DHM tools have been widely used to analyze and improve vehicle occupant packaging and interior design in the automotive industry. However, these tools still present some limitations for this application. Accurately characterizing seated posture is crucial for ergonomic and safety evaluations. Current human posture and motion predictions in DHM tools are not accurate enough for the precise nature of vehicle interior design, typically requiring manual adjustments from DHM users to get more accurate driving and passenger simulations. Manual adjustment processes can be time-consuming, tedious, and subjective, easily causing non-repeatable simulation results. These limitations create the need to validate the simulation results with real-world studies, which increases the cost and time in the vehicle development process. Working with multiple Swedish automotive companies, we have begun to identify and specify the limitations of DHM tools relating to driver and passenger posture predictions given predefined vehicle geometry points/coordinates and specific human body parts relationships. Two general issues frame the core limitations. First, human kinematic models used in DHM tools are based on biomechanics models that do not provide definitions of these models in relation to vehicle geometries. Second, vehicle designers follow standards and regulations to obtain key human reference points in seated occupant locations. However, these reference points can fail to capture the range of human variability. This paper describes the relationship between a seated reference point and a biomechanical hip joint for driving simulations. The lack of standardized connection between occupant packaging guidelines and the biomechanical knowledge of humans creates a limitation for ergonomics designers and DHM users. We assess previous studies addressing hip joint estimation from different fields to establish the key aspects that might affect the relationship between standard vehicle geometry points and the hip joint. Then we suggest a procedure for standardizing points in human models within DHM tools. A better understanding of this problem may contribute to achieving closer to reality driving posture simulations and facilitating communication of ergonomics requirements to the design team within the product development process.
{"title":"Simulation of hip joint location for occupant packaging design","authors":"E. Perez Luque, E. Brolin, M. Lamb, D. Högberg","doi":"10.17077/dhm.31742","DOIUrl":"https://doi.org/10.17077/dhm.31742","url":null,"abstract":"DHM tools have been widely used to analyze and improve vehicle occupant packaging and interior design in the automotive industry. However, these tools still present some limitations for this application. Accurately characterizing seated posture is crucial for ergonomic and safety evaluations. Current human posture and motion predictions in DHM tools are not accurate enough for the precise nature of vehicle interior design, typically requiring manual adjustments from DHM users to get more accurate driving and passenger simulations. Manual adjustment processes can be time-consuming, tedious, and subjective, easily causing non-repeatable simulation results. These limitations create the need to validate the simulation results with real-world studies, which increases the cost and time in the vehicle development process. Working with multiple Swedish automotive companies, we have begun to identify and specify the limitations of DHM tools relating to driver and passenger posture predictions given predefined vehicle geometry points/coordinates and specific human body parts relationships. Two general issues frame the core limitations. First, human kinematic models used in DHM tools are based on biomechanics models that do not provide definitions of these models in relation to vehicle geometries. Second, vehicle designers follow standards and regulations to obtain key human reference points in seated occupant locations. However, these reference points can fail to capture the range of human variability. This paper describes the relationship between a seated reference point and a biomechanical hip joint for driving simulations. The lack of standardized connection between occupant packaging guidelines and the biomechanical knowledge of humans creates a limitation for ergonomics designers and DHM users. We assess previous studies addressing hip joint estimation from different fields to establish the key aspects that might affect the relationship between standard vehicle geometry points and the hip joint. Then we suggest a procedure for standardizing points in human models within DHM tools. A better understanding of this problem may contribute to achieving closer to reality driving posture simulations and facilitating communication of ergonomics requirements to the design team within the product development process.","PeriodicalId":111717,"journal":{"name":"Proceedings of the 7th International Digital Human Modeling Symposium (DHM 2022) and Iowa Virtual Human Summit 2022 -","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122367534","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}
The concept formation of Human Digital Twin (HDT), including basic considerations, construct of HDT, and the features of HDT is presented in this paper. The steps and methods for HDT construction are described, which include template human model creation, human model unification, individualization, data links with data driven models, and integration. Two projects where HDT are used for practical problems are introduced. This basic the Twin (HDT) developed at The developed by Innovision is based on individualized human models combined with personal data containers linked to sensors. The data can be processed using analytics to provide an integrated, dynamic representation of one’s personal physical and physiological states. Combined with physics solvers, a HDT can be used to perform physics-based analysis, simulation, and prediction of physical and physiological performance of an individual (Cheng et 2020).
