Targeted Muscle Effort Distribution with Exercise Robots: Trajectory and Resistance Effects

TL;DR

This study investigates how different trajectory shapes and resistance levels in robotic exercise machines influence muscle effort distribution, revealing complex, time-varying muscle dynamics and the challenges in modeling fatigue effects.

Contribution

It introduces a method to relate muscle effort to robot-controlled trajectory and resistance, and demonstrates the limitations of neural network models over time due to fatigue.

Findings

Muscle effort distribution varies with trajectory and resistance settings.

Neural network model accuracy decreases over time, indicating fatigue effects.

Muscle dynamics are complex and time-varying, especially during long-term use.

Abstract

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.