Hostname: page-component-76fb5796d-x4r87 Total loading time: 0 Render date: 2024-04-25T21:57:55.530Z Has data issue: false hasContentIssue false
  • English
  • Français

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: cjj@nwpu.edu.cn
Get access Rights & Permissions [Opens in a new window]

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
Check for updates
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Arnrich, B, Kappeler-Setz, C, Schumm, J and Trooster, G (2010a) Design, Implementation and Evaluation of a Multimodal Sensor System Integrated Into an Airplane Seat. Sensor Fusion – Foundation and Applications.Google Scholar
Arnrich, B, Setz, C, Marca, RL, Troster, G and Ehlert, U (2010b) What does your chair know about your stress level? IEEE Transactions on Information Technology in Biomedicine 14, 207–214.CrossRefGoogle Scholar
Bermejo, P, Gamez, JA and Puerta, JM (2014) Speeding up incremental wrapper feature subset selection with Naive Bayes classifier. Knowledge Based Systems 55, 140–147.CrossRefGoogle Scholar
Breiman, L (2001) Random forests. Machine Learning 45, 5–32.CrossRefGoogle Scholar
Burges, CJC (1998) A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2, 121–167.CrossRefGoogle Scholar
Cascioli, V, Heusch, AI and Mccarthy, PW (2011) Does prolonged sitting with limited legroom affect the flexibility of a healthy subject and their perception of discomfort? International Journal of Industrial Ergonomics 41, 471–480.CrossRefGoogle Scholar
Ciaccia, FRDA and Sznelwar, LI (2012) An approach to aircraft seat comfort using interface pressure mapping. Work 41, 240.CrossRefGoogle ScholarPubMed
Cover, T and Hart, P (2003) Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13, 21–27.CrossRefGoogle Scholar
De Looze, MP, Kuijt-Evers, LFM and Dieën Jaap, VAN (2003) Sitting comfort and discomfort and the relationships with objective measures. Ergonomics 46, 985–997.CrossRefGoogle ScholarPubMed
Ellegast, RP, Kraft, K, Groenesteijn, L, Krause, F, Berger, H and Vink, P (2012) Comparison of four specific dynamic office chairs with a conventional office chair: impact upon muscle activation, physical activity and posture. Applied Ergonomics 43, 296–307.CrossRefGoogle ScholarPubMed
Fazlollahtabar, H (2010) A subjective framework for seat comfort based on a heuristic multi criteria decision making technique and anthropometry. Applied Ergonomics 42, 16–28.CrossRefGoogle ScholarPubMed
Foubert, N, McKee, AM, Goubran, RA and Knoefel, F (2012) Lying and sitting posture recognition and transition detection using a pressure sensor array. MeMeA 2012–2012 IEEE International Symposium on Medical Measurements and Applications Proceedings, Budapest, pp. 1–6.CrossRefGoogle Scholar
Franz, M, Kamp, I, Durt, A and Kilincsoy, Ü (2011) A light weight car seat shaped by human body contour. International Journal of Human Factors Modelling and Simulation 2, 314–326.CrossRefGoogle Scholar
Gao, S, Zhang, N, Duan, GY, Yang, Z and Zhang, T (2010) Prediction of function changes associated with single-point protein mutations using support vector machines (SVMs). Human Mutation 30, 1161–1166.CrossRefGoogle Scholar
Gholipour, A and Arjmand, N (2016) Artificial neural networks to predict 3D spinal posture in reaching and lifting activities; applications in biomechanical models. Journal of Biomechanics 49, 2946–2952.CrossRefGoogle ScholarPubMed
Groenesteijn, L, Ellegast, RP, Keller, K, Krause, F and Looze, MPD (2012) Office task effects on comfort and body dynamics in five dynamic office chairs. Applied Ergonomics 43, 320–328.CrossRefGoogle ScholarPubMed
Hales, TR and Bernard, BP (1996) Epidemiology of work-related musculoskeletal disorders. Orthopedic Clinics of North America 27, 679.CrossRefGoogle ScholarPubMed
Haller, M, Richter, C, Brandl, P, Gross, S and Inami, M (2011) Finding the right way for interrupting people improving their sitting posture. Human-Computer Interaction-Interact - IFIP TC13 International Conference.CrossRefGoogle Scholar
