Intelligent upper-limb exoskeleton integrated with soft wearable bioelectronics and deep-learning for human intention-driven strength augmentation based on sensory feedback

TL;DR

This paper presents an intelligent upper-limb exoskeleton that uses soft sensors and deep learning to predict human intention, enabling strength augmentation with high accuracy and real-time responsiveness.

Contribution

It introduces a novel cloud-based deep learning system integrated with soft wearable sensors for intention prediction and strength augmentation in upper-limb exoskeletons.

Findings

Achieved 96.2% accuracy in predicting four joint movements.

Provided up to 897 newtons of assistive force.

Augmented human strength by an average of 5.15 times.

Abstract

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.