The age and stroke-associated decline in musculoskeletal strength degrades the ability to perform daily human tasks using the upper extremities. Here, we introduce an intelligent upper-limb exoskeleton system that utilizes deep learning to predict human intention for strength augmentation. The embedded soft wearable sensors provide sensory feedback by collecting real-time muscle activities, which are simultaneously computed to determine the user’s intended movement. Cloud-based deep learning predicts four upper-limb joint motions with an average accuracy of 96.2% at a 500–550 ms 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 newtons of force while generating a displacement of 87 mm at maximum. The intent-driven exoskeleton can reduce human muscle activities by 3.7 times on average compared to the unassisted exoskeleton.

年龄和中风相关的肌肉骨骼力量下降会降低使用上肢执行日常人类任务的能力。在这里，我们介绍了一种智能上肢外骨骼系统，该系统利用深度学习来预测人类增强力量的意图。嵌入式软可穿戴传感器通过收集实时肌肉活动来提供感官反馈，同时计算这些活动以确定用户的预期运动。基于云的深度学习以 500-550 毫秒的响应率预测四种上肢关节运动，平均准确率为 96.2%，这表明外骨骼仅根据人类意图进行操作。此外，一系列软气动装置可提供 897 牛顿的力，同时产生最大 87 毫米的位移，从而辅助预期的运动。与无辅助外骨骼相比，意图驱动外骨骼可以平均减少人体肌肉活动3.7倍。