Abstract
Using Digital human modeling (DHM) early in design brings the advantage of reducing the time and resources committed to building full-scale physical prototypes. DHM also helps in minimizing efforts put on performing human subject data collection. However, majority of the repetitive two- and three-dimensional (2D/3D) object orientations and manikin adjustments in DHM are executed manually via point-and-click based keystrokes and precision mouse control, which correspond with increased user effort and time. Additionally, such manual adjustments often fail to mimic the actual postures with high fidelity; thus, injecting further user bias into the design. Due to lack of automation, engineers follow a much conservative approach via running a limited set of ergonomics simulations on select values in contrast to performing an exhaustive search for exploring an extensive set of anthropometry and postural variations. This study introduces an early design framework to automate manikin setup, task simulation, and ergonomic evaluations in DHM to provide concept design exploration capabilities. In this research work, a cockpit packaging design problem was explored to measure the reach gap values via the automation framework. Results suggest that the automation methodology has the potential to reduce the amount of time required to perform DHM simulations and helps in minimizing user bias. The automation framework generated ergonomic evaluations with high correlation values (>0.97) and provided approximately a 97.5% reduction in time when compared to manual simulations.
Keywords
1 Introduction
The motivation for this paper comes from the need to integrate human factors engineering (HFE) principles into the early design stage to evaluate ergonomics via computational models. This proactive approach helps designers to create concepts that include user needs, abilities, limitations as well as the engineering requirements, which has the potential to reduce not only the time but also the cost associated with prototyping.
Considering human factors in the early stages of design is also significant for the market success of a product; however, the implementation is complex and not always systematic. To understand the needs of the users, traditionally, design teams conduct experiments via human subjects and develop numerous physical prototypes to study human-product interactions [6]. The process of employing human subjects and fabricating physical prototypes requires excessive time and resource commitments. With the advancements of computational design tools in the last two decades, modeling human-product interactions via computational design software has become an alternative method to evaluate ergonomics before building physical prototypes [10, 12, 25].
One of the software approaches developed in the HFE domain, digital human modeling (DHM), becomes a viable option for reducing the total design time and cost by diminishing the number of physical prototypes and the design iterations [13]. DHM offers the advantage of performing ergonomics analysis based on computer-aided design (CAD) or virtual prototypes [10], and allows designers to follow a proactive ergonomics approach before physical prototypes are built. Through DHM, one can create, manipulate, and control the movements of the virtual humans (manikins) to replicate human-machine interactions in a computer environment. Also, with the help of ergonomics analysis tools, designers can evaluate concept designs in terms of ergonomics adequacies and suggest improvements [12, 25]. Within DHM, human postures or motions are generated through the implementation of biomechanical and multi-physics theory, and computational algorithms are used to generate ergonomics analysis [25]. In addition to the ergonomics analysis tools, DHM provides anthropometric databases, posture libraries, and motion prediction packages. These functionalities help designers to check safety and ergonomics early in design which can reduce the overall time and cost required for physical prototype development [12, 14, 17, 19, 25].
Literature review shows that DHM is generally used in the later stages of the design process when most of the critical design decisions are taken and mock-ups are built [8, 14, 18, 20, 23, 24]. At that moment, many aspects of the design such as structure, material, and sub-assemblies are already finalized, and any further design change would require additional resources. This reactive approach often results in further iterations, re-designs, and sometimes a complete design overhaul, until the final design fits the ergonomic requirements. As such, a proactive approach through implementing computational based ergonomic analysis in the early phases of the design process is needed [7, 9].
This study introduces an early design framework to automate manikin setup, task simulation, and ergonomic analysis in DHM. In this research work, the automation framework was evaluated via a cockpit packaging study in terms of assessing accessibility requirements, mainly focusing on reach-gap measurements. The results from the automation methodology were compared with the manual setup method in terms of ergonomic accuracy, time, and user effort. The following section elaborates more on the automation framework proposed in this paper.
2 Background
Many DHM software consists of complex sliders, pop-up windows, and control interfaces dedicated to manipulating manikins and CAD objects that are often found to be non-intuitive and require expert knowledge and skill set [16]. Additionally, repetitive two- and three-dimensional (2D/3D) object manipulations performed to replicate human posture and movements are executed manually via precision mouse control which require significant designer effort and time. The lack of automation and the absence of quick simulation tools have been discussed in the literature as one of the limitations of DHM [7]. Using DHM for design space exploration (DSE) requires substantial designer efforts to build, set up, and execute simulations, which are reported as time-consuming as the tool needs user input and manipulations for every task [8]. There are few toolkits available to mitigate the disadvantages of performing manual operations; however, they often bring minor improvements [9, 11, 21]. For example, task simulation builder (TSB) builder in Siemens Jack platform implements posture and motion prediction modules [21, 22], which aid designers in reducing the need for executing manual joint manipulations as the tool performs human movements automatically. The user selects the type of motion to be performed and the target/reference site as an endpoint for the motion. Although the advancements brought by such toolkits do help in reducing time spent on manual human manipulations, setting up simulations still takes much time and requires designers’ manual effort to adjust each simulation element, including CAD models, human posture, and motion.
