Enhancing digital driver models: Identification of distinct postural strategies used by drivers
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- Enhancing digital driver models: Identification of distinct postural strategies used by drivers
- Kyung, Gyouhyung; Nussbaum, Maury A.; Babski-Reeves, Kari L.
- Anthropometrics; Body mass; Design ergonomics; Digital drivers; Digital human models; Driving posture; Joint angle; Lower limb; Postural strategies; Statistical clustering; Upper limbs; Workspace design
- Issue Date
- TAYLOR & FRANCIS LTD
- ERGONOMICS, v.53, no.3, pp.375 - 384
- Driver workspace design and evaluation is, in part, based on assumed driving postures of users and determines several ergonomic aspects of a vehicle, such as reach, visibility and postural comfort. Accurately predicting and specifying standard driving postures, hence, are necessary to improve the ergonomic quality of the driver workspace. In this study, a statistical clustering approach was employed to reduce driving posture simulation/prediction errors, assuming that drivers use several distinct postural strategies when interacting with automobiles. 2-D driving postures, described by 16 joint angles, were obtained from 38 participants with diverse demographics (age, gender) and anthropometrics (stature, body mass) and in two vehicle classes (sedans and SUVs). Based on the proximity of joint angle sets, cluster analysis yielded three predominant postural strategies in each vehicle class (i. e. ` lower limb flexed', ` upper limb flexed' and ` extended'). Mean angular differences between clusters ranged from 3.8 to 52.48 for the majority of joints, supporting the practical relevance of the distinct clusters. The existence of such postural strategies should be considered when utilising digital human models (DHMs) to enhance and evaluate driver workspace design ergonomically and proactively.
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