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：与年龄增长及中风相关的肌肉骨骼力量衰退会削弱人们利用上肢完成日常活动的能力。尽管已有若干外骨骼案例，但由于缺乏传感器反馈及运动意图预测功能，这些设备仍需手动操作。本文介绍了一种智能上肢外骨骼系统，该系统采用基于云端的深度学习技术预测人体意图以实现力量增强。嵌入式柔性可穿戴传感器通过实时采集肌肉信号提供感觉反馈，同步计算后确定用户意图动作。该云端深度学习系统以200-250毫秒响应速度预测四种上肢关节运动，平均准确率达96.2%，表明外骨骼仅凭人体意图即可运行。此外，一组柔性气动装置通过提供最大897牛顿力与78.7毫米位移辅助预期动作。综合而言，基于意图驱动的外骨骼相较于无辅助外骨骼可将人体力量增强约5.15倍。本研究报告展示了一种基于机器学习云计算与感觉反馈、通过人体意图增强上肢关节运动的外骨骼机器人。