Xiong Bai, Jinying Huang, Liyu Yang, Yuxuan Wang, Gaoshou Deng, Zhenfang Fan, Fan Yang
{"title":"座椅多轴相干振动下驾驶员舒适性评价方法。","authors":"Xiong Bai, Jinying Huang, Liyu Yang, Yuxuan Wang, Gaoshou Deng, Zhenfang Fan, Fan Yang","doi":"10.1080/00140139.2025.2553136","DOIUrl":null,"url":null,"abstract":"<p><p>Ergonomics increasingly emphasises that seat design should align with the driver's physiological needs to enhance comfort and health. This study uses deep learning to evaluate the impact of seat multi-axis coherent vibration on driver comfort. Through road tests, the multi-axis vibration signals were collected from the seat backrest, cushion and floor, simultaneously collecting subjective evaluation data. The consistency between subjective and objective data was verified using Stevens' power law, with the <i>R</i><sup>2</sup> exceeding 70%, indicating subjective evaluations can reflect driver comfort. Furthermore, a deep learning model integrating multimodal coherent features was used for quantitative evaluation. The results show that the method accurately captures frequency characteristics affecting comfort, with the metrics <i>R</i><sup>2</sup>, <i>RMSE</i> and <i>MAE</i> being 0.931, 0.096 and 0.071, respectively. This is comparable to the evaluations of ergonomics experts. The proposed method provides a promising solution for driver comfort evaluation. It is significant for enhancing driver health, comfort and safety.</p>","PeriodicalId":50503,"journal":{"name":"Ergonomics","volume":" ","pages":"1-22"},"PeriodicalIF":2.4000,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Evaluation method for driver comfort under multi axis coherent vibration of seats.\",\"authors\":\"Xiong Bai, Jinying Huang, Liyu Yang, Yuxuan Wang, Gaoshou Deng, Zhenfang Fan, Fan Yang\",\"doi\":\"10.1080/00140139.2025.2553136\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Ergonomics increasingly emphasises that seat design should align with the driver's physiological needs to enhance comfort and health. This study uses deep learning to evaluate the impact of seat multi-axis coherent vibration on driver comfort. Through road tests, the multi-axis vibration signals were collected from the seat backrest, cushion and floor, simultaneously collecting subjective evaluation data. The consistency between subjective and objective data was verified using Stevens' power law, with the <i>R</i><sup>2</sup> exceeding 70%, indicating subjective evaluations can reflect driver comfort. Furthermore, a deep learning model integrating multimodal coherent features was used for quantitative evaluation. The results show that the method accurately captures frequency characteristics affecting comfort, with the metrics <i>R</i><sup>2</sup>, <i>RMSE</i> and <i>MAE</i> being 0.931, 0.096 and 0.071, respectively. This is comparable to the evaluations of ergonomics experts. The proposed method provides a promising solution for driver comfort evaluation. It is significant for enhancing driver health, comfort and safety.</p>\",\"PeriodicalId\":50503,\"journal\":{\"name\":\"Ergonomics\",\"volume\":\" \",\"pages\":\"1-22\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2025-09-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ergonomics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1080/00140139.2025.2553136\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, INDUSTRIAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ergonomics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/00140139.2025.2553136","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
Evaluation method for driver comfort under multi axis coherent vibration of seats.
Ergonomics increasingly emphasises that seat design should align with the driver's physiological needs to enhance comfort and health. This study uses deep learning to evaluate the impact of seat multi-axis coherent vibration on driver comfort. Through road tests, the multi-axis vibration signals were collected from the seat backrest, cushion and floor, simultaneously collecting subjective evaluation data. The consistency between subjective and objective data was verified using Stevens' power law, with the R2 exceeding 70%, indicating subjective evaluations can reflect driver comfort. Furthermore, a deep learning model integrating multimodal coherent features was used for quantitative evaluation. The results show that the method accurately captures frequency characteristics affecting comfort, with the metrics R2, RMSE and MAE being 0.931, 0.096 and 0.071, respectively. This is comparable to the evaluations of ergonomics experts. The proposed method provides a promising solution for driver comfort evaluation. It is significant for enhancing driver health, comfort and safety.
期刊介绍:
Ergonomics, also known as human factors, is the scientific discipline that seeks to understand and improve human interactions with products, equipment, environments and systems. Drawing upon human biology, psychology, engineering and design, Ergonomics aims to develop and apply knowledge and techniques to optimise system performance, whilst protecting the health, safety and well-being of individuals involved. The attention of ergonomics extends across work, leisure and other aspects of our daily lives.
The journal Ergonomics is an international refereed publication, with a 60 year tradition of disseminating high quality research. Original submissions, both theoretical and applied, are invited from across the subject, including physical, cognitive, organisational and environmental ergonomics. Papers reporting the findings of research from cognate disciplines are also welcome, where these contribute to understanding equipment, tasks, jobs, systems and environments and the corresponding needs, abilities and limitations of people.
All published research articles in this journal have undergone rigorous peer review, based on initial editor screening and anonymous refereeing by independent expert referees.