本文介绍了人体数字孪生体(HDT)的概念形成，包括HDT的基本考虑、HDT的构成以及HDT的特点。描述了HDT构建的步骤和方法，包括模板人体模型创建、人体模型统一、个性化、与数据驱动模型的数据链接以及集成。介绍了HDT用于解决实际问题的两个工程。由Innovision公司开发的这种基本的孪生(HDT)是基于个性化的人体模型，结合与传感器相连的个人数据容器。这些数据可以通过分析来处理，以提供个人身体和生理状态的综合动态表示。结合物理求解器，HDT可用于执行基于物理的分析、模拟和预测个人的物理和生理表现(Cheng et 2020)。
{"title":"Human digital twin with applications","authors":"Zhiqing Cheng","doi":"10.17077/dhm.31783","DOIUrl":"https://doi.org/10.17077/dhm.31783","url":null,"abstract":"The concept formation of Human Digital Twin (HDT), including basic considerations, construct of HDT, and the features of HDT is presented in this paper. The steps and methods for HDT construction are described, which include template human model creation, human model unification, individualization, data links with data driven models, and integration. Two projects where HDT are used for practical problems are introduced. This basic the Twin (HDT) developed at The developed by Innovision is based on individualized human models combined with personal data containers linked to sensors. The data can be processed using analytics to provide an integrated, dynamic representation of one’s personal physical and physiological states. Combined with physics solvers, a HDT can be used to perform physics-based analysis, simulation, and prediction of physical and physiological performance of an individual (Cheng et 2020).","PeriodicalId":111717,"journal":{"name":"Proceedings of the 7th International Digital Human Modeling Symposium (DHM 2022) and Iowa Virtual Human Summit 2022 -","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130049714","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}
Collision detection and distance computation algorithms often form the bottlenecks of many digital human modelling simulations in industrial processes. When designing vehicle assembly lines or cobot assembly cells it is essential to be able to accurately simulate collision free interactions both for efficient and safe operations. Hence, any attempt to improve such algorithms can have a broad and significant impact. Most of the focus is typically on speeding up the queries, however, with models becoming larger as scenarios become more realistic and simulations include more elements such as musculoskeletal models and 3D human body modelling, other parts of the proximity query performance are becoming important such as the management of memory. In this paper, we demonstrate a new technique called ME-BVH (Memory Efficient Bounding Volume Hierarchies) to improve memory usage for proximity queries with bounding volume hierarchies. The approach utilizes a simple and effective way of grouping primitives together at the leaf level and building the bounding volume hierarchy top down to the grouped primitive leaves. The paper then shows ways of efficiently carrying out primitive and bounding volume queries to offset the greater number of potential queries. In addition, the modifications taken are simple enough to be easily applied to most bounding volume hierarchies. By using these approaches, we demonstrate on a number of real-life assembly scenarios with millions of primitives that, compared to existing approaches, our proposed method is able to save up to half of the memory used and can reduce the build times at little cost to the query performance. In addition, the methods developed here are compatible with all BVH types and queries used in ergonomic simulations, unlike many other approaches. The developed algorithms present advantages for proximity queries for deformable meshes used in digital human modelling by reducing the time it takes to build a bounding volume hierarchy which often must be rebuilt or updated many times during simulations due to mesh deformations.
