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利用深度学习分析汽车座椅上的体压分布。

Analysis of body pressure distribution on car seats by using deep learning.

机构信息

Research Institute for Science and Engineering, Waseda University, Shinjuku, Tokyo, 169-8555, Japan.

Seating Development, NHK Spring Co. Ltd, Kanazawa-ku, Yokohama, 236-0004, Japan.

出版信息

Appl Ergon. 2019 Feb;75:283-287. doi: 10.1016/j.apergo.2018.08.023. Epub 2018 Nov 21.

Abstract

This study aimed to extract information from body pressure distribution, including comfort, participant body size, and seat characteristics by using supervised deep learning, and body pressure characteristics corresponding to sensory evaluation by using unsupervised deep learning. Body pressure data of 18 participants and 19 kinds of car seats were used for the analysis. Sensory evaluation of 9 items concerning cushion characteristics and seat comfort was conducted. From the analysis, we determined that body size and car seats could be classified with high precision by using body pressure distribution data. For the sensory evaluation items, the correct answer rate was high. By examining the importance of the cells of the mat, the features of the body pressure mat at the seat cushion and backrest, body size, car seat, and parts related to sensory evaluation could be determined in detail. The study findings can be applied in the development of car seats.

摘要

本研究旨在通过有监督的深度学习从体压分布中提取信息,包括舒适度、参与者体型和座椅特征,以及通过无监督的深度学习提取与感官评价相对应的体压特征。分析中使用了 18 名参与者和 19 种汽车座椅的体压数据。对与坐垫特性和座椅舒适度有关的 9 项感官评价进行了测试。分析结果表明,利用体压分布数据可以高精度地对体型和汽车座椅进行分类。对于感官评价项目,正确答案的准确率较高。通过检查垫子细胞的重要性,可以详细确定座垫和靠背、体型、汽车座椅以及与感官评价相关的部分的体压垫特征。研究结果可应用于汽车座椅的开发。

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