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.

翻译：得益于丰富的感知能力和精确的执行能力，机器人在为脑卒中等运动障碍患者提供专业且个性化的康复训练方面展现出巨大潜力。自主机器人康复能够显著减轻漫长康复过程中的人力负担。然而，由于数据稀缺问题，训练康复机器人面临挑战。这一挑战源于临床数据访问和使用过程中的隐私问题（例如患者疾病隐私信息和身份信息泄露的风险）。不同医院和患者间的数据无法共享以进行充分的机器人训练，这进一步影响了康复安全性并限制了应用范围。为解决此问题，本研究提出了一种新颖的联邦联合学习方法，实现跨医院的机器人协同训练。联邦联合学习采用长短期记忆网络-Transformer学习机制，有效探索患者运动状态与机器人康复动作之间复杂的时空关系。为验证联邦联合学习方法在机器人网络训练中的有效性，本研究设计了临床-仿真联合实验。实验采用了200名脑卒中患者（包括上肢偏瘫、帕金森综合征及背痛综合征）的真实康复训练数据，并通过临床数据反向驱动仿真生成了30万条机器人康复指导数据。实验证明，联邦联合学习在联合康复学习中表现优异，性能较基线方法提升20%至30%。