{"title":"ME-BVH: Memory Efficient Bounding Volume Hierarchies","authors":"E. Shellshear, Yi Li, J. Carlson","doi":"10.17077/dhm.31791","DOIUrl":"https://doi.org/10.17077/dhm.31791","url":null,"abstract":"Collision detection and distance computation algorithms often form the bottlenecks of many digital human modelling simulations in industrial processes. When designing vehicle assembly lines or cobot assembly cells it is essential to be able to accurately simulate collision free interactions both for efficient and safe operations. Hence, any attempt to improve such algorithms can have a broad and significant impact. Most of the focus is typically on speeding up the queries, however, with models becoming larger as scenarios become more realistic and simulations include more elements such as musculoskeletal models and 3D human body modelling, other parts of the proximity query performance are becoming important such as the management of memory. In this paper, we demonstrate a new technique called ME-BVH (Memory Efficient Bounding Volume Hierarchies) to improve memory usage for proximity queries with bounding volume hierarchies. The approach utilizes a simple and effective way of grouping primitives together at the leaf level and building the bounding volume hierarchy top down to the grouped primitive leaves. The paper then shows ways of efficiently carrying out primitive and bounding volume queries to offset the greater number of potential queries. In addition, the modifications taken are simple enough to be easily applied to most bounding volume hierarchies. By using these approaches, we demonstrate on a number of real-life assembly scenarios with millions of primitives that, compared to existing approaches, our proposed method is able to save up to half of the memory used and can reduce the build times at little cost to the query performance. In addition, the methods developed here are compatible with all BVH types and queries used in ergonomic simulations, unlike many other approaches. The developed algorithms present advantages for proximity queries for deformable meshes used in digital human modelling by reducing the time it takes to build a bounding volume hierarchy which often must be rebuilt or updated many times during simulations due to mesh deformations.","PeriodicalId":111717,"journal":{"name":"Proceedings of the 7th International Digital Human Modeling Symposium (DHM 2022) and Iowa Virtual Human Summit 2022 -","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133384968","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}
M. Spitzhirn, Sascha Ullman, Sebastian Bauer, L. Fritzsche
For planning and designing production and work systems, a holistic approach is necessary that considers both levels of factory planning and workplace design. Currently, separate digital tools are mostly used for the design of factories and the detailed planning of work systems. That leads to workers being considered inadequately or too late in the planning process of production. The consequence can be a time-consuming and costly replanning to solve problems in existing production and work processes. Using the example of an assembly of washing machines, an iterative approach is presented for a combined digital planning on factory and workplace level. A holistic design of the assembly line is carried out using the ema Software Suite, consisting of the ema Plant Designer (emaPD) and ema Work Designer (emaWD). In the case study, emaPD is used to optimize production elements such as operating resources, layout, and logistics by considering the material flow, throughput times, and production costs. These results are applied for detailed planning and design at the workstation level with emaWD, which uses an algorithmic approach for self-initiated motion generation based on objective task descriptions. The generated simulations are examined and optimized based on production time estimation (MTM-UAS) and ergonomic risk assessments (EAWS, NIOSH, reach and vision analysis) as well as workers’ abilities (age, anthropometry). As a result, an efficient factory with an optimized material flow could be planned while minimizing the manufacturing costs and throughput times while complying with the space specifications and ergonomics. The takeover of ergonomically unfavorable processes by robots as hybrid workstations enables, among other things, an improvement in ergonomics. The digital planning approach of combined factory (emaPD) and workplace design (emaWD) also enable early, coordinated, efficient planning of economical and ergonomic production.
{"title":"Digital production planning and human simulation of manual and hybrid work processes using the ema Software Suite","authors":"M. Spitzhirn, Sascha Ullman, Sebastian Bauer, L. Fritzsche","doi":"10.17077/dhm.31740","DOIUrl":"https://doi.org/10.17077/dhm.31740","url":null,"abstract":"For planning and designing production and work systems, a holistic approach is necessary that considers both levels of factory planning and workplace design. Currently, separate digital tools are mostly used for the design of factories and the detailed planning of work systems. That leads to workers being considered inadequately or too late in the planning process of production. The consequence can be a time-consuming and costly replanning to solve problems in existing production and work processes. Using the example of an assembly of washing machines, an iterative approach is presented for a combined digital planning on factory and workplace level. A holistic design of the assembly line is carried out using the ema Software Suite, consisting of the ema Plant Designer (emaPD) and ema Work Designer (emaWD). In the case study, emaPD is used to optimize production elements such as operating resources, layout, and logistics by considering the material flow, throughput times, and production costs. These results are applied for detailed planning and design at the workstation level with emaWD, which uses an algorithmic approach for self-initiated motion generation based on objective task descriptions. The generated simulations are examined and optimized based on production time estimation (MTM-UAS) and ergonomic risk assessments (EAWS, NIOSH, reach and vision analysis) as well as workers’ abilities (age, anthropometry). As a result, an efficient factory with an optimized material flow could be planned while minimizing the manufacturing costs and throughput times while complying with the space specifications and ergonomics. The takeover of ergonomically unfavorable processes by robots as hybrid workstations enables, among other things, an improvement in ergonomics. The digital planning approach of combined factory (emaPD) and workplace design (emaWD) also enable early, coordinated, efficient planning of economical and ergonomic production.","PeriodicalId":111717,"journal":{"name":"Proceedings of the 7th International Digital Human Modeling Symposium (DHM 2022) and Iowa Virtual Human Summit 2022 -","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123579234","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}
