Biologically Inspired Optimal Terminal Iterative Learning Control for the Swing Phase of Gait in a Hybrid Neuroprosthesis: A Modeling Study

Bioengineering, 2022 · DOI: https://doi.org/10.3390/bioengineering9020071 · Published: February 12, 2022

Simple Explanation

This study developed a control strategy (BIOTILC) for a Motor-Assisted Hybrid Neuroprosthesis (MAHNP), which combines an exoskeletal brace with neural stimulation to help individuals with paraplegia walk. The BIOTILC strategy aims to swing the legs in a natural, ballistic motion by maximizing muscle use and using the motorized brace to assist as needed. The control algorithm was tested using a detailed computer model of the lower leg and pelvis, modified to include the exoskeletal brace, and the results showed that the controller could learn to balance muscle and motor contributions for consistent stepping.

Study Duration

Not specified

Participants

Anatomically realistic three-dimensional musculoskeletal model of the lower leg and pelvis

Evidence Level

Level 5: Modeling Study

Key Findings

1
The BIOTILC controller effectively learned to achieve the desired leg swing motion, balancing muscular and motor contributions to minimize error.
2
The controller was able to ensure adequate foot clearance during the swing phase and attain the correct leg position for weight acceptance at the end of the swing.
3
Even with simulated muscle fatigue reducing force production, the controller adapted and maintained performance by adjusting the balance between muscle and motor assistance.

Research Summary

This study introduces a biologically inspired control algorithm (BIOTILC) for a motor-assisted hybrid neuroprosthesis (MAHNP) designed to restore walking in individuals with paraplegia. The BIOTILC algorithm effectively coordinates muscle stimulation and motorized assistance to achieve a natural leg swing motion, maximizing muscle recruitment and adapting to muscle fatigue. Simulation results demonstrate the algorithm's ability to achieve desired swing phase configuration, ensure foot clearance, and adapt to changes in muscle strength.

Practical Implications

Clinical Application

The BIOTILC could be implemented as a way to optimize motor assistance and stimulation inputs via simulation prior to clinical implementation with a person in the loop.

Personalized Therapy

The findings indicate the feasibility of updating control in real-time when implemented with a physical exoskeleton and user with SCI to balance the contributions of motorized assistance and stimulated muscle outputs for ballistic control of swing.

Rehabilitation Robotics

The results suggest the controller can learn both the mechanical interactions as well as the complex contributions of the various stimulated muscles.

Study Limitations

1
The controller approach does not ensure reaching the targets with every step.
2
The signs of the initial gradient estimate are required, and some of the weighting factors may require retuning to ensure convergence and prevent oscillation.
3
The simulation cannot capture all the subtle dynamics of the physical system; therefore, some tuning will be required during physical implementation.

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