Improving digital human modelling for proactive ergonomics in design

This paper presents the need to improve existing digital human models (DHMs) so they are better able to serve as effective ergonomics analysis and design tools. Existing DHMs are meant to be used by a designer early in a product development process when attempting to improve the physical design of vehicle interiors and manufacturing workplaces. The emphasis in this paper is placed on developing future DHMs that include valid posture and motion prediction models for various populations. It is argued that existing posture and motion prediction models now used in DHMs must be changed to become based on real motion data to assure validity for complex dynamic task simulations. It is further speculated that if valid human posture and motion prediction models are developed and used, these can be combined with psychophysical and biomechanical models to provide a much greater understanding of dynamic human performance and population specific limitations and that these new DHM models will ultimately provide a powerful ergonomics design tool.     

A New Methodology for Human Grasp Prediction

Grasping is an essential requirement for digital human models (DHMs). It is a complex process and thus a challenging problem for DHMs, involving a skeletal structure with many degrees-of-freedom (DOFs), cognition, and interaction between the human and objects in the environment. Furthermore, grasp planning involves not only finding the shape of the hand and the position and orientation of the wrist but also the posture of the upper body required for producing realistic grasping simulations. In this paper, a new methodology is developed for grasping prediction by combining a shape-matching method and an optimization-based posture prediction technique. We use shape matching to pick a hand shape from a database of stored grasps, then position the hand around the object. The posture prediction algorithm then calculates the optimal posture for the whole upper body necessary to execute the grasp. The proposed algorithm is tested on a variety of objects in a 3-D environment. The results are realistic and suggest that the new method is more suitable for grasp planning than conventional methods. This improved performance is particularly apparent when the nature of the grasped objects is not known a priori , and when a complex high-DOF hand model is necessary.      

A memory-based model for planning target reach postures in the presence of obstructions

Existing posture prediction and motion simulation models generally lack the capability of simulating human obstruction avoidance during target reach. This compromises the utility of digital human models for ergonomics, as many design problems involve interactions between humans and obstructions. To address this problem, this paper presents a novel memory-based posture planning (MBPP) model, which plans reach postures that avoid obstructions. In this model, the task space is partitioned into small regions called cells. For a given human figure, each cell is linked to a memory that stores various alternative postures for reaching the cell. When a posture planning problem is given in terms of a target and an obstruction configuration, the model examines postures belonging to the relevant cell, selects collision-free ones and modifies them to exactly meet the hand target acquisition constraint. Simulation results showed that the MBPP model is capable of rapidly and robustly planning reach postures for various scenarios.     

A memory-based model for planning target reach postures in the presence of obstructions

Existing posture prediction and motion simulation models generally lack the capability of simulating human obstruction avoidance during target reach. This compromises the utility of digital human models for ergonomics, as many design problems involve interactions between humans and obstructions. To address this problem, this paper presents a novel memory-based posture planning (MBPP) model, which plans reach postures that avoid obstructions. In this model, the task space is partitioned into small regions called cells. For a given human figure, each cell is linked to a memory that stores various alternative postures for reaching the cell. When a posture planning problem is given in terms of a target and an obstruction configuration, the model examines postures belonging to the relevant cell, selects collision-free ones and modifies them to exactly meet the hand target acquisition constraint. Simulation results showed that the MBPP model is capable of rapidly and robustly planning reach postures for various scenarios.     

On simulating human reach motions for ergonomics analyses

Many existing job analysis tools for ergonomics have concentrated on the potential adverse effects of force, posture, and repetition, as these appear to be traditionally recognized risk factors in the workplace. Recent investigations have indicated that this pragmatic approach may be overly simplistic, and thus miss prediction of risk factors associated with certain motions. This article reviews some of the research under way in the University of Michigan's Human Motion Simulation Laboratory to develop a set of human motion prediction models. To produce these models, over 37,000 motions of 100 men and women from 18 to 78 years in age have been measured with a motion capture system. The motions are typical of people reaching and moving light to moderate load objects while either seated or standing. A 17‐link kinematics model has been developed to resolve the dynamics of the motions. Thus far, initial motion algorithms have been developed that capture well over 95% of the between participant repeatability. Advantages and limitations of the methods and data being used are discussed and illustrated. © 2002 Wiley Periodicals, Inc.     