Evan Gallouin, Xuguang Wang, P. Beillas, T. Bellet
Taking over the manual control of a car after Automated Driving (AD) is a key issue for future road safety. However, performance to resume this manual control may be dependant of the driver’s level of engagement in driving during AD. Indeed, according to the level of automation (from L2 to L3 of the SAE), drivers will be in charge of monitoring the driving situation, or will be allowed to perform non-driving related tasks (NDRT) and thus, to be fully disengaged of the driving task. In this context, the present study aims to investigate the influence of the driver’s level of engagement/disengagement during AD on takeover performance using a driving simulator. Four levels of engagement/disengagement were studied: (C1) being engaged in driving situation monitoring without TakeOver Request (TOR) to resume the manual control, (C2) being engaged in driving situation monitoring with a TOR to resume the manual control, (C3) being disengaged of the driving monitoring by performing a cognitively demanding secondary task with a TOR to resume the manual control, and (C4) being disengaged of the driving monitoring in a relaxed position situation with eyes closed and with a TOR to resume the manual control. Forty participants were performed sixteen critical takeover scenarios involving different critical takeover situations. Drivers reaction times and collision risks were measured to assess their takeover performances and to investigate the safety of automation levels 2 and 3. Driving situation monitoring with a TOR (C2) induce shortest reaction times and a lower number of collisions. For the relaxed posture (C4), drivers took longer time to react than the other three conditions. Driving situation monitoring without TOR (C1), had the highest number of collisions. This suggests that the engagement in driving is not always effective and efficient without TOR. Moreover, being in a relaxed position during automated driving decreases takeover performance.
{"title":"Takeover performance according to the level of disengagement during automated driving","authors":"Evan Gallouin, Xuguang Wang, P. Beillas, T. Bellet","doi":"10.17077/dhm.31754","DOIUrl":"https://doi.org/10.17077/dhm.31754","url":null,"abstract":"Taking over the manual control of a car after Automated Driving (AD) is a key issue for future road safety. However, performance to resume this manual control may be dependant of the driver’s level of engagement in driving during AD. Indeed, according to the level of automation (from L2 to L3 of the SAE), drivers will be in charge of monitoring the driving situation, or will be allowed to perform non-driving related tasks (NDRT) and thus, to be fully disengaged of the driving task. In this context, the present study aims to investigate the influence of the driver’s level of engagement/disengagement during AD on takeover performance using a driving simulator. Four levels of engagement/disengagement were studied: (C1) being engaged in driving situation monitoring without TakeOver Request (TOR) to resume the manual control, (C2) being engaged in driving situation monitoring with a TOR to resume the manual control, (C3) being disengaged of the driving monitoring by performing a cognitively demanding secondary task with a TOR to resume the manual control, and (C4) being disengaged of the driving monitoring in a relaxed position situation with eyes closed and with a TOR to resume the manual control. Forty participants were performed sixteen critical takeover scenarios involving different critical takeover situations. Drivers reaction times and collision risks were measured to assess their takeover performances and to investigate the safety of automation levels 2 and 3. Driving situation monitoring with a TOR (C2) induce shortest reaction times and a lower number of collisions. For the relaxed posture (C4), drivers took longer time to react than the other three conditions. Driving situation monitoring without TOR (C1), had the highest number of collisions. This suggests that the engagement in driving is not always effective and efficient without TOR. Moreover, being in a relaxed position during automated driving decreases takeover performance.","PeriodicalId":111717,"journal":{"name":"Proceedings of the 7th International Digital Human Modeling Symposium (DHM 2022) and Iowa Virtual Human Summit 2022 -","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128313759","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}
When designing the interior of automated cars, it is necessary to take the non-driving related tasks and the take-over maneuver into account. These take-overs are critical moments since the driver needs to take back control of the vehicle as fast as possible. To facilitate this, interior designers need to design the cabin with enough space to carry out this movement. This paper presents a revised modelling approach using mixed linear effects models to predict the grasping movement of the hand during take-over scenarios. A study with 52 participants doing grasping movements was carried out to model the data obtained via motion capture. The participants were instructed to carry out movements from predefined grasping elements mounted in front of them. The trajectory of the hand was recorded using a marker-based motion capturing system. It is observed that the trajectories can be assumed as a two-dimensional phenomenon, since they seem to lie on one plane. Thus, the trajectories were modeled as a 1+2-dimensional problem. A one-dimensional model for the plane and a second two-dimensional model for the trajectory. The model of grasping trajectory described in this paper was modeled using 4 th degree polynomials. In older approaches, the trajectory was modeled in four different models for each constant of the polynomial. In this paper a new modeling approach is used to merge the polynomial into one model. This increased the R² m and R² c drastically and led to three major discoveries on the nature of human grasping movements: Task factors, such as grasping handle and handle position, play the major role in the grasping trajectory. Body height plays a role in the modelling of hand trajectories. Gender, age, and dominant hand show only negligible influence on the trajectory. Other individual human factors not evaluated in this study do not seem to heavily influence the hand movement.