There is also the possibility of users introducing errors during the ergonomics analysis setup. However, only a minimal number of studies have been conducted to understand the challenges faced by the users and the errors caused due to manual DHM manipulations. Ziolek and Nebel (2003) discussed that the most common errors are related to the positioning and body manipulations of the manikins [26]. For example, while performing occupant packaging tasks for two manikins with different anthropometries, the position and posture used during simulation setup might slightly differ if a standard reference is not considered [26]. Another limitation occurs due to designers usually controlling the manikin movements based on intuition. Thus, task simulation becomes the designers’ perspective of how one would accomplish a specific task. However, in reality, there might be variations in the way humans might perform these tasks.
Overall, the lack of DHM automation and the complexities associated with performing DHM simulations not only adds extra time and user effort but also causes engineers to follow a conservative approach. Focusing only on a limited set of ergonomics simulations rather than performing an exhaustive search limits DSE efforts. The practice of using a limited set of design variables results in a narrow DSE coverage and exploring only a few numbers of design alternatives [26]. Besides, relying on intuition increases the probability of injecting bias and errors, which can lead to the selection of erroneous or infeasible concepts. The next section elaborates more on how the framework functions.
3 Methodology
The automation framework proposed in this study uses Jackscript, a python-based scripting language, within Jack - a DHM software developed by Siemens that includes tool commanding language (Tcl) [4]. The main objective of the framework is to automate the ergonomic simulation process to increase the speed and accuracy of the simulation setup and ergonomic analysis. In this research, Jackscript and Tcl are used for creating automated sequences that help in reducing the effort required from the user to perform the ergonomic evaluations. The automation methodology provides real-time dynamic simulations of human motions, simultaneously performing ergonomic evaluations and storing data with minimal user effort.
The case study in this research is based on the design exploration research published by Ahmed et al. (2018), which focuses on the occupant packaging of a cockpit design [7] (See Fig. 1(b)). Figure 1(a) shows the data flow within the design exploration study. A total of 432 DHM simulations were run; each simulation consisted of numerous manual manipulations that included repetitive click-and-point keyboard strokes and precision mouse control. The entire process took around 108 h, with each simulation requiring around 20 min for manikin and object manipulation. Considering a typical eight-hour-long shift for five days a week, running these simulations approximately takes two weeks for a DHM expert to complete - considering there are no distractions.
If DHM simulations can be automated, a significant amount of time and human effort can be saved. Also, automation can aid designers to explore concept alternatives, which facilitates comprehensive DSE activities. In this study, we demonstrated our rationale by adding automation to generate cockpit packaging ergonomics based on the design study previously published by Salman et al. (2018). The next section will explain how the automation methodology works.
4 Simulation Setup: Case Study and Automation
This design problem focused on a fire/smoke emergency in a civilian aircraft cockpit. The DSE study was conducted to measure Reach Gap, which is referred to as the distance between the manikin’s thumb tip and the target control. In this task, the manikin attempts to reach and operate the controls with its hand fully stretched from the shoulder in an upright posture.
4.1 Simulation Setup - Digital Human Modeling Environment
The CAD model for the cockpit packaging study was based on a Boeing 767 cockpit design. Figure 2 shows the exterior and interior configuration of the low-fidelity CAD replica, which was composed of multiple smaller sub-components, including front-facing instruments panel (main panel), central console with controls and displays, rudder pedals, yoke and pilots’ seat.
4.2 Simulation Setup - Task Sequence
Hierarchical task analysis (HTA) was employed to identify what essential tasks had to be performed and the sequence in which these tasks must be executed. A six-step simplified HTA task sequence selected in this study was based on the checklists provided by the National Transport Safety Board (1996) [3]. The task sequence reflects what pilots are asked to perform in case of smoke build-up in a cockpit, which includes immediate descent or landing within 15 min of the fire being detected if possible [2].
