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A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

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A study on feedback error learning controller for functional electrical stimulation: generation of target

Takashi Watanabe¹, Keisuke Fukushima

¹Graduate School of Biomedical Engineering Graduate School of Engineering, Tohoku University, Sendai, Japan. nabet@bme.tohoku.ac.jp

Artificial Organs

|March 16, 2011

Summary

This study enhanced functional electrical stimulation (FES) wrist control using an artificial neural network (ANN) trained with minimum jerk trajectories. The inverse dynamics model (IDM) improved tracking for unlearned movements, performing best when trained on varied trajectories.

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Area of Science:

Neuroscience
Biomedical Engineering
Robotics

Background:

Previous studies demonstrated the efficacy of Feedback Error Learning (FEL) controllers for functional electrical stimulation (FES) of wrist movements.
Artificial neural networks (ANNs) were previously trained for specific sinusoidal trajectories, limiting adaptability.

Purpose of the Study:

To investigate the application of ANNs trained with minimum jerk trajectories for two-point reaching movements in FES wrist control.
To evaluate the impact of training data variability on the performance of the inverse dynamics model (IDM).

Main Methods:

Computer simulations were conducted using target trajectories generated by the minimum jerk model.
ANNs were trained with varying numbers of target trajectories over 50 control trials.
The performance of the trained ANN as an inverse dynamics model (IDM) was assessed based on tracking accuracy and feedback controller output power.

Main Results:

The IDM, realized by the trained ANN, significantly reduced feedback controller output power.
Improved tracking performance was observed for unlearned target trajectories.
The IDM demonstrated optimal effectiveness when the ANN was trained with target trajectories changing every control trial.

Conclusions:

Training ANNs with diverse, minimum jerk trajectories enhances the adaptability of FES wrist control systems.
The IDM approach shows significant potential for improving FES control by enabling adaptation to novel movements.
Varying target trajectories during ANN training is crucial for maximizing the IDM's performance in FES applications.