Development of a digital human model generation method for ergonomic design in virtual environment

A group of digital human models (DHMs) representing the target population under consideration is used to design products and workplaces in virtual environment. The present study proposes a two-step method which generates a group of DHMs in various sizes to properly accommodate the designated level of the human size variability of the target population. In the first step, a designated number of pairs of stature and weight within a specified accommodation range are generated from the bivariate normal distribution of stature and weight of the target population. In the second step, for each pair of stature and weight, the sizes of the DHM body segments are determined using hierarchical regression models and corresponding prediction distributions of individual values. The proposed generation method was applied to the 1988 US Army anthropometric survey data and then implemented to a web-based system for passenger car interior design. This web-based generation system is capable of generating a group of DHMs as nationality, gender, accommodation percentage, and the number of DHMs required is specified.

Relevance to industry

A digital human simulation system has been used as an effective tool for ergonomic design and evaluation of products and workplaces in virtual environment. The human model generation method proposed in the present study is of use to efficiently generate a group of human models representing the target population.     

Ted Megaw: an appreciation

Driver workspace design and evaluation is, in part, based on assumed driving postures of users and determines several ergonomic aspects of a vehicle, such as reach, visibility and postural comfort. Accurately predicting and specifying standard driving postures, hence, are necessary to improve the ergonomic quality of the driver workspace. In this study, a statistical clustering approach was employed to reduce driving posture simulation/prediction errors, assuming that drivers use several distinct postural strategies when interacting with automobiles. 2-D driving postures, described by 16 joint angles, were obtained from 38 participants with diverse demographics (age, gender) and anthropometrics (stature, body mass) and in two vehicle classes (sedans and SUVs). Based on the proximity of joint angle sets, cluster analysis yielded three predominant postural strategies in each vehicle class (i.e. ‘lower limb flexed’, ‘upper limb flexed’ and ‘extended’). Mean angular differences between clusters ranged from 3.8 to 52.4° for the majority of joints, supporting the practical relevance of the distinct clusters. The existence of such postural strategies should be considered when utilising digital human models (DHMs) to enhance and evaluate driver workspace design ergonomically and proactively.

Statement of Relevance: This study identified drivers' distinct postural strategies, based on actual drivers' behaviours. Such strategies can facilitate accurate positioning of DHMs and hence help design ergonomic driver workspaces.     

Calculation and visualization of the dynamic ability of the human body

There is a great demand for data on the mobility and strength capability of the human body in many areas, such as ergonomics, medical engineering, biomechanical engineering, computer graphics (CG) and virtual reality (VR). This paper proposes a new method that enables the calculation of the maximal force exertable and acceleration performable by a human body during arbitrary motion. A musculoskeletal model of the legs is used for the calculation. Using our algorithm, it is possible to evaluate whether a given posture or motion is a feasible one. A tool to visualize the calculated maximal feasibility of each posture is developed. The obtained results can be used as criteria of manipulability or strength capability of the human body, important in ergonomics and human animation. Since our model is muscle‐based, it is possible to simulate and visualize biomechanical effects such as fatigue and muscle training. The solution is based on linear programming and the results can be obtained in real time. Copyright © 1999 John Wiley & Sons, Ltd.     

