  The objective of this work is to relate muscle effort distributions to the
trajectory and resistance settings of a robotic exercise and rehabilitation
machine. Muscular effort distribution, representing the participation of each
muscle in the training activity, was measured with electromyography sensors
(EMG) and defined as the individual activation divided by the total muscle
group activation. A four degrees-of-freedom robot and its impedance control
system are used to create advanced exercise protocols whereby the user is asked
to follow a path against the machine's neutral path and resistance. In this
work, the robot establishes a zero-effort circular path, and the subject is
asked to follow an elliptical trajectory. The control system produces a
user-defined stiffness between the deviations from the neutral path and the
torque applied by the subject. The trajectory and resistance settings used in
the experiments were the orientation of the ellipse and a stiffness parameter.
Multiple combinations of these parameters were used to measure their effects on
the muscle effort distribution. An artificial neural network (ANN) used part of
the data for training the model. Then, the accuracy of the model was evaluated
using the rest of the data. The results show how the precision of the model is
lost over time. These outcomes show the complexity of the muscle dynamics for
long-term estimations suggesting the existence of time-varying dynamics
possibly associated with fatigue.
