Predicting preferred motorcycle riding postures to support human factors/ergonomic trade-off analyses within a...

Area of Science:

• Ergonomics and Human Factors
• Automotive Design
• Computational Modeling

Background:

• Digital Human Models (DHM) are crucial for early-stage vehicle design, enabling prediction of user interactions with new geometries.
• Accurate prediction of human postures in DHM requires understanding the performance criteria that influence user preferences.
• Specific performance criteria driving preferred motorcycle riding postures remain largely unknown.

Purpose of the Study:

• To identify the key performance criteria and their weightings that best predict preferred motorcycle riding postures using DHM.
• To address the gap in knowledge regarding the factors influencing motorcycle rider posture prediction in digital environments.

Main Methods:

• Literature review to gather experimental data on preferred motorcycle riding postures (joint angles).
• Response surface methodology employed to optimize performance criteria and weightings for DHM posture prediction.
• Analysis focused on criteria such as joint range of motion, displacement from neutral, and joint torque.

Main Results:

• The minimization of discomfort emerged as the most significant performance criterion.
• Optimal weightings were determined for discomfort-related factors, including joint range of motion, displacement from neutral, and joint torque.
• This approach successfully predicted preferred motorcycle riding postures within the DHM framework.

Conclusions:

• Minimizing rider discomfort is the primary driver for preferred motorcycle riding postures in DHM simulations.
• The study provides a validated method for optimizing DHM parameters to accurately reflect rider preferences in motorcycle design.
• Findings contribute to more realistic and effective early-stage design trade-off analyses in the automotive industry.