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Low-Cost Wearable Band Sensors of Surface Electromyography for Detecting Hand Movements
Sensors ( IF 3.4 ) Pub Date : 2022-08-09 , DOI: 10.3390/s22165931
Manuela Gomez-Correa 1, 2 , David Cruz-Ortiz 1
Affiliation  

Surface electromyography (sEMG) is a non-invasive measure of electrical activity generated due to muscle contraction. In recent years, sEMG signals have been increasingly used in diverse applications such as rehabilitation, pattern recognition, and control of orthotic and prosthetic systems. This study presents the development of a versatile multi-channel sEMG low-cost wearable band system to acquire 4 signals. In this case, the signals acquired with the proposed device have been used to detect hand movements. However, the WyoFlex band could be used in some sections of the arm or the leg if the section’s diameter matches the diameter of the WyoFlex band. The designed WyoFlex band was fabricated using three-dimensional (3D) printing techniques employing thermoplastic polyurethane and polylactic acid as manufacturing materials. Then, the proposed wearable electromyographic system (WES) consists of 2 WyoFlex bands, which simultaneously allow the wireless acquisition of 4 sEMG channels of each forearm. The collected sEMG can be visualized and stored for future post-processing stages using a graphical user interface designed in Node-RED. Several experimental tests were conducted to verify the performance of the WES. A dataset with sEMG collected from 15 healthy humans has been obtained as part of the presented results. In addition, a classification algorithm based on artificial neural networks has been implemented to validate the usability of the collected sEMG signals.

中文翻译:

用于检测手部运动的表面肌电图的低成本可穿戴带传感器

表面肌电图 (sEMG) 是一种非侵入性测量由于肌肉收缩而产生的电活动。近年来,sEMG 信号越来越多地用于各种应用,例如康复、模式识别以及矫形和假肢系统的控制。本研究介绍了一种多功能多通道 sEMG 低成本可穿戴带系统的开发,以获取 4 个信号。在这种情况下,使用所提出的设备获取的信号已用于检测手部运动。但是,如果部分的直径与 WyoFlex 带的直径匹配,则 WyoFlex 带可用于手臂或腿的某些部分。设计的 WyoFlex 表带采用三维 (3D) 打印技术制造,采用热塑性聚氨酯和聚乳酸作为制造材料。然后,拟议的可穿戴肌电图系统 (WES) 由 2 个 WyoFlex 频段组成,可同时无线采集每个前臂的 4 个 sEMG 通道。可以使用 Node-RED 设计的图形用户界面对收集的 sEMG 进行可视化和存储,以供将来的后处理阶段使用。为了验证 WES 的性能,我们进行了多项实验测试。作为呈现结果的一部分,已获得从 15 名健康人收集的带有 sEMG 的数据集。此外,已经实现了基于人工神经网络的分类算法,以验证收集到的 sEMG 信号的可用性。可以使用 Node-RED 设计的图形用户界面对收集的 sEMG 进行可视化和存储,以供将来的后处理阶段使用。为了验证 WES 的性能,我们进行了多项实验测试。作为呈现结果的一部分,已获得从 15 名健康人收集的带有 sEMG 的数据集。此外,已经实现了基于人工神经网络的分类算法,以验证收集到的 sEMG 信号的可用性。可以使用 Node-RED 设计的图形用户界面对收集的 sEMG 进行可视化和存储,以供将来的后处理阶段使用。为了验证 WES 的性能,我们进行了多项实验测试。作为呈现结果的一部分,已获得从 15 名健康人收集的带有 sEMG 的数据集。此外,已经实现了基于人工神经网络的分类算法,以验证收集到的 sEMG 信号的可用性。
更新日期:2022-08-09
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