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Draft:Digital Human Modeling (DHM)


Introduction

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Digital human modeling (DHM) technology creates digital representations of individuals, including their size, proportions, muscles, and movement aspects. This technology enables the integration of virtual humans into computer-aided design (CAD) or virtual reality (VR) environments. DHM software employs advanced visualizations alongside mathematical and scientific principles to aid designers in predicting the safety and effectiveness of their designs. Through DHM, designers can develop digital models of people from various populations and test different design options in a virtual setting. This virtual testing can significantly reduce the time and costs associated with modifications to physical prototypes later in the design process.

Definition(s)

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Digital human modeling (DHM) encompasses a wide array of methodologies and technologies aimed at portraying human characteristics and behaviors within digital or virtual environments. The concept of DHM varies significantly across different disciplines and domains, reflecting diverse connotations and applications. In the realm of ergonomics, DHM serves as a pivotal tool enabling Human Factors and Ergonomics (HFE) specialists to simulate and analyze ergonomic factors crucial to product design and workplace settings. From a design perspective, DHM integrates human body dimensions, postures, and behaviors into computer-aided design (CAD) systems to enhance product usability and user comfort. Beyond physical attributes, DHM extends into behavioral sciences, encompassing the modeling of cognitive processes and decision-making. This interdisciplinary approach highlights DHM's versatility across fields such as manufacturing, healthcare, and virtual reality, where it plays a vital role in optimizing human-system interactions and assessing design feasibility.

The following are definitions collected from various sources about digital human modeling (DHM):

  1. Chaffin (2008), defines it as a technology that equips Human Factors and Ergonomics (HFE) specialists with the computational power to simulate ergonomic issues in products or applications.[1]
  2. Reed et al. (2019) view DHM as a method for representing human body size, shape, and posture within computer-aided design (CAD) systems (both 2D and 3D). Their focus lies on improving product accommodation and reducing worker stress.[2]
  3. Bubb and Fritzsche (2009) describe DHM as computer-based models that capture both human appearance and behavior[3].
  4. Stephens and Jones (2009) highlight its utility in evaluating assembly feasibility and identifying process capability issues.[4]
  5. Abdel-Malek et al. (2001) emphasize the importance of realistic postures and motions within DHM.[5]
  6. Jung et al. (2009) presents it as a technique for visualizing human-workstation interaction from an ergonomic perspective.[6]
  7. Garneau and Parkinson (2008) define DHM as a graphical design method that utilizes manikins (digital representations of human figures) with a mathematical basis. This approach allows designers to modify and assess designs for user needs.[7]
  8. Backs et al. (2009) introduce the concept of digital manikins as virtual agents, suggesting the incorporation of human cognitive behavior alongside physical attributes for a more realistic model.[8]

Therefore, a broader definition of DHM is:

"A research domain that focuses on synthesizing and applying theory, principles, methods, and technology from a wide range of disciplines. This domain enables computational visualization, modeling, and simulation of human-systems interactions. The goal is to facilitate the prediction and optimization of human well-being and performance".[9]

Benefits and Challenges of Designing with Digital Human Models (DHM)

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Digital human modeling (DHM) offers several significant advantages in the design process. One of the primary benefits is cost-effectiveness, as DHM can reduce overall design costs by facilitating early ergonomics assessments and potentially minimizing the reliance on physical prototypes. Moreover, DHM tools empower designers, including those without extensive ergonomics expertise, to conduct basic ergonomics assessments. These tools include 3D human models that accurately represent physical attributes, vision analysis tools for assessing peripheral coverage and obscuration zones, predefined postures with predictive models to generate realistic poses, toolkits for assessing comfort and injury risk, and performance measurement tools for calculating factors like comfort, fatigue, energy expenditure, static strength, and cognition (with certain limitations).[9]

However, there are also notable challenges associated with DHM. Fidelity limitations mean that DHM simulations may not accurately capture real-world user variations in posture and behavior, potentially leading to inaccurate results. The representativeness of manikins in DHM libraries can be limited, as they may not include data for all user populations, such as the elderly and people with disabilities.[9]

Seamless integration between Digital Human Modeling (DHM) and Computer-Aided Engineering (CAE) is often lacking, making the workflow cumbersome for designers. Integrated DHM-CAE systems, such as Catia manikin, allow for quick ergonomic assessments within the design platform itself, eliminating the need for file conversions and transfers. This integration offers significant benefits by streamlining the design process. In contrast, standalone DHM software requires constant file transfers between it and CAE software for design modifications, which is time-consuming and disrupts the design flow.[10]