A statistical method for predicting automobile driving posture

A new model for predicting automobile driving posture is presented. The model, based on data from a study of 68 men and women in 18 vehicle package and seat conditions, is designed for use in posturing the human figure models that are increasingly used for vehicle interior design. The model uses a series of independent regression models, coupled with data-guided inverse kinematics, to fit a whole-body linkage. An important characteristic of the new model is that it places greatest importance on prediction accuracy for the body locations that are most important for vehicle interior design: eye location and hip location. The model predictions were compared with the driving postures of 120 men and women in five vehicles. Errors in mean eye location predictions in the vehicles were typically less than 10 mm. Prediction errors were largely independent of anthropometric variables and vehicle layout. Although the average posture of a group of people can be predicted accurately, individuals' postures cannot be predicted precisely because of interindividual posture variance that is unrelated to key anthropometric variables. The posture prediction models developed in this research can be applied to posturing computer-rendered human models to improve the accuracy of ergonomic assessments of vehicle interiors.     

An assessment of the realism of digital human manikins used for simulation in ergonomics

In this study, the accuracy of the joint centres of the manikins generated by RAMSIS and Human Builder (HB), two digital human modelling (DHM) systems widely used in industry for virtual ergonomics simulation, was investigated. Eighteen variously sized females and males were generated from external anthropometric dimensions and six joint centres (knee, hip and four spine joints) were compared with their anatomic locations obtained from the three-dimensional reconstructed bones from a low-dose X-ray system. Both RAMSIS and HB could correctly reproduce external anthropometric dimensions, while the estimation of internal joint centres location presented an average error of 27.6 mm for HB and 38.3 mm for RAMSIS. Differences between both manikins showed that a more realistic kinematic linkage led to better accuracy in joint location. This study opens the way to further research on the relationship between the external body geometry and internal skeleton in order to improve the realism of the internal skeleton of DHMs, especially for a biomechanical analysis requiring information of joint load and muscle force estimation.

Practitioner summary: This study assessed two digital human modelling (DHM) systems widely used in industry for virtual ergonomics. Results support the need of a more realistic human modelling, especially for a biomechanical analysis and a standardisation of DHMs.     

Isokinetic strength characteristics in wrist flexion and extension

A laboratory experiment was conducted to measure strength characteristics in dynamic (isokinetic) wrist flexion and extension. Twenty four college-age males exerted their maximum torque in both concentric flexion and extension at 60, 120, and 180°/s of angular velocity through a ±60° range of deviation from wrist neutral. Results show that velocity and motion direction significantly effected both peak torque as well as the postural displacement of peak torque. The value of peak torque decreased with an increase in velocity and the wrist angle at peak torque generally moved to a more deviated, flexed posture (from neutral) with increasing velocity as well. Peak torque for all velocity and motion-type conditions tested occurred in a flexed posture relative to neutral. It is anticipated that these results may be of use as biomechanically based considerations in the evaluation and design of upper extremity tasks involving wrist flexion/extension as well as to perhaps give insight into functional characteristics of the wrist. Finally, regression equations were developed to aid in the prediction of peak torque based upon task, individual and/or population parameters.

Relevance to industry

Results from this study should enhance the overall understanding of wrist functioning. Specifically, motion type, velocity of movement and wrist posture are important ergonomic design considerations. These results can also be used to modify existing biomechanical models that do not consider wrist variables.     

Computerized task risk assessment using digital human modeling based Job Risk Classification Model

Different from the classical ergonomics analysis, Virtual Interactive Design methodology relies on manikin movements in virtual environment. This platform has been implemented by researchers to accomplish static ergonomic analyses including Rapid Upper Limb Assessment, NIOSH lifting equation and Static Strength Prediction. However, considering only static posture information limits the capacity of ergonomics analysis. In this study, the methodology of performing dynamic ergonomics analysis based on this Virtual Interactive Design platform is proposed. This environment allows velocity and angular velocity of specified body segments/joints calculated for designed tasks to be used to assess the corresponding risk levels based on Job Risk Classification Model. The motion calculation is completed based on the captured interaction between human participants and virtual workplace/mockup. To evaluate the validity and reliability of this upgraded platform, potential errors are analyzed by comparing outputs from several designed experimental conditions.     

