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Specifying comfortable driving postures for ergonomic design and evaluation of the driver workspace using digital
Gyouhyung Kyung1, Maury A Nussbaum
1School of Design and Human Engineering, UNIST, Ulsan, Republic of Korea.
Ergonomics
|July 25, 2009
Summary
Comfortable driving postures vary by vehicle type, age, and gender, influencing ergonomic workspace design. Identifying optimal joint angle ranges improves driver comfort and digital human model accuracy.
Area of Science:
- Ergonomics
- Human Factors Engineering
- Automotive Design
Background:
- Ergonomic design of driver workspaces relies on understanding comfortable driving postures.
- Existing recommendations for driving postures require enhancement and expansion.
- Comfortable postures are crucial for driver well-being and workspace evaluation.
Purpose of the Study:
- To enhance and expand upon existing recommendations for comfortable driving postures.
- To investigate the influence of vehicle class, venue, and seat comfort on driving postures.
- To identify precise ranges of joint angles for optimal driving postures.
Main Methods:
- Thirty-eight participants completed six driving sessions across different vehicle classes (sedan, SUV), venues (lab, field), and seat comfort levels.
- Sixteen joint angles were measured to characterize preferred driving postures.
- Perceptual responses related to comfort were collected alongside postural data.
Main Results:
- Driving postures exhibited bilateral asymmetry and significant differences based on vehicle class, venue, age, and gender.
- A subset of preferred postural ranges was identified using a filtering mechanism based on perceptual responses.
- Distinct postural variations were observed between different driving conditions and participant demographics.
Conclusions:
- Accurate ranges of joint angles for comfortable driving postures are essential for ergonomic design.
- Vehicle and driver-specific factors significantly influence comfortable driving postures.
- Findings facilitate the ergonomic design and evaluation of driver workspaces, especially within digital human models.