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用于手势识别的液态金属柔性 EMG 凝胶电极

Liquid Metal Flexible EMG Gel Electrodes for Gesture Recognition.

机构信息

School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China.

School of Advanced Engineering, University of Science and Technology Beijing, Beijing 100083, China.

出版信息

Biosensors (Basel). 2023 Jun 29;13(7):692. doi: 10.3390/bios13070692.

Abstract

Gesture recognition has been playing an increasingly important role in the field of intelligent control and human-computer interaction. Gesture recognition technology based on electromyography (EMG) with high accuracy has been widely applied. However, conventional rigid EMG electrodes do not fit the mechanical properties of human skin. Therefore, rigid EMG electrodes are easily influenced by body movements, and uncomfortable to wear and use for a long time. To solve these problems, a stretchable EMG electrode based on liquid metal nanoparticles was developed in this research. It is conformal with human skin because of its similar mechanical properties to skin. Liquid metal nanoparticles mixed in polymer can be connected to each other to form conductive circuits when pressed by mechanical force. Therefore, this preparation method of liquid metal flexible gel electrodes is low-cost and can be fabricated largely. Moreover, the liquid metal flexible gel electrodes have great stretch ability. Their resistance increases slightly at maximum strain state. Based on these advantages, the flexible gel electrodes are applied to arm to collect EMG signals generated by human hand movements. In addition, the signals are analyzed by artificial intelligence algorithm to realize accurate gesture recognition.

摘要

手势识别在智能控制和人机交互领域发挥着越来越重要的作用。基于肌电图(EMG)的高精度手势识别技术得到了广泛应用。然而,传统的刚性 EMG 电极不符合人体皮肤的机械特性。因此,刚性 EMG 电极容易受到身体运动的影响,长时间佩戴和使用会感到不适。为了解决这些问题,本研究开发了一种基于液态金属纳米粒子的可拉伸 EMG 电极。由于其机械性能与皮肤相似,因此与皮肤贴合。当受到机械力的挤压时,混合在聚合物中的液态金属纳米粒子可以相互连接,形成导电回路。因此,这种液态金属柔性凝胶电极的制备方法成本低廉,可以大规模制造。此外,液态金属柔性凝胶电极具有很大的拉伸能力。在最大应变状态下,其电阻仅略有增加。基于这些优势,柔性凝胶电极被应用于手臂,以采集人体手部运动产生的 EMG 信号。此外,这些信号通过人工智能算法进行分析,以实现准确的手势识别。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f7e8/10377211/e12a120d92de/biosensors-13-00692-g001.jpg

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