通过无线表面肌电图测量的时空卷积网络进行多分类步态相位分类

IF 2.2 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
V. Mallikarjuna Reddy M;P. S. Pandian;Karthick P A
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引用次数: 0

摘要

最近,康复领域的进步和发展导致为残疾患者发明了肌电控制界面。然而,从腘绳肌和股四头肌的表面肌电图(sEMG)信号中解码运动意图具有挑战性,这是因为与负重关节相关的复杂力学以及信号的随机、非稳态和多分量行为。在这封信中,我们提出了一种新方法,利用 sEMG 信号的时序卷积网络(TCN)对平地行走时的步态相位进行多级分类。为此,我们同时记录了 20 名健康参与者在跑步机上以 2.5 km/h 的速度平步行走时的 sEMG 和惯性测量单元(IMU)数据。sEMG 采集自肌肉,即股直肌(RF)、股外侧肌(VL)、股二头肌(BF)和半腱肌(SEM)。利用 IMU 测量的膝关节屈伸数据来标记步态周期的四个阶段。sEMG 时序的均方根用于设计 TCN 框架。结果表明,建议的框架能够利用所有四块肌肉的肌电活动区分步态的四个等级,准确率最高可达 86.00%。来自 SEM 和 VL 肌肉对以及 RF 和 BF 肌肉对的信息的正确检测率分别为 83.00% 和 84.00%。此外,与卷积神经网络架构的准确率相比,TCN 的准确率也提高了 6%。研究结果表明,所提出的方法能有效解码下肢肌肉的运动意图,这可能有助于开发下肢假肢的精确运动控制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multiclass Gait Phase Classification From the Temporal Convolutional Network of Wireless Surface Electromyography Measurements
Recent advancements and developments in the field of rehabi- litation lead to the invention of myoelectric control interfaces for patients with disabilities. However, decoding the motion intent from the surface electromyography (sEMG) signals of hamstrings and quadriceps is challenging due to its complex mechanics associated with weight bearing joints and stochastic, nonstationary, and multicomponent behavior of signals. In this letter, a novel approach is proposed for multiclass gait phase classification during level walking using temporal convolutional network (TCN) of sEMG signals. For this purpose, sEMG and inertial measurement unit (IMU) data were recorded concurrently from 20 healthy participants during level walking on treadmill at a speed of 2.5 km/h. sEMG were collected from the muscles, namely, rectus femoris (RF), vastus lateralis (VL), biceps femoris (BF), and semitendinosus (SEM). The IMU measurements of knee flexion/extension data are utilized for labeling the four phases of gait cycle. The root mean square of sEMG epochs is used to design the TCN framework. The results show that the proposed framework has the ability to differentiate the four classes of gait with a maximum accuracy of 86.00% using the myoelectric activity from all the four muscles. The information from the muscle pairs SEM and VL, and RF and BF, yielded the correct detection rate of 83.00% and 84.00%, respectively. In addition, the accuracy is also improved by 6% with TCN when we compare accuracy obtained through convolutional neural network architecture. The findings suggest that the proposed approach is effective in decoding the motion intent of lower limb muscles, which may lead to the development of precise movement control of lower limb prosthesis.
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来源期刊
IEEE Sensors Letters
IEEE Sensors Letters Engineering-Electrical and Electronic Engineering
CiteScore
3.50
自引率
7.10%
发文量
194
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