Predicting Distal Arm Demand from Task Requirements

The hand and fingers can grip an object in many ways and can exert (grip) forces on the object and transmit forces and moments to the environment. This paper describes an approach that directly relates task demands to distal arm demand. Graded contractions (forces, moments and combinations) between zero and maximum in five grips were applied by 40 adults (20M, 20F). Surface electromyography (EMG) from eight muscle of the forearm and hand and a rating of perceived exertion (RPE) were recorded. Artificial neural network modeling was used to describe the relationship between the forces and moments exerted and the EMG and RPE. Use of the approach is demonstrated.