Towards AI-controlled FES-restoration of movements: Learning cycling stimulation pattern with reinforcement learning

by   Nat Wannawas, et al.

Functional electrical stimulation (FES) has been increasingly integrated with other rehabilitation devices, including robots. FES cycling is one of the common FES applications in rehabilitation, which is performed by stimulating leg muscles in a certain pattern. The appropriate pattern varies across individuals and requires manual tuning which can be time-consuming and challenging for the individual user. Here, we present an AI-based method for finding the patterns, which requires no extra hardware or sensors. Our method has two phases, starting with finding model-based patterns using reinforcement learning and detailed musculoskeletal models. The models, built using open-source software, can be customised through our automated script and can be therefore used by non-technical individuals without extra cost. Next, our method fine-tunes the pattern using real cycling data. We test our both in simulation and experimentally on a stationary tricycle. In the simulation test, our method can robustly deliver model-based patterns for different cycling configurations. The experimental evaluation shows that our method can find a model-based pattern that induces higher cycling speed than an EMG-based pattern. By using just 100 seconds of cycling data, our method can deliver a fine-tuned pattern that gives better cycling performance. Beyond FES cycling, this work is a showcase, displaying the feasibility and potential of human-in-the-loop AI in real-world rehabilitation.


page 1

page 3


Towards AI-controlled FES-restoration of arm movements: neuromechanics-based reinforcement learning for 3-D reaching

Reaching disabilities affect the quality of life. Functional Electrical ...

Model-Based Multiple Instance Learning

While Multiple Instance (MI) data are point patterns -- sets or multi-se...

MBRL-Lib: A Modular Library for Model-based Reinforcement Learning

Model-based reinforcement learning is a compelling framework for data-ef...

An Evaluation of Generative Pre-Training Model-based Therapy Chatbot for Caregivers

With the advent of off-the-shelf intelligent home products and broader i...

Low-level Pose Control of Tilting Multirotor for Wall Perching Tasks Using Reinforcement Learning

Recently, needs for unmanned aerial vehicles (UAVs) that are attachable ...

DTT: An Example-Driven Tabular Transformer by Leveraging Large Language Models

Many organizations rely on data from government and third-party sources,...

Physics-informed reinforcement learning via probabilistic co-adjustment functions

Reinforcement learning of real-world tasks is very data inefficient, and...

Please sign up or login with your details

Forgot password? Click here to reset