{"title":"Improved modeling approach for the usage of mixed linear effects models in empirical digital human models","authors":"Martin Fleischer","doi":"10.17077/dhm.31786","DOIUrl":"https://doi.org/10.17077/dhm.31786","url":null,"abstract":"When designing the interior of automated cars, it is necessary to take the non-driving related tasks and the take-over maneuver into account. These take-overs are critical moments since the driver needs to take back control of the vehicle as fast as possible. To facilitate this, interior designers need to design the cabin with enough space to carry out this movement. This paper presents a revised modelling approach using mixed linear effects models to predict the grasping movement of the hand during take-over scenarios. A study with 52 participants doing grasping movements was carried out to model the data obtained via motion capture. The participants were instructed to carry out movements from predefined grasping elements mounted in front of them. The trajectory of the hand was recorded using a marker-based motion capturing system. It is observed that the trajectories can be assumed as a two-dimensional phenomenon, since they seem to lie on one plane. Thus, the trajectories were modeled as a 1+2-dimensional problem. A one-dimensional model for the plane and a second two-dimensional model for the trajectory. The model of grasping trajectory described in this paper was modeled using 4 th degree polynomials. In older approaches, the trajectory was modeled in four different models for each constant of the polynomial. In this paper a new modeling approach is used to merge the polynomial into one model. This increased the R² m and R² c drastically and led to three major discoveries on the nature of human grasping movements: Task factors, such as grasping handle and handle position, play the major role in the grasping trajectory. Body height plays a role in the modelling of hand trajectories. Gender, age, and dominant hand show only negligible influence on the trajectory. Other individual human factors not evaluated in this study do not seem to heavily influence the hand movement.","PeriodicalId":111717,"journal":{"name":"Proceedings of the 7th International Digital Human Modeling Symposium (DHM 2022) and Iowa Virtual Human Summit 2022 -","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128990902","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}
Neil Mansfield, Geetika Aggarwal, F. Vanheusden, Steve Faulkner
Comfort in aircraft cabins is influenced by many ergonomic and physical environment factors. For reasons of sustainability, the fleet of future regional passenger aircraft are expected to have an increased proportion that are propeller powered. Current turboprop regional aircraft have a reputation for being noisy and exposing passengers to vibration. Laboratory studies have simulated the aircraft cabin including noise, vibration and thermal stressors and sought subjective responses from volunteers. These data were used to build multi-factorial models of comfort in an aircraft cabin. Two modelling approaches were used: second order polynomial curve fitting allowed for prediction of subjective ratings from measurements of noise and vibration at discrete temperatures. A multi-factorial model including noise, vibration, and thermal parameters was developed using a linear regression machine-learning approach. This model allows for the prediction of subjective responses within a range of noise, vibration, and temperature levels that are experienced in aircraft. This paper presents the development of a model of the human response to noise, vibration and thermal stimuli. The model allows for the prediction of the response to noise, the response to vibration, the response to the thermal environment and the overall discomfort. It also predicts which of the modalities will be most important in terms of human response.
