Model-based development of neuroprosthesis for paraplegic patients

Area of Science:

• Biomedical Engineering
• Neuroscience
• Rehabilitation Technology

Background:

• Paraplegic patients with upper motor neuron lesions experience interrupted neural signals to muscles.
• Functional electrical stimulation (FES) offers a way to bypass these interrupted signals.
• Developing effective neuroprostheses for controlling multiple joints with FES is challenging due to system complexity and physiological factors like fatigue and spasticity.

Purpose of the Study:

• To explore the role of mathematical models in the development of neuroprostheses for paraplegic patients.
• To present model-based approaches for designing and testing FES control strategies.
• To demonstrate how musculoskeletal modeling can enhance understanding and performance of neuroprosthetic systems.

Main Methods:

• Literature review of existing work in musculoskeletal modeling for neuroprosthesis development.
• Presentation of two author-developed examples of model-based neuroprosthesis design.
• Utilizing mathematical models to simulate and analyze musculoskeletal system dynamics, muscle force production, and movement coordination.

Main Results:

• Musculoskeletal modeling provides deeper insights into muscle force generation and movement coordination.
• Models facilitate the design and evaluation of FES stimulation patterns and feedback control strategies.
• Incorporating model components into controllers improves performance and can mitigate issues like muscle fatigue.

Conclusions:

• Mathematical models are crucial for advancing the design of neuroprostheses for paraplegic individuals.
• Model-based approaches lead to improved controller performance, reduced need for human subject testing, and optimized muscular force output.
• The development of reliable, closed-loop controlled, lower extremity neuroprostheses is increasingly reliant on mathematical modeling.