A hybrid modeling structure composed of a one degree of freedom computational musculo-skeletal model and a multilayer perceptron neural network was used to effectively map electromyography (EMG) from a human exercise trial to muscle activations in a physiologically feasible and accurate fashion. Several configurations of the complete hybrid system were used to map four muscle surface EMGs from a ballistic elbow flexion to normalized muscle activations, estimated individual muscle forces and torque about the joint. The net joint torque was used to train the neural portion of the hybrid system to minimize kinematic error. The model allowed the estimation of the nonobservable parameters: normalized muscle activations and forces which was used to penalize the learning system. With these parameters in the learning equation, our system produced muscle activations consistent with the classic triphasic response present in ballistic movements.

1.
Graupe
D.
,
Salahi
J.
, and
Zhang
D.
,
1985
, “
Stochastic Analysis of Myoelectric Temporal Signatures for Multifunctional Single-Site Activation of prosthesis and Orthoses
,”
J. Biomedical Eng.
, Vol.
7
, pp.
18
29
.
2.
Gonzalez, R., Hutchins, E., Barr, R., and Abraham, L., 1996, “Development and Evaluation of a Musculoskeletal Model of The Elbow Joint Complex,” ASME JOURNAL OF BIOMECHANICAL ENGINEERING, Vol. 118, Feb.
3.
Hannaford
B.
, and
Stark
L.
,
1985
, “
Roles of the Elements of the Triphasic Control Signal
,”
Experimental Neurophysiology
, Vol.
90
, pp.
619
634
.
This content is only available via PDF.
You do not currently have access to this content.