{"title":"Multi-factorial modeling of comfort in an aircraft cabin considering thermal, noise, and vibration metrics","authors":"Neil Mansfield, Geetika Aggarwal, F. Vanheusden, Steve Faulkner","doi":"10.17077/dhm.31787","DOIUrl":"https://doi.org/10.17077/dhm.31787","url":null,"abstract":"Comfort in aircraft cabins is influenced by many ergonomic and physical environment factors. For reasons of sustainability, the fleet of future regional passenger aircraft are expected to have an increased proportion that are propeller powered. Current turboprop regional aircraft have a reputation for being noisy and exposing passengers to vibration. Laboratory studies have simulated the aircraft cabin including noise, vibration and thermal stressors and sought subjective responses from volunteers. These data were used to build multi-factorial models of comfort in an aircraft cabin. Two modelling approaches were used: second order polynomial curve fitting allowed for prediction of subjective ratings from measurements of noise and vibration at discrete temperatures. A multi-factorial model including noise, vibration, and thermal parameters was developed using a linear regression machine-learning approach. This model allows for the prediction of subjective responses within a range of noise, vibration, and temperature levels that are experienced in aircraft. This paper presents the development of a model of the human response to noise, vibration and thermal stimuli. The model allows for the prediction of the response to noise, the response to vibration, the response to the thermal environment and the overall discomfort. It also predicts which of the modalities will be most important in terms of human response.","PeriodicalId":111717,"journal":{"name":"Proceedings of the 7th International Digital Human Modeling Symposium (DHM 2022) and Iowa Virtual Human Summit 2022 -","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126229831","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}
Digital human models are usually constructed to study the human anatomical or topological features and its variance and to optimize the size and shape of various products and tasks. Therefore, most of the researchers focussed on developing accurate three-dimensional digital human models based on surface mesh using various methods and techniques. However, such models do not allow biomechanical and ergonomic analyses of product interface materials that are in direct contact with the user. Based on manual testing using various materials and analysing the subjective response of users, researchers have shown that product interface material has an important impact on the overall product safety, comfort and even performance. Basic ergonomic and biomechanical guidelines regarding the material choice were provided based on the findings, however detailed material choice and even material parameter determination has not been studied, evaluated, and discussed due to the complex biomechanical systems and lack of appropriate digital human models. To overcome these limitations, numerical methods, especially the finite element method has been already used in the past by several authors. Finite element method allows calculating of various results in terms of internal stresses and contact pressure, deformations, and displacements, however it requires accurate development of numerical digital human models that accurately represent the anatomical, topological, material properties and boundary conditions. In this paper we present theoretical background and provide methodology for successful development of numerical digital human models that can be used for biomechanical analyses and product material ergonomic improvement. This is presented with a case study of the development of a numerical digital human finger model for ergonomic improvement of the biomechanical response of a product handle deformable interface material. Based on the developed numerical model, a novel deformable interface material is analysed that reduces the resulting contact pressure during grasping and provides more uniform pressure distribution while still providing sufficient stability.
{"title":"Improving ergonomic value of product interface materials using numerical digital human models","authors":"G. Harih, Vasja Plesec","doi":"10.17077/dhm.31739","DOIUrl":"https://doi.org/10.17077/dhm.31739","url":null,"abstract":"Digital human models are usually constructed to study the human anatomical or topological features and its variance and to optimize the size and shape of various products and tasks. Therefore, most of the researchers focussed on developing accurate three-dimensional digital human models based on surface mesh using various methods and techniques. However, such models do not allow biomechanical and ergonomic analyses of product interface materials that are in direct contact with the user. Based on manual testing using various materials and analysing the subjective response of users, researchers have shown that product interface material has an important impact on the overall product safety, comfort and even performance. Basic ergonomic and biomechanical guidelines regarding the material choice were provided based on the findings, however detailed material choice and even material parameter determination has not been studied, evaluated, and discussed due to the complex biomechanical systems and lack of appropriate digital human models. To overcome these limitations, numerical methods, especially the finite element method has been already used in the past by several authors. Finite element method allows calculating of various results in terms of internal stresses and contact pressure, deformations, and displacements, however it requires accurate development of numerical digital human models that accurately represent the anatomical, topological, material properties and boundary conditions. In this paper we present theoretical background and provide methodology for successful development of numerical digital human models that can be used for biomechanical analyses and product material ergonomic improvement. This is presented with a case study of the development of a numerical digital human finger model for ergonomic improvement of the biomechanical response of a product handle deformable interface material. Based on the developed numerical model, a novel deformable interface material is analysed that reduces the resulting contact pressure during grasping and provides more uniform pressure distribution while still providing sufficient stability.","PeriodicalId":111717,"journal":{"name":"Proceedings of the 7th International Digital Human Modeling Symposium (DHM 2022) and Iowa Virtual Human Summit 2022 -","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125392309","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}