Hiemstra-van, S, Meyenborg, I, and Hoogenhout, M (2016) The influence of activities and duration on comfort and discomfort development in time of aircraft passengers. Work 54, 955–961.CrossRefGoogle Scholar
Huang, YR and Ouyang, XF (2013) Sitting posture detection and recognition using force sensor. International Conference on Biomedical Engineering & Informatics.CrossRefGoogle Scholar
Jongryun, R, Hyeong-Jun, P, Kwang, JL and Hyeong, J (2018) Sitting posture monitoring system based on a low-cost load cell using machine learning. Sensors 18, 208.Google Scholar
Kamiya, K, Kudo, M, Nonaka, H and Toyama, J (2008) Sitting posture analysis by pressure sensors. International Conference on Pattern Recognition.Google Scholar
Kamp, I, Kilincsoy, U and Vink, P (2011) Chosen postures during specific sitting activities. Ergonomics 54, 1029–1042.CrossRefGoogle ScholarPubMed
Kolich, M (2004) Predicting automobile seat comfort using a neural network. International Journal of Industrial Ergonomics 33, 285–293.CrossRefGoogle Scholar
Konz, MRCA (2002) Leg swelling, comfort and fatigue when sitting, standing, and sit/standing. International Journal of Industrial Ergonomics 29, 289–296.Google Scholar
Kyung, G and Nussbaum, MA (2013) Age-related difference in perceptual responses and interface pressure requirements for driver seat design. Ergonomics 56, 1795–1805.CrossRefGoogle ScholarPubMed
Lan, M, Ke, L and Wu, C (2010) A sitting posture surveillance system based on image processing technology. International Conference on Computer Engineering & Technology.CrossRefGoogle Scholar
Le, P, Rose, J, Knapik, G and Marras, WS (2014) Objective classification of vehicle seat discomfort. Ergonomics 57, 536–544.CrossRefGoogle ScholarPubMed
Lengsfeld, M, Van Deursen, DL, Rohlmann, A, Van Deursen, LL and Griss, P (2000) Spinal load changes during rotatory dynamic sitting. Clinical Biomechanics 15, 295–297.CrossRefGoogle ScholarPubMed
Lewis, L, Patel, H, D'Cruz, M, and Cobb, S (2017) What makes a space invader? Passenger perceptions of personal space invasion in aircraft travel. Ergonomics 60, 1–28.CrossRefGoogle ScholarPubMed
Li, YC, Wang, PD, Johan, G, Jen, SL and Young, MS (1999) The development of a distributed pressure measurement cushion to prevent bad sitting posture. Proceedings of the First Joint BMES/EMBS Conference. 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Annual Fall Meeting of the Biomedical Engineering Society (Cat. N, Atlanta, GA, USA), p. 611.CrossRefGoogle Scholar
Li, W, Yu, S, Yang, H, Pei, H, Zhao, C (2017) Effects of long-duration sitting with limited space on discomfort, body flexibility, and surface pressure. International Journal of Industrial Ergonomics 58, 12–24.CrossRefGoogle Scholar
Luttmann, A, Schmidt, KH and Jaeger, M (2010) Working conditions, muscular activity and complaints of office workers. International Journal of Industrial Ergonomics 40, 549–559.CrossRefGoogle Scholar
Ma, C, Li, W, Gravina, R and Fortino, G (2017) Posture detection based on smart cushion for wheelchair users. Sensors 17, 719.CrossRefGoogle ScholarPubMed
Mastrigt, HV, Groenesteijn, L, Vink, P and Kuijt-Evers, LFM (2016) Predicting passenger seat comfort and discomfort on the basis of human, context and seat characteristics: a literature review. Ergonomics 60, 1–44.Google Scholar
Meyer, J, Arnrich, B, Schumm, J and Troster, G (2010) Design and modeling of a textile pressure sensor for sitting posture classification. IEEE Sensors Journal 10, 1391–1398.CrossRefGoogle Scholar
Moes, NCCM (2007) Variation in sitting pressure distribution and location of the points of maximum pressure with rotation of the pelvis, gender and body characteristics. Ergonomics 50, 536–561.CrossRefGoogle ScholarPubMed
Mota, S and Picard, RW (2003) Automated posture analysis for detecting learner's interest level. Proceedings CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition 5, p. 49.Google Scholar