Effect of posture positions on the evaporative resistance and thermal insulation of clothing

Evaporative resistance and thermal insulation of clothing are important parameters in the design and engineering of thermal environments and functional clothing. Past work on the measurement of evaporative resistance of clothing was, however, limited to the standing posture with or without body motion. Information on the evaporative resistance of clothing when the wearer is in a sedentary or supine posture and how it is related to that when the wearer is in a standing posture is lacking. This paper presents original data on the effect of postures on the evaporative resistance of clothing, thermal insulation and permeability index, based on the measurements under three postures, viz. standing, sedentary and supine, using the sweating fabric manikin-Walter. Regression models are also established to relate the evaporative resistance and thermal insulation of clothing under sedentary and supine postures to those under the standing posture. The study further shows that the apparent evaporated resistances of standing and sedentary postures measured in the non-isothermal condition are much lower than those in the isothermal condition. The apparent evaporative resistances measured using the mass loss method are generally lower than those measured using the heat loss method due to moisture absorption or condensation within clothing. STATEMENT OF RELEVANCE: The thermal insulation and evaporative resistance values of clothing ensembles under different postures are essential data for the ergonomics design of thermal environments (e.g. indoors or a vehicle's interior environment) and functional clothing. They are also necessary for the prediction of thermal comfort or duration of exposure in different environmental conditions.     

基于分级规划策略的拟人机械臂仿人运动规划算法研究

为使拟人机械臂具有高精度的仿人运动,提出一种通过触发条件和分级规划策略 的仿人运动新方法。将人臂运动过程离散为不同运动阶段,在每一个运动阶段都有与之对应的 规划层,在不同的规划层中,拟人机械臂的运动特点不同。利用各自的特点建立不同规划层下 的运动模型及臂姿预测指标,对拟人机械臂臂姿进行预测。最后,以NAO 机器人为实验平台, 比较所提方法与最小势能法(MTPE)的静态臂姿与动态臂姿预测,并与运动捕捉系统(OptiTrack) 采集的真实人臂运动数据进行比较。实验表明,该方法具有较小的静态臂姿和动态臂姿预测误 差,能使拟人机械臂产生高度逼真的仿人运动。     

Prediction of human reach posture using a neural network for ergonomic man models

For proper evaluation of operator's usability through ergonomic man models, accurate prediction of human reach is one of the essential functions that those models should possess. This study examined the applicability of artificial neural networks to the prediction of human reach posture. The three-dimensional motion trajectories of the joints of upper limb (shoulder, elbow, and wrist) in the right arm from 5 percentile female to 95 percentile male were obtained through a motion analysis system that photographed actual human reach. The data obtained were divided into two data sets — training data set and test data set. The backpropagation method being usually used for a pattern associator was employed as a tool for predicting such human movements. Comparisons between prediction and real measurements were made using a pairwise t-test, and no significant differences were found between the two data sets for all the joints considered. Thus, the neural network approach adopted in this study showed a very promising prediction capability of human reach and it is, therefore, expected that this method be used to accurately simulate human reach better than existing heuristic or analytic methods as well as to improve a human modelling capability in general.     

基于舒适度最大化的人体运动控制

在分析人体自身运动反馈控制机理的基础上,引入舒适度作为人体运动控制的性能指标函数,提出了一种基于舒适度最大化的人体运动控制算法．该算法综合考虑了运动学、动力学以及人体工效学等因素,避免了单纯从动力学方面求解带来的虚拟人体动画所隐含的力量冲突,提高了仿真结果的真实性和合理性．最后,用实验验证了文中算法的有效性．该算法为设计安全舒适的人体动作和作业提供了一定的参考依据．     

基于H-anim的数字化人机设计研究*

为实现数字化人机工程,引入数字化人体模型来统一表达人机测量尺度、肢体动作特征和作业空间范围等人机要素,并提出基于H-anim的数字化人体建模方法,结合VRML原型节点建立了三维的,具有真实感的人体尺寸模型,利用EAI技术仿真人体动作行为。最后,以机床为对象,构建了计算机辅助人机设计系统,实现了操作空间的可见性、可及性与宜人性人机评价。     