Joseph Alemany, Meghan Garvey, Kristin Gowers, Robyn Highfill-McRoy, Patrick Walsh, Arlington Wilson
{"title":"Multi-modal Event Standardization Platform of Biometric-Derived Human Performance Models in University Students","authors":"Joseph Alemany, Meghan Garvey, Kristin Gowers, Robyn Highfill-McRoy, Patrick Walsh, Arlington Wilson","doi":"10.17077/dhm.31765","DOIUrl":"https://doi.org/10.17077/dhm.31765","url":null,"abstract":"","PeriodicalId":111717,"journal":{"name":"Proceedings of the 7th International Digital Human Modeling Symposium (DHM 2022) and Iowa Virtual Human Summit 2022 -","volume":"9 2-4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123690274","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}
M. Lamb, Seunghun Lee, E. Billing, D. Högberg, James Yang
Posture/motion prediction is the basis of the human motion simulations that make up the core of many digital human modeling (DHM) tools and methods. With the goal of producing realistic postures and motions, a common element of posture/motion prediction methods involves applying some set of constraints to biomechanical models of humans on the positions and orientations of specified body parts. While many formulations of biomechanical constraints may produce valid predictions, they must overcome the challenges posed by the highly redundant nature of human biomechanical systems. DHM researchers and developers typically focus on optimization formulations to facilitate the identification and selection of valid solutions. While these approaches produce optimal behavior according to some, e.g., ergonomic, optimization criteria, these solutions require considerable computational power and appear vastly different from how humans produce motion. In this paper, we take a different approach and consider the Forward and Backward Reaching Inverse Kinematics (FABRIK) solver developed in the context of computer graphics for rigged character animation. This approach identifies postures quickly and efficiently, often requiring a fraction of the computation time involved in optimization-based methods. Critically, the FABRIK solver identifies posture predictions based on a lightweight heuristic approach. Specifically, the solver works in joint position space and identifies solutions according to a minimal joint displacement principle. We apply the FABRIK solver to a 7-degree of freedom human arm model during a reaching task from an initial to an end target location, fixing the shoulder position and providing the end effector (index fingertip) position and orientation from each frame of the motion capture data. In this preliminary study, predicted postures are compared to experimental data from a single human subject. Overall the predicted postures were very near the recorded data, with an average RMSE of 1.67°. Although more validation is necessary, we believe that the FABRIK solver has great potential for producing realistic human posture/motion in real-time, with applications in the area of DHM.
{"title":"Forward and Backwards Reaching Inverse Kinematics (FABRIK) solver for DHM: A pilot study","authors":"M. Lamb, Seunghun Lee, E. Billing, D. Högberg, James Yang","doi":"10.17077/dhm.31772","DOIUrl":"https://doi.org/10.17077/dhm.31772","url":null,"abstract":"Posture/motion prediction is the basis of the human motion simulations that make up the core of many digital human modeling (DHM) tools and methods. With the goal of producing realistic postures and motions, a common element of posture/motion prediction methods involves applying some set of constraints to biomechanical models of humans on the positions and orientations of specified body parts. While many formulations of biomechanical constraints may produce valid predictions, they must overcome the challenges posed by the highly redundant nature of human biomechanical systems. DHM researchers and developers typically focus on optimization formulations to facilitate the identification and selection of valid solutions. While these approaches produce optimal behavior according to some, e.g., ergonomic, optimization criteria, these solutions require considerable computational power and appear vastly different from how humans produce motion. In this paper, we take a different approach and consider the Forward and Backward Reaching Inverse Kinematics (FABRIK) solver developed in the context of computer graphics for rigged character animation. This approach identifies postures quickly and efficiently, often requiring a fraction of the computation time involved in optimization-based methods. Critically, the FABRIK solver identifies posture predictions based on a lightweight heuristic approach. Specifically, the solver works in joint position space and identifies solutions according to a minimal joint displacement principle. We apply the FABRIK solver to a 7-degree of freedom human arm model during a reaching task from an initial to an end target location, fixing the shoulder position and providing the end effector (index fingertip) position and orientation from each frame of the motion capture data. In this preliminary study, predicted postures are compared to experimental data from a single human subject. Overall the predicted postures were very near the recorded data, with an average RMSE of 1.67°. Although more validation is necessary, we believe that the FABRIK solver has great potential for producing realistic human posture/motion in real-time, with applications in the area of DHM.","PeriodicalId":111717,"journal":{"name":"Proceedings of the 7th International Digital Human Modeling Symposium (DHM 2022) and Iowa Virtual Human Summit 2022 -","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114785361","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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