Linked Life Data

20191412

Source:http://linkedlifedata.com/resource/pubmed/id/20191412

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2010-3-1
pubmed:abstractText
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 52.4 degrees 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. STATEMENT OF RELEVANCE: This study identified drivers' distinct postural strategies, based on actual drivers' behaviours. Such strategies can facilitate accurate positioning of DHMs and hence help design ergonomic driver workspaces.
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Mar
pubmed:issn
1366-5847
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
53
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
375-84
pubmed:dateRevised
2010-6-29
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Enhancing digital driver models: identification of distinct postural strategies used by drivers.
pubmed:affiliation
School of Design & Human Engineering, UNIST, 100 Banyeon-ri, Eonyang-eup, Ulju-gun, Ulsan, Korea. ghkyung@unist.ac.kr [corrected]
pubmed:publicationType
Journal Article