Additionally, most DHM tools focus on physical ergonomics, neglecting cognitive and perceptual aspects such as human emotion, decision-making, and mental workload. An ideal DHM platform should integrate both physical and cognitive data for a comprehensive ergonomic analysis, incorporating cognitive models like facial expressions, stress, and mental workload.[11]

Applications of DHM

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Applications in Automobile Industry

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Digital human modeling (DHM) finds diverse applications within the automobile industry, enhancing vehicle design and safety across various fronts. DHM is instrumental in optimizing vehicle packaging by analyzing interior space allocation to improve comfort and functionality. It enables the evaluation of driving comfort based on human body dimensions (anthropometry), ensuring that controls and displays are appropriately positioned for drivers of different sizes. Visual analysis with DHM assesses visibility of essential controls behind the steering wheel, identifying potential obstructions such as pillars or interior elements that could affect driver visibility.[9]

Applications in Aviation and Space Industries

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Digital human modeling (DHM) is integral to the design, manufacturing, and maintenance processes of civil (e.g., Boeing 777), military (e.g., F-15, F-22), and space vehicles (e.g., ISS MPLM). DHM plays a crucial role in ergonomic design by identifying and addressing ergonomic issues early in the development phase, ensuring that personnel of varying body sizes can safely and comfortably perform tasks. This proactive approach not only reduces costs associated with physical prototyping and human testing, particularly in microgravity environments like space stations, but also enhances safety by mitigating risks during maintenance and operations.[9]

Applications in Production (Assembly & Manufacturing)

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Digital human modeling (DHM) plays a critical role in ergonomic assessment and design across various industries, focusing on safety, comfort, and efficiency in workplace environments. DHM utilizes methods such as Rapid Upper Limb Assessment (RULA) and Ovako Working Posture Analysis System (OWAS) to analyze safety and potential injury risks during the design phase of workstations and tasks. Simulations conducted by DHM assess joint angles and comfort levels for workers of diverse anthropometry, ensuring ergonomic suitability across different body sizes.[9]

Applications in Medicine and Healthcare

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Key applications of DHM in medicine include ergonomic assessment of medical products, where it evaluates usability and user interaction with devices across a spectrum of abilities. DHM also aids in accessibility design, offering insights into how individuals with disabilities can interact with medical equipment such as wheelchairs or walkers. In rehabilitation planning, DHM simulations provide valuable tools to assess potential outcomes of procedures or therapeutic interventions before implementation. Additionally, voxel-based DHM contributes detailed anatomical models for advanced medical mannequins used in training, medical illustrations, and prosthetic development.

Future advancements in DHM within medicine include integrating DHM with advanced visualization technologies such as 3D tomography, virtual and augmented reality (VR/AR), and haptic feedback systems. These integrations are envisioned to enhance surgical planning and simulations, improving precision and outcomes. Moreover, DHM coupled with haptic interfaces holds potential for advancing medical education and training, particularly in developing palpation skills and enhancing tactile feedback for learners.[9]

Applications in Consumer Goods

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DHM offers comprehensive ergonomic evaluations throughout product development, catering to diverse user groups and identifying potential accessibility issues, repetitive strain risks, and awkward postures in maintenance tasks. It optimizes ergonomic design by simulating interactions with customers, manufacturing workers, and maintenance personnel, enhancing comfort and usability. Emerging trends in DHM include product personalization through 3D scanning and anthropometric data, allowing for customized products like scoliosis braces.[9]

DHM Software Tools

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1.Siemens Jack (University of Pennsylvania/Siemens PLM Software):

  - Strengths: Large anthropometric library, customizable manikins, motion capture support, ergonomic analysis tools.

  - Weaknesses: Expensive and complex for new users.[12]

2. RAMSIS (Tecmath/Institute of Ergonomics of the Technische Universität München):

  - Strengths: Industry-specific modules (automotive, aviation, industrial vehicles), focus on occupant packaging and preventative maintenance.

  - Weaknesses: Limited applications outside the automotive industry.[13]

3. Santos Pro (Virtual Soldier Research/University of Iowa):

  - Strengths: Physics-based model for motion prediction, real-time posture prediction, advanced muscle modeling.

  - Weaknesses: May not be suitable for tasks requiring pre-recorded motion data.[14]

4. 3DSSPP (3D Static Strength Prediction Program/Center of Ergonomics at the University of Michigan):

  - Strengths: Accurately predicts static strength requirements for manual handling tasks.

  - Weaknesses: Limited to slow movements used in heavy material handling.[15]

5. HumanCAD 4 (NexGen Ergonomics):

  - Strengths: Easy-to-use interface, real-time kinematics, basic ergonomic analysis tools.

  - Weaknesses: Limited biomechanical modeling capabilities.[16]

6. AnyBody Modeling (AnyBody Technology):

  - Strengths: Detailed musculoskeletal modeling, ability to simulate daily living activities.

