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Citation |
Pal C, Tomosaburo O, Vimalathithan K, Jeyabharath M, Muthukumar M, Satheesh N, Narahari S. Accid. Anal. Prev. 2014; 72C: 193-209. |
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Affiliation |
Renault Nissan Technology Business Centre India, Chennai, India. |
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Copyright |
(Copyright © 2014, Elsevier Publishing) |
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DOI |
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PMID |
25079104 |
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Abstract |
A comprehensive analysis is performed to evaluate the effect of weight, height and body mass index (BMI) of occupants on side impact injuries at different body regions. The accident dataset for this study is based on the National Automotive Sampling System-Crashworthiness Data System (NASS-CDS) for accident year 2000-08. The mean BMI values for driver and front passenger are estimated from all types of crashes using NASS database, which clearly indicates that mean BMI has been increasing over the years in the USA. To study the effect of BMI in side impact injuries, BMI was split into three groups namely (1) thin (BMI<21), (2) normal (BMI 24-27), (3) obese (BMI>30). For more clear identification of the effect of BMI in side impact injuries, a minimum gap of three BMI is set in between each adjacent BMI groups. Car model years from MY1995-1999 to MY2000-2008 are chosen in order to identify the degree of influence of older and newer generation of cars in side impact injuries. Impact locations particularly side-front (F), side-center (P) and side-distributed (Y) are chosen for this analysis. Direction of force (DOF) considered for both near side and far side occupants are 8 o'clock, 9 o'clock, 10 o'clock and 2 o'clock, 3 o'clock and 4 o'clock respectively. Age <60 years is also one of the constraints imposed on data selection to minimize the effect of bone strength on the occurrence of occupant injuries. AIS2+ and AIS3+ injury risk in all body regions have been plotted for the selected three BMI groups of occupant, delta-V 0-60kmph, two sets (old and new) of car model years. The analysis is carried with three approaches: (a) injury risk percentage based on simple graphical method with respect to a single variable, (b) injury distribution method where the injuries are marked on the respective anatomical locations and (c) logistic regression, a statistical method, considers all the related variables together. Lower extremity injury risk appears to be high for thin BMI group. It is found that BMI does not have much influence on head injuries but it is influenced more by the height of the occupant. Language: en |