Nonlinear inverse optimization approach for determining the weights of objective function in standing reach tasks

This paper presents a nonlinear inverse optimization approach to determine the weights for the joint displacement function in standing reach tasks. This inverse optimization problem can be formulated as a bi-level highly nonlinear optimization problem. The design variables are the weights of a cost function. The cost function is the weighted summation of the differences between two sets of joint angles (predicted posture and the actual standing reach posture). Constraints include the normalized weights within limits and an inner optimization problem to solve for joint angles (predicted standing reach posture). The weight linear equality constraints, obtained through observations, are also implemented in the formulation to test the method. A 52 degree-of-freedom (DOF) human whole body model is used to study the formulation and visualize the prediction. An in-house motion capture system is used to obtain the actual standing reach posture. A total of 12 subjects (three subjects for each percentile in stature of 5th percentile female, 50th percentile female, 50th percentile male and 95th percentile male) are selected to run the experiment for 30 tasks. Among these subjects one is Turkish, two are Chinese, and the rest subjects are Americans. Three sets of weights for the general standing reach tasks are obtained for the three zones by averaging all weights in each zone for all subjects and all tasks. Based on the obtained sets of weights, the predicted standing reach postures found using the direct optimization-based approach have good correlation with the experimental results. Sensitivity of the formulation has also been investigated in this study. The presented formulation can be used to determine the weights of cost function within any multi-objective optimization (MOO) problems such as any types of posture prediction and motion prediction.     

Nonlinear inverse optimization approach for determining the weights of objective function in standing reach tasks

This paper presents a nonlinear inverse optimization approach to determine the weights for the joint displacement function in standing reach tasks. This inverse optimization problem can be formulated as a bi-level highly nonlinear optimization problem. The design variables are the weights of a cost function. The cost function is the weighted summation of the differences between two sets of joint angles (predicted posture and the actual standing reach posture). Constraints include the normalized weights within limits and an inner optimization problem to solve for joint angles (predicted standing reach posture). The weight linear equality constraints, obtained through observations, are also implemented in the formulation to test the method. A 52 degree-of-freedom (DOF) human whole body model is used to study the formulation and visualize the prediction. An in-house motion capture system is used to obtain the actual standing reach posture. A total of 12 subjects (three subjects for each percentile in stature of 5th percentile female, 50th percentile female, 50th percentile male and 95th percentile male) are selected to run the experiment for 30 tasks. Among these subjects one is Turkish, two are Chinese, and the rest subjects are Americans. Three sets of weights for the general standing reach tasks are obtained for the three zones by averaging all weights in each zone for all subjects and all tasks. Based on the obtained sets of weights, the predicted standing reach postures found using the direct optimization-based approach have good correlation with the experimental results. Sensitivity of the formulation has also been investigated in this study. The presented formulation can be used to determine the weights of cost function within any multi-objective optimization (MOO) problems such as any types of posture prediction and motion prediction.     

Green ergonomics: definition and scope

This paper demonstrates that the goals of ergonomics (i.e. effectiveness, efficiency, health, safety and usability) are closely aligned with the goals of design for environmental sustainability. In this paper, the term ‘green ergonomics’ is conceptualised to specifically describe ergonomics interventions with a pro-nature emphasis. Green ergonomics is focused on the bi-directional connections between human systems and nature. This involves looking at (1) how ergonomics design and evaluation might be used to conserve, preserve, and restore nature and (2) how ecosystem services might be harnessed to facilitate the improved wellbeing and effectiveness of human systems. The paper proposes the scope of green ergonomics based on these bi-directional relationships in the areas of the design of low resource systems and products, the design of green jobs, and the design for behaviour change. Suggestions for further work in the green ergonomics domain are also made.

Practitioner Summary: Given the enormous environmental challenges facing modern industrial society, this paper encourages ergonomics science to embrace a pro-nature understanding of work design and research. This paper sets out the role for green ergonomics based on an appreciation of the human–nature connections that have been integrated with our understanding of ergonomics science and practice.