| Optimising Driver Comfort Through Pressure Distribution and Physiological Data: A Predictive Model for Human–Machine Interface Design |
| Mingyue Li1, Fei Yu1, Baohua Ding2, Chongyue Shi2, Qi Wang1, Yisheng Du1, Hong Li1 |
1School of Mechanical Engineering , Yangzhou University, No. 196 West Huayang Road, Hanjiang District, Yangzhou City 225127, Jiangsu Province, China
2Ningbo Geely Automobile Research and Development Co. Ltd, NingBo 315000, China |
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Received: November 13, 2024; Revised: December 22, 2024 Accepted: March 25, 2025. Published online: June 5, 2025. |
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| ABSTRACT |
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The development of automotive intelligence has become an unstoppable global trend. To keep up with the forefront of human–machine interaction design and meet the growing demand for driving comfort from consumers, this study comprehensively considers multiple factors such as the driver's body shape, muscle activation, and driving behavior to establish four comfort prediction models. Through comparison, it was found that the support vector machine (SVM) leads with an R2 of 92.18%, having the lowest RMSE and Theil IC, making it the overall optimal model. The RBF model slightly outperforms in MAPE, but is generally inferior to SVM. The decision tree model ranks the lowest across all metrics. Based on the prediction models, the driver's body pressure distribution data is obtained through a pressure mat, and the driver's muscle activation levels are assessed using AnyBody simulation. Finally, an orthogonal experimental design is used to determine the optimal size parameters for the cockpit layout, including the seat, pedals, and steering wheel. |
| Key Words:
Operation comfort · Pressure distribution · Physiological information · Comfort prediction model · Optimal human–machine arrangement |
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Corresponding Author.
Mingyue Li , Email. 
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