The Use of Standardized Forearm Emg Measures to Predict Single and Multi-Digit Forces

Abstract

Determination and integration of force requirements is an essential component of designing tools and workstations that involve the use of the hand and fingers. This study investigated the feasibility of circumventing direct force measurements through the use of linear models to estimate time varying forces from myoelectric measures. Such a process is of utility when designing and evaluating hand tools and human-machine interfaces involving finger intensive tasks, since the integration of task force requirements are necessary to reduce the risk of injury to the upper limbs. Surface electrodes were used to record electromyographic signals collected from three standardized electrode sites on the forearm. Multiple linear regression models were generated to predict finger force levels with the three normalized electromographic measures as predictor variables. The results suggest that standardized procedures for obtaining EMG data and simple linear models can be used to accurately predict finger forces (R²-adj: 0.77–0.88; standard error: 9.21 N – 12.42N) during controlled maximal exertions. Additional work is being performed to determine if the models can be generalized to more complex tasks.