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可穿戴摩擦电传感器实现基于物联网的智能医疗保健应用中的步态分析和腰部运动捕捉。

Wearable Triboelectric Sensors Enabled Gait Analysis and Waist Motion Capture for IoT-Based Smart Healthcare Applications.

机构信息

Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China.

School of Artificial Intelligence, Shanghai University, Shanghai, 200444, China.

出版信息

Adv Sci (Weinh). 2022 Feb;9(4):e2103694. doi: 10.1002/advs.202103694. Epub 2021 Nov 19.

Abstract

Gait and waist motions always contain massive personnel information and it is feasible to extract these data via wearable electronics for identification and healthcare based on the Internet of Things (IoT). There also remains a demand to develop a cost-effective human-machine interface to enhance the immersion during the long-term rehabilitation. Meanwhile, triboelectric nanogenerator (TENG) revealing its merits in both wearable electronics and IoT tends to be a possible solution. Herein, the authors present wearable TENG-based devices for gait analysis and waist motion capture to enhance the intelligence and performance of the lower-limb and waist rehabilitation. Four triboelectric sensors are equidistantly sewed onto a fabric belt to recognize the waist motion, enabling the real-time robotic manipulation and virtual game for immersion-enhanced waist training. The insole equipped with two TENG sensors is designed for walking status detection and a 98.4% identification accuracy for five different humans aiming at rehabilitation plan selection is achieved by leveraging machine learning technology to further analyze the signals. Through a lower-limb rehabilitation robot, the authors demonstrate that the sensory system performs well in user recognition, motion monitoring, as well as robot and gaming-aided training, showing its potential in IoT-based smart healthcare applications.

摘要

步态和腰部运动总是包含大量的人员信息,通过物联网(IoT)基于可穿戴电子设备提取这些数据进行识别和医疗保健是可行的。同时,人们仍然需要开发一种具有成本效益的人机界面,以增强长期康复过程中的沉浸感。而摩擦纳米发电机(TENG)在可穿戴电子设备和物联网方面的优势,使其成为一种可能的解决方案。在这里,作者提出了基于可穿戴 TENG 的设备,用于步态分析和腰部运动捕捉,以提高下肢和腰部康复的智能性和性能。四个摩擦电传感器等距缝制在织物带上,以识别腰部运动,实现实时机器人操作和沉浸式虚拟游戏的腰部训练。带有两个 TENG 传感器的鞋垫用于检测行走状态,通过机器学习技术进一步分析信号,实现对 5 名不同人的 98.4%的识别准确率,以选择康复计划。通过下肢康复机器人,作者证明了传感系统在用户识别、运动监测以及机器人和游戏辅助训练方面表现良好,展示了其在基于物联网的智能医疗保健应用中的潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e342/8811828/c2061bb3270d/ADVS-9-2103694-g002.jpg

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