found no significant association between the chosen gait metrics and the shoe wear rate. Conclusions The lack of an association between the spatiotemporal gait characteristics and shoe wear rate indicates that these factors may not explain the differences in wear rate between participants. This negative finding suggests that other measures such as the required coefficient of friction are better for individualizing footwear replacement guidelines.","PeriodicalId":73332,"journal":{"name":"IISE transactions on occupational ergonomics and human factors","volume":"10 1","pages":"1 - 6"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47833145","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}
Pub Date : 2021-11-11DOI: 10.1080/24725838.2021.2005720
T. Schmalz, Anja Colienne, Emily A. Bywater, L. Fritzsche, C. Gärtner, M. Bellmann, Samuel M. F. Reimer, M. Ernst
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.
{"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}
Pub Date : 2021-10-02DOI: 10.1080/24725838.2021.2004265
James Yang, Brad M. Howard, Juan Baus
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.
{"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}
Pub Date : 2021-10-02DOI: 10.1080/24725838.2021.1997834
Aitor Iriondo Pascual, D. Högberg, Dan Lämkull, E. Perez Luque, Anna Syberfeldt, L. Hanson
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.
{"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}
Pub Date : 2021-10-02DOI: 10.1080/24725838.2021.2027508
Gunther Paul, Xuguang Wang, James Yang
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.
{"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}
Pub Date : 2021-10-02DOI: 10.1080/24725838.2021.2021458
S. Almosnino, Jessica Cappelletto
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.
我们提出了一种实用的方法来最小化腰背累积负荷，该方法利用数字人体建模能力和使用进化算法进行优化。我们在模拟的起重任务中演示了该方法的使用。结果表明，该方法对不同的累积荷载计算例程具有较强的鲁棒性。所提出的方法可以帮助人体工程学工程师在设计阶段早期解决潜在的风险因素，即使在没有确定阈值的情况下，它也可以通过消除在许多设计可能性中调整工作场所参数的需要来节省时间。技术摘要背景:过度暴露于腰背累积负荷(LBCL)已被认为是在手工搬运(MMH)任务中发生疼痛或损伤的危险因素。然而，由于缺乏一个公认的、被广泛接受的LBCL阈值，在概念工程设计期间解决LBCL问题是具有挑战性的。因此，我们使用数字人体建模(DHM)辅助的优化框架来制定设计挑战。方法我们构建了一个假设的MMH任务，要求将箱子抬起、搬运和放置到16个存储位置。由四种不同质量类别组成的外部负载处理250次，有四种不同的相对处理频率。根据建议的四种方法对得到的低背压缩力时间序列进行积分。在此基础上，定义了目标函数和约束条件，并采用进化算法求解。结果4种不同的相对处理频率和综合方法的符合率在89.5% ~ 100%之间。Kendall’s coefficient of concordance值在0.74 ~ 1.0之间，表明解决方案之间的一致性很好到完全。结论:最小化LBCL暴露是有益的，尤其是在任务设计阶段。我们的研究结果表明，无论与LBCL量化相关的理论背景如何，所提出的方法都能产生一个鲁棒且基本相似的解决方案，至少对于我们模拟的MMH场景而言是如此。我们提出的方法利用DHM功能来模拟各种MMH场景，并在概念设计阶段提供解决方案估计。所提出的方法可以使用多目标优化方案和附加约束进行扩展，以提供解决多种损伤和疲劳途径的解决方案。
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Pub Date : 2021-10-02DOI: 10.1080/24725838.2021.1972056
Linh Q. Vu, K. H. Kim, S. Rajulu
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
{"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}
Pub Date : 2021-09-16DOI: 10.1080/24725838.2021.1973613
Jie Yang, Brad M. Howard, Juan Baus
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.
{"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}
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