Throughout this study, it was assumed that the smoke was detected in the cockpit, and the main pilot has put the oxygen mask on, as shown in Figs. 2b and 3. In the following step, the pilot sent a warning signal to the flight crew by activating the warning/caution alert lights using the warning button located at the front panel. In this particular case, it was assumed that the smoke/fire emergency was unmanageable, and pilots had to perform diversion or immediate landing. Thus, the next step performed by the pilot was to reach the vertical speed knob to identify and set the speed to descent. The third step included monitoring the engine indication and crew alerting system (EICAS) to check the status of the engine and any warning indicators from the enhanced ground proximity warning system (EGPWS) or traffic alert and collision avoidance system (TCAS) [1]. These tasks are not necessarily the steps that lead to immediate succession but a subset of tasks in an ascending order that the pilots should go through during fire/smoke emergencies. Also, these steps required DHM manikins to execute reach and visual monitoring tasks, which cover a wide range of cockpit volume and regions on the instrument panel. Based on the tasks identified via HTA, the sequence is as follows:
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1.
Look at the crew warning and alerting control
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2.
Reach the warning control
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3.
Look at the vertical speed indicator
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4.
Reach the vertical speed control
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5.
Look at the EICAS display screen
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6.
Reach the EICAS display screen.
4.3 Simulation Setup - Manikin Calibration and Reference Posture
Throughout the DHM simulations, a reference manikin was defined with a neutral posture for calibration and initialization. Figure 3 shows a side view of the neutral posture used in this study. A \(50^{th}\) percentile U.S. female represents a pilot positioned in the CAD environment, sitting in an upright posture and the legs comfortably reaching control pedals (Fig. 3). The comfortable upright posture was determined by using the Comfort Assessment toolbox in Jack’s Occupant Packaging toolkit. The manikin was first positioned at the pilot seat, and the knee/ankle joints were adjusted such that the manikin’s feet can reach the rudder pedals while maintaining comfort ratings. Sufficient space was also provided between the instrument panel, and the hands were located on the yoke. The oxygen mask was attached to the face of the manikin to represent the initial step of the fire/smoke emergency procedure. The neutral posture was used as a reference to eliminate any positioning-related errors during the automation and initiation of each simulation.
5 Automation in Digital Human Modeling via Jackscript
In this step, the task simulation and ergonomic evaluation script were developed using Jackscript. The script can be written using any python-based integrated development environment like Spyder or PyCharm. Jackscript provides various manikin and object control functions such as Move, Walk, Look At, and Reach. Thus, one can implement the required tasks or interactions by calling these functions in Jackscript console. For example, if one wants to simulate a manikin looking at the display screen and reaching the radio control button, then the task sequence can be programmed as follows in Fig. 4. In the code below, “Task1” represents the manikin performs the task of looking at a predefined point (DisplayScreen) located at the front control display. Similarly, “Task2” represents that the manikin reaches the radio button (RadioButton). Other features can also be used, such as “DoTogether” to perform tasks simultaneously.
Based on the tasks identified in HTA, each manikin went through the six-step task sequence (see Sect. 4.2), including three target sites located on the instrument panel, and performed Reach actions to activate crew warning, change vertical speed control, and reach EICAS display (Fig. 5). Each manikin was calibrated to perform Reach motions via “Locked Torso” inverse kinematics (IK) strategy, from the shoulder with the waist constrained (Fig. 6), which guaranteed no awkward postures by keeping joint angles within comfortable ranges.
5.1 Data Collection and Ergonomics Analysis
Along with performing task simulations listed above, the automation code was also executed for collecting Reach Gap data to perform occupant packaging study. The thumb-tip is often used as a standard end-effector for reach studies [15]. However, “ReachHold” function found in Jackscript only considers the forearm as the end-effector, which affected the “ReachHold” inverse kinematics (IK) scheme and resulted in awkward postures that were unrealistic to what humans would perform in reality. Thus, an alternate approach was identified by using the forearm end-effector with the palm-center site as the reference point (Fig. 6). As both sites represent a 3D vector, the Euclidean distance method was used to measure the distance between two vectors, as shown below.
Reach Gap analysis explained above was incorporated into the Jackscript code along with the automation algorithm that changes the manikin size and position. Once all the reach tasks were simulated, as the task sequence identified in Sect. 4.2, the ergonomic analysis was performed for the given anthropometry. Later, the manikin was scaled based on the corresponding anthropometry found in the anthropometry database. To ensure that each manikin was positioned according to its anthropometric scale, the neutral posture shown in Fig. 3 was kept as a reference, with heels of the manikin anchored to rudder pedals during scaling. This method ensured that while the manikin maintained the neutral posture, it also shifted in position as per the size of the manikin to provide proper access to the instrument panel and sufficient legroom space. For example, a \(5^{th}\) percentile Japanese female was positioned closer to the instrument panel due to the smaller stature. The manikin needed to be closer to the instrument panel for better accessibility (Fig. 7a). In contrast, a \(95^{th}\) percentile U.S. male was positioned farther from the instrument panel to provide functional legroom (Fig. 7b). In Fig. 7, the base of the pedal and the human lower torso sites are taken as references to show the differences in the positioning of two manikins.
