Digital Human Modelling Tools Research Articles

Postural Behaviors during One-Hand Force Exertions

Posture and external loads such as hand forces have a dominant effect on ergonomic analysis outcomes. Yet, current digital human modeling tools used for proactive ergonomics analysis lack validated models for predicting postures for standing hand-force exertions. To address this need, the effects of hand magnitude and direction on whole-body posture for standing static hand-force exertion tasks were quantified in a motion-capture study of 19 men and women with widely varying body size. The objective of this work was to identify postural behaviors that might be incorporated into a postureprediction algorithm for standing hand-force tasks. Analysis of one-handed exertions indicates that, when possible, people tend to align their bodies with the direction of force application, converting potential crossbody exertions into sagittal plane exertions. With respect to the hand-force plane, pelvis position is consistent with a postural objective of reducing rotational trunk torques. One-handed task postures are characterized by axial rotation of the torso towards or away from the point of force application.

Effects of stature, age and vehicle motion on ingress movement

Vehicle ingress is an important automotive industry issue. End-users and production line assemblers perform similar ingress tasks. In this study, comparisons were made of the car ingress motion in 40 subjects of different statures, acting both as assemblers and end-users. Half of the subjects were under 28 years of age and the remaining were over 60. Results show no significant differences in motion patterns between assemblers and end-users, i.e., slow forward motion of the car on the assembly line had no effect on ingress patterns. This suggests that ergonomic departments working either with end-users or assemblers may instead cooperate or even be fully integrated. Stature significantly affected joint angle distribution and joint angle velocity distribution. No stature effect was found on time to perform ingress movements or on ingress technique. Age significantly affected all test parameters and is thus an issue for developers to consider along with anthropometric variables like stature. To facilitate age analysis, manikins in digital human modelling tools should be able to replicate the physical characteristics of different age groups and the movement behaviour of older people.

Guide and documentation system to support digital human modeling applications

Car developers use digital human modeling tools to analyze and visualize car interiors in relation to human characteristics before the vehicles are actually constructed. Developers, reviewers and users of human simulation tools often claim that such tools can reduce development time and costs. In car industry companies today, human simulation tools are used by a single or a few experts in an informal working process with insufficient documentation. To prepare for extensive, effective and efficient use of human simulation modeling tools in industry with several users within a company, the aim of this study was to design and evaluate a digital guide and documentation system to support digital human modeling applications. A participative design approach was used in developing the guide, involving human simulation tool users and managers within the General Motors Group. The system consists of two major parts: a usage guide and database. The usage guide is divided into three sections considering the professionals involved: (1) initiation of human–vehicle interaction analysis, (2) preparation and running of the digital human tool and (3) recommendation formulation and closure. The guide was connected to a database with search and print capabilities for previous and ongoing human simulation analyses. Sixteen subjects from industry and university settings evaluated the support system. Results showed that the users appreciated the guide and documentation system, in particular, the database for storing human simulation work. The guide was perceived as being especially useful for guidance in large analyses, whereas for smaller ones the subjects felt the formalized guide was too lengthy and time consuming. The use of the formalized guide is likely to reduce differences in results, within and between tool users. The support system guides the simulation tool user through an acknowledged process; it documents, stores and keeps track of ongoing and previous analyses, and facilitates the reuse of studies. Relevance to industry The system can be very helpful for industries striving to improve process quality and documentation of digital human modeling analyses.

Human Performance Modeling: Identification of Critical Variables for National Airspace Safety

Computational human performance modeling tools have been under development for over 50 years to generate human performance predictions for studying complex human behaviors that possess high costs associated with failures. A common measure for analysis from the digital human modeling tools is operator workload. One complex behavioral environment that is being explored by international regulatory and airline operations groups and is likely to reflect workload differences is known as “free flight”. “Free flight” in the United States is a Federal Aviation Administration (FAA) strategic goal for system capacity and for Air Traffic Services to improve accessibility, flexibility, and predictability in the national airspace in order to reduce flight times, crew resources, maintenance, and fuel costs. The experimental scenarios used to explore “free flight” are based on the full “free flight” concepts anticipated by Requirements and Technical Concepts for Aviation (RTCA) in the year 2025. These concepts explore the farthest out parameters of the aviation system. An evaluation of predicted behavioral costs associated with current day and “free flight” operations was performed using two “first principles” models, Air Man-machine Integration Design and Analysis System (Air MIDAS) and the Integrated Performance Modeling Environment (IPME). In analyses of a common scenario, both tools revealed increases along a seven-point, four-channel workload scale from current (MAir MIDAS = 0.77, MIPME = 1.24) to “free flight” operations (MAir MIDAS = 1.15, MIPME= 1.96). The inclusion of a handoff and an emergency event was found to increase the workload levels of the virtual operators within both software tools and provided different performance profile predictions depending on the operator's role in the NAS when faced with such a rule set change as the one proposed by the RTCA (Air MIDAS current day - Mground = 0.84, MAir = 0.71; IPME current day - Mground = 1.85, MAir = 0.62; Air MIDAS “free flight” - Mground = 0.77, MAir = 1.53; IPME “free flight” - Mground = 2.07, MAir = 1.84). These findings support the notion of initially using models for exploration of variables for inclusion in costly simulation studies. A validation effort of these findings with human-in-the-loop data is required and anticipated.