The age and stroke-associated decline in musculoskeletal strength degrades the ability to perform daily human tasks using the upper extremities. Although there are a few examples of exoskeletons, they need manual operations due to the absence of sensor feedback and no intention prediction of movements. Here, we introduce an intelligent upper-limb exoskeleton system that uses cloud-based deep learning to predict human intention for strength augmentation. The embedded soft wearable sensors provide sensory feedback by collecting real-time muscle signals, which are simultaneously computed to determine the user's intended movement. The cloud-based deep-learning predicts four upper-limb joint motions with an average accuracy of 96.2% at a 200-250 millisecond response rate, suggesting that the exoskeleton operates just by human intention. In addition, an array of soft pneumatics assists the intended movements by providing 897 newton of force and 78.7 millimeter of displacement at maximum. Collectively, the intent-driven exoskeleton can augment human strength by 5.15 times on average compared to the unassisted exoskeleton. This report demonstrates an exoskeleton robot that augments the upper-limb joint movements by human intention based on a machine-learning cloud computing and sensory feedback.

翻译：年龄增长及中风导致的肌肉骨骼力量衰退会降低人使用上肢完成日常任务的能力。尽管已有若干外骨骼案例，但由于缺乏传感器反馈且无法预测运动意图，这些设备仍需手动操作。本文介绍一种智能上肢外骨骼系统，该系统利用基于云端的深度学习预测人类意图以实现力量增强。嵌入式软体可穿戴传感器通过实时采集肌肉信号提供感觉反馈，同步计算以确定用户意图动作。基于云端的深度学习能够以200-250毫秒的响应速度预测四种上肢关节运动，平均准确率达96.2%，表明外骨骼可仅凭人类意图驱动运行。此外，一组软体气动装置可提供最大897牛顿力和78.7毫米位移以辅助意图运动。综合而言，相比无辅助外骨骼，该意图驱动型外骨骼平均能增强人体力量5.15倍。本研究报告展示了一台基于机器学习云计算与感觉反馈、通过人类意图增强上肢关节运动的外骨骼机器人。