Along with performing the ergonomic evaluations, Jackscript was also used for writing data to a CSV file. The automation algorithm was validated by comparing manual simulation results with data coming from automated simulations.
5.2 Automated Task Simulation and Ergonomic Evaluation
After the Jackscript code was developed, it was imported to the DHM simulation interface via Tcl module, which enables Jack to take commands about what functions to perform once an option is selected from the graphical user interface (GUI). A text file was created within the Tcl script, which commanded Jack to import and run the Python file containing the Jackscript code. Figure 8 shows how the automation structure functions within Jack. Additional features can be integrated into this toolbox, depending on the design requirements.
6 Simulation Study
The objective of this simulation study is to generate a design exploration study about the smoke/fire problem and provide a cockpit packaging assessment via automation capabilities integrated into DHM. In this study, each percentile within the anthropometric database was considered instead of applying a typically conservative approach of only running selective percentiles (e.g., \(5^{th}\), \(50^{th}\), \(95^{th}\)).
Jack’s built-in anthropometric libraries contain stature (height and weight) for only specific percentiles, including \(1^{th}\), \(5^{th}\), \(50^{th}\), \(95^{th}\) and \(99^{th}\). With the help of Pennsylvania State University’s Open Design Lab computational anthropometry tool (Data Explorer II), each percentile of anthropometry ranging from \(1^{th}\) to \(99^{th}\) in the U.S. ANSUR II library was extracted [5]. Then, the data was used to construct manikins, which were inserted into DHM simulations within the automation framework. The simulation study was composed of a total of 198 simulations. The experimental design is provided in the Table 1 below.
7 Results and Discussions
The performance of the automation tool is analyzed in terms of (1) the accuracy of simulation outcomes, and (2) the time required to complete each simulation. A random sample set of ten population percentiles and five standard population (\(1^{th}\), \(5^{th}\), \(50^{th}\), \(95^{th}\) and \(99^{th}\)) percentiles from ANSUR II database were selected. A total of fifteen manikins (see Table 2) were used for generating task simulations and ergonomic evaluations manually via Jack’s graphical user interface. Jack’s human control interface was used for manipulating joint angles and neck positions. The reach gap was measured by using the “Measure Distance” tool in Jack. Data collected manually was compared with the corresponding values calculated by the automation algorithm. The time required to perform the entire simulation was recorded. During automated data collection, a standard was set for positioning the manikins. The location of the “Human Lower Torso” site for each manikin was identified from the “Seat Location” data collected by the automation tool. Thus, manikins were positioned exactly at the corresponding seat location, and the neutral posture was assigned at the beginning of each simulation (Fig. 3). This approach guaranteed in eliminating any discrepancies and errors due to differences in positioning. The following sections provide detailed information on the results and comparisons.
7.1 Comparison in Accuracy - Manual vs. Automated Simulations
One of the critical aspects of the automation methodology discussed in this study is the accuracy of the ergonomics results. This section provides data on the accuracy of the automation framework in terms of the ergonomic evaluation results when compared to results generated via the manual method. The Reach Gap calculation performed by the automation tool using the Euclidean distance method was compared by performing the same study manually. A total of fifteen manikins (see Table 2) were selected for comparison of the accuracy.
The majority of the reach gap values calculated by the automation tool were within 0.5 cm of the mean absolute error when compared to the values calculated using the manual simulation method. The difference in the results only differed for the Target 1 (2.2 cm), which is not an error in distance calculations but an inaccuracy due to the differences in the way Jack performs the dynamic motions. Jack follows a specific IK scheme when performing “Reach” function using the “Manipulate” option, as opposed to the IK scheme, followed when using the “ReachHold” function in Jackscript. It can be justified by the fact that the distance calculations for Targets 2 and 3 resulted in high accuracy, which indicates that the movement performed by the manikin for Target 1 is different. To check this justification, the Jackscript was used to perform the manikin movements using the “ReachHold” function, and the reach distances measured both via manual and automated methods were found to be similar, as shown in Table 2.
