Targeted muscle effort distribution with exercise robots: Trajectory and resistance effects

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.

Real-time optimization of an ellipsoidal trajectory orientation using muscle effort with Extremum Seeking Control

We present an approach for real-time model-free optimization of the orientation of the elliptical trajectory. The performance is evaluated in simulation and experimental stages. Our model-free approach is based on the use of Extremum Seeking Control (ESC) as the real-time optimizer. The experimental stage is performed using a 4 degrees-of-freedom robot and its impedance control system to create advanced exercise protocols whereby the user is asked to follow a path against the machine's neutral path and resistance. Another model-free approach based on the use of the global optimizer Biogeography-based optimization (BBO) was previously reported for simulation results. This last framework has a good performance as a result of exhaustive searches but with a high computational cost limiting its use on real-time experiments. The performance of the ESC approach was validated by comparing the results with those of BBO using five different arm models representing real human arms. In the real-time experiments, muscle activations representing the participation of each muscle in the training activity were measured with electromyography sensors (EMG) and real-time processed from raw signals. The muscle objective can be professionally selected by a therapist to emphasize or de-emphasize certain muscle groups. 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 force/torque applied by the subject. The results show that the framework was able to successfully find the optimal ellipsoidal orientation converging to similar solutions in short period trials of 50 s.