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Sitting posture detection and recognition of aircraft passengers using machine learning

Published online by Cambridge University Press:  02 September 2021

Wenzhe Cun Open the ORCID record for Wenzhe Cun [Opens in a new window] ,
Rong Mo ,
Jianjie Chu ,
Suihuai Yu ,
Huizhong Zhang ,
Hao Fan ,
Yanhao Chen ,
Mengcheng Wang ,
Hui Wang  and
Chen Chen
Wenzhe Cun
Affiliation:
Shaanxi Engineering Laboratory for Industrial Design, Northwestern Polytechnical University, Xi'an 710072, PR China
Rong Mo
Affiliation:
Shaanxi Engineering Laboratory for Industrial Design, Northwestern Polytechnical University, Xi'an 710072, PR China
Jianjie Chu*
Affiliation:
Shaanxi Engineering Laboratory for Industrial Design, Northwestern Polytechnical University, Xi'an 710072, PR China
Suihuai Yu
Affiliation:
Shaanxi Engineering Laboratory for Industrial Design, Northwestern Polytechnical University, Xi'an 710072, PR China
Huizhong Zhang
Affiliation:
Shaanxi Engineering Laboratory for Industrial Design, Northwestern Polytechnical University, Xi'an 710072, PR China
Hao Fan
Affiliation:
Shaanxi Engineering Laboratory for Industrial Design, Northwestern Polytechnical University, Xi'an 710072, PR China
Yanhao Chen
Affiliation:
Shaanxi Engineering Laboratory for Industrial Design, Northwestern Polytechnical University, Xi'an 710072, PR China
Mengcheng Wang
Affiliation:
Shaanxi Engineering Laboratory for Industrial Design, Northwestern Polytechnical University, Xi'an 710072, PR China
Hui Wang
Affiliation:
Shaanxi Engineering Laboratory for Industrial Design, Northwestern Polytechnical University, Xi'an 710072, PR China
Chen Chen
Affiliation:
Shaanxi Engineering Laboratory for Industrial Design, Northwestern Polytechnical University, Xi'an 710072, PR China
*
Author for correspondence: Jianjie Chu, E-mail: [email protected]
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Abstract

Prolonged sitting in a fixed or constrained position exposes aircraft passengers to long-term static loading of their bodies, which has deleterious effects on passengers’ comfort throughout the duration of the flight. The previous studies focused primarily on office and driving sitting postures and few studies, however, focused on the sitting postures of passengers in aircraft. Consequently, the aim of the present study is to detect and recognize the sitting postures of aircraft passengers in relation to sitting discomfort. A total of 24 subjects were recruited for the experiment, which lasted for 2 h. Furthermore, a total of 489 sitting postures were extracted and the pressure data between subjects and seat was collected from the experiment. After the detection of sitting postures, eight types of sitting postures were classified based on key parts (trunk, back, and legs) of the human bodies. Thereafter, the eight types of sitting postures were recognized with the aid of pressure data of seat pan and backrest employing several machine learning methods. The best classification rate of 89.26% was obtained from the support vector machine (SVM) with radial basis function (RBF) kernel. The detection and recognition of the eight types of sitting postures of aircraft passengers in this study provided an insight into aircraft passengers’ discomfort and seat design.

Keywords

Aircraft passengercomfortdetectionmachine learningrecognitionseat designsitting posture
Type
Research Article
Information
AI EDAM , Volume 35 , Issue 3 , August 2021 , pp. 284 - 294
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

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