title: The 'Arm Force Field' method to predict manual arm strength based on only hand location and force direction.
abstract: This paper describes the development of a novel method (termed the 'Arm Force Field' or 'AFF') to predict manual arm strength (MAS) for a wide range of body orientations, hand locations and any force direction. This method used an artificial neural network (ANN) to predict the effects of hand location and force direction on MAS, and included a method to estimate the contribution of the arm's weight to the predicted strength. The AFF method predicted the MAS values very well (r2= 0.97, RMSD = 5.2 N, n = 456) and maintained good generalizability with external test data (r2= 0.842, RMSD = 13.1 N, n = 80). The AFF can be readily integrated within any DHM ergonomics software, and appears to be a more robust, reliable and valid method of estimating the strength capabilities of the arm, when compared to current approaches.
authors.affiliation: Department of Kinesiology, University of Waterloo, Waterloo, Ontario, Canada.
authors.forename: Nicholas J
authors.initials: NJ
authors.lastname: La Delfa
authors.affiliation: Department of Kinesiology, McMaster University, Hamilton, Ontario, Canada. Electronic address: potvinj@mcmaster.ca.
authors.forename: Jim R
authors.initials: JR
authors.lastname: Potvin
mesh_terms: D000328:Adult; D001132:Arm / Q000502:physiology*; D006804:Ergonomics / Q000379:methods; D005260:Female; D006112:Gravitation; D006225:Hand / Q000502:physiology*; D006801:Humans; D008875:Middle Aged; D053580:Muscle Strength; D016571:Neural Networks, Computer; D011187:Posture; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012984:Software; D055815:Young Adult
keywords: Acceptable hand loads; Artificial neural network; Manual arm strength; Strength capabilities