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

Reiko Mitsuya, Kazuhito Kato, Nei Kou, Takeshi Nakamura, Kohei Sugawara, Hiroki Dobashi, Takuro Sugita, Takashi Kawai

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)283-287
Number of pages5
JournalApplied Ergonomics
Volume75
DOIs
Publication statusPublished - 2019 Feb

Keywords

  • Body pressure distribution
  • Car seat
  • Characteristics extraction
  • Deep learning
  • Machine learning
  • Support vector machine

ASJC Scopus subject areas

  • Human Factors and Ergonomics
  • Physical Therapy, Sports Therapy and Rehabilitation
  • Safety, Risk, Reliability and Quality
  • Engineering (miscellaneous)

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