  - Weaknesses: May be overly complex for some DHM applications.[17]

7. MADYMO (Mathematical Dynamic Models/TNO-Netherlands Organisation for Applied Scientific Research, acquired by Siemens AG):

  - Strengths: Powerful tool for occupant safety analysis in crash testing.

  - Weaknesses: Primarily focused on safety simulations, not general ergonomics.[18]

8. DELMIA Human (Digital Enterprise Lean Manufacturing Interactive Application/Dassault Systèmes):

  - Strengths: Integrates with Dassault Systèmes PLM software, offers various ergonomic analysis tools.

  - Weaknesses: May lack some of the advanced features of other DHM software.[19]

9. IPS IMMA (Industrial Path Solutions Intelligently Moving Manikins):

  - Strengths: Fast and efficient ergonomic evaluation, automatic motion generation, user-friendly interface.

  - Weaknesses: Primarily focused on automotive manufacturing applications.[20]

References

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  1. ^ Chaffin, D. B (2008). "Digital human modeling for workspace design". Reviews of Human Factors and Ergonomics. 4 (1): 41–74. doi:10.1002/hfm.10018. hdl:2027.42/35212.
  2. ^ Reed, M. P., Jones, M. L. H., & Park, B.-K. (2019). "In S. Bagnara, R. Tartaglia, S. Albolino, T. Alexander, & Y. Fujita (Eds.) Modeling people wearing body armor and protective equipment: Applications to vehicle design". Advances in Intelligent Systems and Computing: Proceedings of the 20th Congress of the International Ergonomics Association. 826: 596–601.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  3. ^ Bubb, H., & Fritzsche, F. (2009). (2009). "A scientific perspective of digital human models: Past, present, and future. In V. G. Duffy (Ed.)". Handbook of Digital Human Modeling: 1–26.{{cite journal}}: CS1 maint: multiple names: authors list (link) CS1 maint: numeric names: authors list (link)
  4. ^ Stephens, A., & Jones, M. L. H. (2009). "Workplace methods and use of digital human models. In V. G. Duffy (Ed.)". Handbook of Digital Human Modeling: 1–11.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  5. ^ Abdel-Malek, K., Yu, W., Mi, Z., Tanbour, E., & Jaber, M. (2001). "Posture prediction versus inverse kinematics". In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. 80227: 37–45.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  6. ^ Jung, K., Kwon, O., & You, H. (2009). "Development of a digital human model generation method for ergonomic design in virtual environment". International Journal of Industrial Ergonomics. 39 (5): 744–748. doi:10.1016/j.ergon.2009.04.001.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  7. ^ Garneau, C. J., & Parkinson, M. B. (2008). "Optimal product sizing through digital human models". SAE Technical Paper Series. 1. doi:10.4271/2008-01-1921 – via SAE International.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  8. ^ Backs, W., & Boucsein, R. W. (2009). "Psychophysiology in digital human modeling". In V. G. Duffy (Ed.), Handbook of Digital Human Modeling: 1–9.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  9. ^ a b c d e f g h H. Onan Demirel, Salman Ahmed, and Vincent G. Duffy (2022). "Digital Human Modeling: A Review and Reappraisal of Origins, Present, and Expected Future Methods for Representing Humans Computationally". International Journal of Human-Computer Interaction. 38 (10): 897–937. doi:10.1080/10447318.2021.1976507.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  10. ^ Badler, N. I., Allbeck, J., Lee, S.-J., Rabbitz, R. J., Broderick, T. T., & Mulkern, K. M. (2005). "New behavioral paradigms for virtual human models". SAE Technical Paper Series. 723–729. doi:10.4271/2005-01-2689.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  11. ^ Zhang, X., & Chaffin, D. B. (2005). "Digital human modeling for computer-aided ergonomics. In W. S. Marras & W. Karwowski (Eds.)". The Occupational Ergonomics Handbook: 1–20.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  12. ^ "Process Simulate Human". Retrieved 1 July 2024.
  13. ^ "DIGITAL ERGONOMICS". Retrieved 1 July 2024.
  14. ^ "Santos: The Virtual Human". Retrieved 1 July 2024.
  15. ^ "3DSSPP Software - Center for Ergonomics". Retrieved 1 July 2024.
  16. ^ "NexGen Ergonomics". Retrieved 1 July 2024.
  17. ^ "AnyBody Technology". Retrieved 1 July 2024.
  18. ^ "Simcenter Madymo software". Retrieved 1 July 2024.
  19. ^ "DELMIA". 27 January 2023. Retrieved 1 July 2024.
  20. ^ "IPS IMMA". Retrieved 1 July 2024.
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