Multidirectional manual arm strength and its relationship with resultant shoulder moment and arm posture. Issue 12 (1st December 2016)

• Record Type: Journal Article
• Authors: La Delfa, Nicholas J.; Potvin, Jim R.
• Is Part Of: Ergonomics. Volume 59:Issue 12(2016)
• Page Start: 1625
• Page End: 1636
• Publication Date: 2016-12-01
• Subjects: Manual arm strength -- strength capabilities -- upper extremity strength -- maximum shoulder moment; Human engineering -- Periodicals; Cybernetics -- Periodicals; Industrial management -- Periodicals; Ergonomie -- Périodiques; Cybernétique -- Périodiques; Gestion d'entreprise -- Périodiques; 620.8205
• DOI: 10.1080/00140139.2016.1157628
• Languages: English
• ISSNs: 0014-0139

Abstract

Abstract: Previous work has quantified manual force capabilities for ergonomics design, but the number of studies and range of conditions tested are limited in scope. Therefore, the aims of this study were to collect seated manual arm strength (MAS) data from 24 females in several unique exertion directions ( n = 26) and hand locations relative to the shoulder ( n = 8), and to investigate the associations between MAS and shoulder/elbow moments. MAS was generally highest when the direction of force application was oriented parallel to the vector from the shoulder to knuckle, and weakest when oriented orthogonal to that vector. Moderate correlations were found between MAS and: (1) resultant shoulder moment ( r = 0.34), (2) resultant moment arms ( r = −0.545) and (3) elbow flexion/extension moment ( r = 0.481). Our strength data will be used in the development of a comprehensive MAS predictive method, so that strength capabilities can be predicted to help design acceptable tasks in the workplace. Practitioner Summary: This study sought to enhance our understanding of one-handed manual arm strength capabilities for ergonomics task evaluations. Our findings provide researchers and practitioners with manual strength data for off-axis force directions, as well as hand locations not previously measured. These data will contribute to future methods for predicting strength capabilities.