Driving Posture Evaluation through Electroencephalographic Measurement and Digital Human Modeling

2017-01-1394

03/28/2017

Features
Event
WCX™ 17: SAE World Congress Experience
Authors Abstract
Content
Drivers’ physical and physiological states change with prolonged driving. Driving for extended periods of time can lead to an increased risk of low back pain and other musculoskeletal disorders, caused by the discomfort of the seats. Static and dynamic are the two main categories must be considered within the seating development. The posture and orientation of the occupant are the important factors on static comfort. Driving posture measurement is essential for the evaluation of a driver workspace and improved seat comfort design. This study evaluated the comfortable driving posture through physiological and ergonomics measurements of an automotive premium driver seat. The physiological evaluation includes electroencephalographic (EEG) for brain waves, Biopac’s AcqKnowledge program, and subjective measurements on 32 healthy individuals. JACK simulation was used for the ergonomics evaluation, i.e., the magnitude of the spinal loads about lumbar vertebrae was estimated. Sixteen anthropometric characteristics of the population from Size Korea and Size US databases were used to model digital humans in the JACK simulation software. Both males and females with ranging ages (19-65 years) and size (25th, 50th, and 75th) were considered for ergonomics evaluation. Seat backrest angles ranged from 90° to 135° in 5° steps for both physiological and ergonomics evaluation. Results of the American and Korean anthropometric characteristics were significantly different: the results of EEG, subjective questionnaire of the healthy individuals, and the difference in lumbar spinal loads between the two ethnicities are discussed in this paper.
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DOI
https://doi.org/10.4271/2017-01-1394
Pages
6
Citation
Min, S., Subramaniyam, M., Hong, S., Kim, D. et al., "Driving Posture Evaluation through Electroencephalographic Measurement and Digital Human Modeling," SAE Technical Paper 2017-01-1394, 2017, https://doi.org/10.4271/2017-01-1394.
Additional Details
Publisher
Published
Mar 28, 2017
Product Code
2017-01-1394
Content Type
Technical Paper
Language
English