Development of a Statistical Model of Reach Exertion Magnitude Perception for Use in Designing Truck Cabs

Open Access
Klein, Brittany Anne
Graduate Program:
Mechanical Engineering
Master of Science
Document Type:
Master Thesis
Date of Defense:
March 30, 2012
Committee Members:
  • Matthew B Parkinson, Thesis Advisor
  • reach envelopes
  • spherical harmonics
  • anthropometry
  • design for human variability
  • truck cabs
  • ergonomics
The goal of this work was to develop a statistical model to describe a user’s perception of the difficulty of performing various reaching tasks in a truck cab as a function of the reach location. Towards this end, a piecewise model that would yield a surface of points corresponding to the desired difficulty rating for a specified user as a function of his or her anthropometry was developed. This model consisted of a linear combination of the first four spherical harmonics, with the coefficients of each term in the model changing according to the desired difficulty rating. The model was developed using data gathered from an experimental study performed by the University of Michigan Transportation Research Institute (UMTRI) in which volunteers performed reaches to a variety of points and rated each point according to their perception of the reach task’s difficulty. Nonlinear regression techniques and individual subject anthropometry were used to fit to fit the model to the data. The model successfully replicated the experimental results, with residual sum of squares values varying from 0.624 for the lowest difficulty rating to 0.992 for the highest. Concerns over unexpected ordering of the iso-rating surfaces generated by the model led to a revision of the model that classified reach locations as “easy,” “acceptable,” or “difficult” to provide a more generalized model that was useful for predicting difficulty ratings of individuals not present in the experiment. An application of the anthropometry of individuals recorded in the U.S. Army Anthropometry Survey (ANSUR) to the model indicated that the model can be used to predict reach surfaces for populations of individuals other than the experimental group. Future iterations of the model are expected to benefit from including additional data such as the force exerted during the reach tasks in the model calculations. In addition, changing the scale of difficulty ratings used in experiments may improve accuracy if a less subjective scale, possibly with fewer divisions, could be implemented successfully.