Mota, S and Picard, RW (2008) Automated posture analysis for detecting learner's interest level. 2003 Conference on Computer Vision and Pattern Recognition Workshop IEEE.CrossRefGoogle Scholar
Mutlu, B, Krause, A, Forlizzi, J, Guestrin, C, and Hodgins, J (2007) Robust, low-cost, non-intrusive sensing and recognition of seated postures. ACM Symposium on User Interface Software & Technology.CrossRefGoogle Scholar
Safavian, SR and Landgrebe, D (2002) A survey of decision tree classifier methodology. IEEE Transactions on Systems Man & Cybernetics 21, 660–674.CrossRefGoogle Scholar
Schrempf, A, Schossleitner, G, Minarik, T, Haller, M and Gross, S (2011) Posturecare – towards a novel system for posture monitoring and guidance. IFAC Proceedings Volumes 44, 593–598.CrossRefGoogle Scholar
Smulders, M, Berghman, K, Koenraads, M, Kane, JA, Krishna, K, Carter, TK and Schultheis, U (2016) Comfort and pressure distribution in a human contour shaped aircraft seat (developed with 3D scans of the human body). Work 54, 1–16.CrossRefGoogle Scholar
Song-Lin, W and Rong-Yi, C (2010) Human behavior recognition based on sitting postures. 2010 International Symposium on Computer, Communication, Control and Automation (3CA), Tainan, pp. 138–141.CrossRefGoogle Scholar
Szeto, GPY, Straker, L and Raine, S (2002) A field comparison of neck and shoulder postures in symptomatic and asymptomatic office workers. Applied Ergonomics 33, 75–84.CrossRefGoogle ScholarPubMed
Tan, HZ and Ebert, DS (2002) Sensing chair as an input device for human computer interaction.Google Scholar
Tan, HZ, Slivovsky, LA and Pentland, A (2001) A sensing chair using pressure distribution sensors. IEEE/ASME Transactions on Mechatronics 6, 261–268.CrossRefGoogle Scholar
Tessendorf, B, Arnrich, B, Schumm, J, Setz, C and Troster, G (2009) Unsupervised monitoring of sitting behavior. Conference proceedings: … Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 6197–6200.CrossRefGoogle Scholar
Vanacore, A, Lanzotti, A, Percuoco, C, Capasso, A and Vitolo, B (2019) Design and analysis of comparative experiments to assess the (dis-)comfort of aircraft seating. Applied Ergonomics 76, 155–163.CrossRefGoogle ScholarPubMed
Vos, GA, Congleton, JJ, Moore, JS, Amendola, AA, Ringer, L (2006) Postural versus chair design impacts upon interface pressure. Applied Ergonomics 37, 619–628.CrossRefGoogle ScholarPubMed
Xu, L, Gang, C, Wang, J, Shen, R and Shen, Z (2012) A sensing cushion using simple pressure distribution sensors. Multisensor Fusion & Integration for Intelligent Systems.CrossRefGoogle Scholar
Xu, W, Huang, MC, Amini, N and He, L (2013) Ecushion: a textile pressure sensor array design and calibration for sitting posture analysis. IEEE Sensors Journal 13, 3926–3934.CrossRefGoogle Scholar
Yoo, WG, Yi, CH, Kim, MH (2006) Effects of a proximity-sensing feedback chair on head, shoulder, and trunk postures when working at a visual display terminal. Journal of Occupational Rehabilitation 16, 631–637.CrossRefGoogle Scholar
Zemp, R, Taylor, WR, Lorenzetti, S (2015) Are pressure measurements effective in the assessment of office chair comfort/discomfort? A review. Applied Ergonomics 48, 273–282.CrossRefGoogle ScholarPubMed
Zemp, R, Tanadini, M, Plüss, S, Schnüriger, K, Singh, NB, Taylor, WR and Lorenzetti, S (2016) Application of machine learning approaches for classifying sitting posture based on force and acceleration sensors. BioMed Research International 2016, 1–9.CrossRefGoogle ScholarPubMed
Zhu, M, Martinez, AM, Tan, HZ (2003) Template-based recognition of static sitting postures. Conference on Computer Vision & Pattern Recognition Workshop.CrossRefGoogle Scholar
Zubic, S (2007) Sensitive chair: a force sensing chair with multimodal real-time feedback via agent. Proceedings of the 14th European Conference on Cognitive Ergonomics: Invent! Explore!, ECCE 2007, London, UK, August 28–31, 2007.Google Scholar