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Agreement between a frequency-weighted filter for continuous biomechanical measurements of repetitive wrist flexion against a load and published psychophysical data.

Lin-ML; Radwin-RG

Ergonomics 1998 Apr; 41(4):459-475

https://doi.org/10.1080/001401398186946

20030565

A previous pilot study demonstrated that a force and frequency-weighted filter network could be developed for processing continuous biomechanical measures of repetitive wrist motions and exertions. The current study achieves the objective by modelling subjective discomfort for repetitive wrist flexion using controlled posture, pace and force. A three-level fractional factorial experiment was conducted involving repetitive wrist flexion (2 s/motion, 6 s/motion, 10 s/motion) from a neutral posture to a given angle (10 degrees, 28 degrees, 45 degrees) against a controlled resistance (5 N, 25 N, 50 N) using a Box Behnken design. Ten subjects participated. Discomfort was reported on a 10 cm visual analogue scale. Results of response surface regression analysis revealed that main effects of force, wrist flexion angle, and repetition were all significant (p < 0.05) and that no second-order effects were observed. Linear regression analysis on these factors established a discomfort model on which the filter characteristics were based. The pure error test model revealed no significant lack of fit (p > 0.05). The continuous model was compared and agreed with discrete psychophysical data from other published studies. The model was used for generating parameters for a force and frequency-weighted digital filter that weighs continuous wrist postural signals with corresponding force in proportion to the equal discomfort function as a function of frequency of repetition. These filters will enable integration of large quantities of biomechanical data in field studies.

Biomechanics; Repetitive-work; Posture; Biomechanical-modeling; Models; Cumulative-trauma-disorders

ERGOAX

19980401

Journal Article

Grant

1998

Grant-Number-K03-OH-000170

4

0014-0139

Ergonomics

WI