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Characterizing Vehicle Occupant Body Dimensions and Postures Using a Statistical Body Shape Model
ISSN: 0148-7191, e-ISSN: 2688-3627
Published March 28, 2017 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Reliable, accurate data on vehicle occupant characteristics could be used to personalize the occupant experience, potentially improving both satisfaction and safety. Recent improvements in 3D camera technology and increased use of cameras in vehicles offer the capability to effectively capture data on vehicle occupant characteristics, including size, shape, posture, and position. In previous work, the body dimensions of standing individuals were reliably estimated by fitting a statistical body shape model (SBSM) to data from a consumer-grade depth camera (Microsoft Kinect). In the current study, the methodology was extended to consider seated vehicle occupants. The SBSM used in this work was developed using laser scan data gathered from 147 children with stature ranging from 100 to 160 cm and BMI from 12 to 27 kg/m2 in various sitting postures. A principal component (PC) analysis was conducted based on these scans along with the manually-measured body landmarks, and 100 PC scores were retained to account for 99% of variance in the body shape and sitting postures. A PC-based fast fitting method was applied to estimate the occupant characteristics by fitting the SBSM to an incomplete depth image of a subject. The results demonstrate that a fast, inexpensive system can be used to produce useful estimates of occupant characteristics that could be applied to improve personalization of component adjustments, restraint systems, and infotainment systems.
CitationPark, B. and Reed, M., "Characterizing Vehicle Occupant Body Dimensions and Postures Using a Statistical Body Shape Model," SAE Technical Paper 2017-01-0497, 2017, https://doi.org/10.4271/2017-01-0497.
Data Sets - Support Documents
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- Park , Byoung-Keon , Lumeng Julie C. , Lumeng Carey N. , Ebert Sheila M. , and Reed Matthew P. Child body shape measurement using depth cameras and a statistical body shape model Ergonomics 58 2 2015 301 309
- Park , Byoung-Keon , and Reed Matthew P. Parametric body shape model of standing children aged 3-11 years Ergonomics 58 10 2015 1714 1725
- Tong , Jing , Zhou Jin , Liu Ligang , Pan Zhigeng , and Yan Hao Scanning 3d full human bodies using kinects IEEE transactions on visualization and computer graphics 18 4 2012 643 650
- Yu , Wurong , and Xu Bugao A portable stereo vision system for whole body surface imaging Image and vision computing 28 4 2010 605 613
- Biswas , Kanad K. , and Basu Saurav Kumar Gesture recognition using microsoft kinect® Automation, Robotics and Applications (ICARA), 2011 5th International Conference on 100 103 IEEE 2011
- Fern'ndez-Baena , Adso , Susín Antonio , and Lligadas Xavier Biomechanical validation of upper-body and lower-body joint movements of kinect motion capture data for rehabilitation treatments Intelligent Networking and Collaborative Systems (INCoS), 2012 4th International Conference on 656 661 IEEE 2012
- Dutta , Tilak Evaluation of the Kinect™ sensor for 3-D kinematic measurement in the workplace Applied ergonomics 43 4 2012 645 649
- Allen , Brett , Curless Brian , and Popović Zoran The space of human body shapes: reconstruction and parameterization from range scans In ACM transactions on graphics (TOG) 22 3 587 594 ACM 2003
- Reed Matthew P. , and Parkinson Matthew B. Modeling variability in torso shape for chair and seat design In ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference 561 569 American Society of Mechanical Engineers 2008