  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.
