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Intelligent upper-limb exoskeleton integrated with soft bioelectronics and deep learning for intention-driven augmentation

Engineering and Technology

Intelligent upper-limb exoskeleton integrated with soft bioelectronics and deep learning for intention-driven augmentation

J. Lee, K. Kwon, et al.

This groundbreaking research by Jinwoo Lee, Kangkyu Kwon, Ira Soltis, and colleagues unveils an intelligent upper-limb exoskeleton that uses deep learning to predict human movement intentions, achieving remarkable accuracy. By utilizing embedded soft wearable sensors, the system enhances strength and reduces muscle effort, paving the way for revolutionary applications in rehabilitation and assistance.

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Playback language: English
Abstract
This paper introduces an intelligent upper-limb exoskeleton system utilizing deep learning to predict human intention for strength augmentation. Embedded soft wearable sensors collect real-time muscle activities to determine intended movement. Cloud-based deep learning predicts four upper-limb joint motions with 96.2% accuracy and a 500–550 ms response rate. Soft pneumatics assist movements with 897 N of force and 87 mm displacement. The exoskeleton reduces muscle activity by 3.7 times compared to unassisted movement.
Publisher
npj Flexible Electronics
Published On
Feb 10, 2024
Authors
Jinwoo Lee, Kangkyu Kwon, Ira Soltis, Jared Matthews, Yoon Jae Lee, Hojoong Kim, Lissette Romero, Nathan Zavanelli, Youngjin Kwon, Shinjae Kwon, Jimin Lee, Yewon Na, Sung Hoon Lee, Ki Jun Yu, Minoru Shinohara, Frank L. Hammond, Woon-Hong Yeo
Tags
upper-limb exoskeleton
deep learning
human intention
muscle activity
strength augmentation
cloud-based prediction
soft pneumatics

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