One can see from Fig. 9, the Reach Gap values calculated by the automation tool follow the same trend-line as that of the values calculated using manual DHM simulations. Along with this, the Pearson correlation coefficients between manual and automated simulation results show a positive high-correlation with the coefficient values being 0.997, 0.971, and 0.995 for Target 1, 2, and 3, respectively (Table 4). The results show that the automation tool is accurate, with only minor improvements required in the simulation for hand movements while performing “Reach” motion. The majority of the values were within the absolute error of 0.5 cm except for the reach distances of Target 1, which can be accounted for the differences in the IK scheme. The automation tool is capable of simulating the dynamic human movements. Thus, which method to consider in terms of accuracy is arguable, especially for early design purposes (Table 3).
7.2 Comparison in Time - Manual vs. Automated Simulations
One of the key objectives for developing the automation framework was the need to reduce the time for running DHM simulation; thus, making the DHM-based design approach more natural to use for engineers in the early design. Specifically, when running HFE assessments for early purposes via DHM, one needs to consider multiple design concepts or alternatives, which require running numerous simulations. For example, as noted early, the study published by Salman et al. [7] uses manual DHM methods, which take around 15 min to execute simulations for a single manikin. As the number of simulations increases, the total simulation time and the effort required to generate ergonomics analysis becomes challenging to manage. In this study, one simulation consists of simulating all the tasks identified in Sect. 4.2 and performing ergonomic analysis for given human anthropometry. One manikin simulation includes eight tasks with the additional repetitive tasks of reach and head manipulations for Target 2 and Target 3; thus, resulting in a total of 16 tasks for a single simulation.
Table 5 provides a summary of the total-time required between manual and automated DHM simulations. Performing 16 tasks for one manikin took around 15 min and 50 s when executed manually by a DHM expert. In contrast, the automation tool performed these task simulations within 24 s. When considering the case of an exhaustive search approach or design exploration, such as the cockpit packaging analysis executed in this study, the total number of required simulations went up to 198 (99 manikins \(\times \) 2 genders) for a single DHM environment. These simulations, when performed manually, took around 52 h and 15 min for an expert user. In contrast, the same set of simulations only took 1 h and 19 min when performed with the automation approach. The automation method in DHM provided around a 97.5% improvement in total simulation time. In other words, the time required for the DHM simulation is reduced by 97.5%. Thus, the automation approach not reduce down the simulation time but also eases the user effort.
8 Conclusion
An automation framework for DHM-based simulation and ergonomics analysis was proposed in this study. The feasibility of this framework was assessed through a cockpit packaging study. Results show that the automation approach can result in reducing the expense in time without sacrificing for accuracy.
Although the proposed automation tool helps in saving significant user effort and time that is otherwise spend on performing the simulations manually in DHM, there are also disadvantages to using this tool. Manikin movements simulated by the automation tool are solely based on the path calculations performed by the IK scheme, which does not always reflect how humans behave while interacting with products. The majority of the DHM tools offer capabilities of integration with motion capture systems, in which case human subjects can be used to capture realistic postures. Another important observation is that a standard error exists within each computational approach since each computational model is an assumption of reality. The automation framework proposed in this study was specifically targeted to human-centered design studies that occur early in design, which often requires quick ergonomics analyses. Studies that focus on high precision and accuracy of human motion, such as kinesiology or biomechanics, might require actual human-subject data collection and experimentation for further validity check.
Overall, this automation tool offers a viable and quick solution when a design problem and task sequences are known for a design problem in which performing manual simulations won’t be feasible. The next section talks about the future research directions.
9 Future Work
One of the limitations of this study was that the ergonomic evaluations were done by considering only the ideal conditions. Although the case study considered a fire/smoke in the cockpit emergency scenario, the study doesn’t consider the actual effects of smoke on human performance (e.g., vision obscuration). The use of DHM for fire/smoke emergencies in the cockpit and evaluating pilot performance will be explored in the following studies.
Another limitation of this study is that it only considers a single design for ergonomics evaluation. The tool can be beneficial if the changes in the design environment can also be automated. We plan to implement this approach in the future, where the automation tool will be used for the design space exploration with multiple designs environments.
Additionally, programming using Jackscript is not a straight-forward option for novices. One needs to know and understand how Jackscript works and how various functions can be utilized to simulate human task. Future work in this area can focus on the development of graphical user interfaces similar to that of Jack’s TSB interface, which will make it easier for the designer to use this tool for their purpose.
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Gawand, M.S., Demirel, H.O. (2020). A Design Framework to Automate Task Simulation and Ergonomic Analysis in Digital Human Modeling. In: Duffy, V. (eds) Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Posture, Motion and Health. HCII 2020. Lecture Notes in Computer Science(), vol 12198. Springer, Cham. https://doi.org/10.1007/978-3-030-49904-4_4
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