  Advanced by rich perception and precise execution, robots possess immense
potential to provide professional and customized rehabilitation exercises for
patients with mobility impairments caused by strokes. Autonomous robotic
rehabilitation significantly reduces human workloads in the long and tedious
rehabilitation process. However, training a rehabilitation robot is challenging
due to the data scarcity issue. This challenge arises from privacy concerns
(e.g., the risk of leaking private disease and identity information of
patients) during clinical data access and usage. Data from various patients and
hospitals cannot be shared for adequate robot training, further compromising
rehabilitation safety and limiting implementation scopes. To address this
challenge, this work developed a novel federated joint learning (FJL) method to
jointly train robots across hospitals. FJL also adopted a long short-term
memory network (LSTM)-Transformer learning mechanism to effectively explore the
complex tempo-spatial relations among patient mobility conditions and robotic
rehabilitation motions. To validate FJL's effectiveness in training a robot
network, a clinic-simulation combined experiment was designed. Real
rehabilitation exercise data from 200 patients with stroke diseases (upper limb
hemiplegia, Parkinson's syndrome, and back pain syndrome) were adopted.
Inversely driven by clinical data, 300,000 robotic rehabilitation guidances
were simulated. FJL proved to be effective in joint rehabilitation learning,
performing 20% - 30% better than baseline methods.
