用于人体关节角度监测和手势识别的高耐久性、抗紫外线石墨烯基针织物传感套筒

IF 6.8 Q1 AUTOMATION & CONTROL SYSTEMS Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2024-06-02 DOI:10.1002/aisy.202400124
Yi Zhou, Yilin Sun, Yangfangzheng Li, Cheng Shen, Zhiyuan Lou, Xue Min, Rebecca Stewart
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引用次数: 0

摘要

基于纺织品的柔性应变传感器因其重量轻、柔性好、佩戴舒适而受到广泛关注。然而,将纺织品应变传感器集成到可穿戴传感设备所面临的挑战包括:需要出色的传感性能、长期监测的稳定性,以及实现全面监测所需的快速、便捷的集成过程。本文介绍的可扩展制造技术通过将可定制的石墨烯传感网络集成到针织结构中,从而制造出用于精确运动检测和区分的传感套筒,解决了这些难题。在受试者内部和受试者之间的研究中,通过对角度估计和复杂关节运动识别的精度,对传感套筒的性能和实际应用潜力进行了评估。在受试者内部分析中,传感套筒在 20 名受试者的五种不同膝关节活动中仅表现出 2.34° 的角度误差,在膝关节和肘关节的手势分类中,传感套筒的准确率分别高达 94.1% 和 96.1%。在受试者之间的分析中,传感套筒的角度误差为 4.21°,膝关节和肘关节手势分类的准确率分别高达 79.9% 和 85.5%。为了说明其潜在的应用前景,我们还展示了一个与传感套筒兼容的活动引导用户界面,用于家庭医疗保健应用中的人体运动监测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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A Highly Durable and UV-Resistant Graphene-Based Knitted Textile Sensing Sleeve for Human Joint Angle Monitoring and Gesture Differentiation

Flexible strain sensors based on textiles have attracted extensive attention owing to their light weight, flexibility, and comfort when wearing. However, challenges in integrating textile strain sensors into wearable sensing devices include the need for outstanding sensing performance, long-term monitoring stability, and fast, convenient integration processes to achieve comprehensive monitoring. The scalable fabrication technique presented here addresses these challenges by incorporating customizable graphene-based sensing networks into knitted structures, thus creating sensing sleeves for precise motion detection and differentiation. The performance and real-world application potential of the sensing sleeve are evaluated by its precision in angle estimation and complex joint motion recognition during intra- and intersubject studies. For intra-subject analysis, the sensing sleeve only exhibits a 2.34° angle error in five different knee activities among 20 participants, and the sensing sleeves show up to 94.1% and 96.1% accuracy in the gesture classification of knee and elbow, respectively. For inter-subject analysis, the sensing sleeve demonstrates a 4.21° angle error, and it shows up to 79.9% and 85.5% accuracy in the gesture classification of knee and elbow, respectively. An activity-guided user interface compatible with the sensing sleeves for human motion monitoring in home healthcare applications is presented to illustrate the potential applications.

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CiteScore
1.30
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审稿时长
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