Analysis and Recognition of Human Lower Limb Motions Based on Electromyography (EMG) Signals

Wang, Junyao; Dai, Yuehong; Si, Xiaxi

doi:10.3390/electronics10202473

Cited by 28 publications

(11 citation statements)

References 19 publications

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“…Sensor Types of motion Accuracy Prediction time SVM [33] sEMG and IMU 4 94.29% -LDA [58] sEMG 5 94.26% -SVM [59] EMG and ground reaction force (GRF) 3 96.80% -SVM [60] EMG and plantar stress distribution (PSD) 5 96.53% -BP neural network [41] EMG and IMU 5 93.67% -SVM [42] sEMG and mechanical 7 98.00% 372.63 ms NIDA-TCN-SDF (our) sEMG and IMU 4 97.66% 51.106 ms potential of DA-TCNs needs to be further explored.…”

Section: Classifier Methodsmentioning

confidence: 99%

“…With the time series analysis, it was well known that more and more attention is being paid to TCNs. TCN was a recently proposed new technique for CNNbased sequence models learning the hidden information and time dependencies of a given input signal smoothly and efficiently, and outperforming the more burdensome and complex, while state-of-theart, RNNs, among the many challenges of sequence analysis modeling [41]. The 1D convolutional layer smoothes the input data along the time dimension and has been shown to exhibit superior results on video recognition datasets [11][12][13]42], and sEMG decoding work [9,10].…”

Section: Temporal Convolutional Network (Tcns)mentioning

confidence: 99%

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Non-invasive dual attention TCN for electromyography and motion data fusion in lower limb ambulation prediction

Zhou

Feng²,

Wang³

et al. 2022

J. Neural Eng.

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Objective. Recent technological advances show the feasibility of fusing surface electromyography (sEMG) signals and movement data to predict lower limb ambulation intentions. However, since the invasive fusion of different signals is a major impediment to improving predictive performance, searching for a non-invasive fusion mechanism for lower limb ambulation pattern recognition based on different modal features is crucial. Approach. We propose an end-to-end sequence prediction model with non-invasive dual attention temporal convolutional networks (NIDA-TCN) as a core to elegantly address the essential deficiencies of traditional decision models with heterogeneous signal fusion. Notably, the NIDA-TCN is a weighted fusion of sEMG and inertial measurement units (IMU) with time-dependent effective hidden information in the temporal and channel dimensions using TCN and self-attentive mechanisms. The new model can better discriminate between walking, jumping, downstairs, and upstairs four lower limb activities of daily living (ADL). Main results. The results of this study show that the NIDA-TCN models produce predictions that significantly outperform both frame-wise and TCN models in terms of accuracy, sensitivity, precision, F1 score, and stability. Particularly, the NIDA-TCN with sequence decision fusion (NIDA-TCN-SDF) models, have maximum accuracy and stability increments of 3.37% and 4.95% relative to the frame-wise model, respectively, without manual feature-encoding and complex model parameters. Significance. It is concluded that the results demonstrate the validity and feasibility of the NIDA-TCN-SDF models to ensure the prediction of daily lower limb ambulation activities, paving the way to the development of fused heterogeneous signal decoding with better prediction performance.

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Section: Classifier Methodsmentioning

confidence: 99%

Section: Temporal Convolutional Network (Tcns)mentioning

confidence: 99%

Non-invasive dual attention TCN for electromyography and motion data fusion in lower limb ambulation prediction

Zhou

Feng²,

Wang³

et al. 2022

J. Neural Eng.

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show abstract

“…Sensor Model Accuracy (%) [21] 7 IMU LSTM-CNN 97.78 [22] 4 IMU DDLMI 97.64 [40] IMU EMG BP 93.76 [41] EEG sEMG EDMEFNet 88.44 [42] 8 sEMG GA-DANN 94.89 [37] 5 IMU LSTM >95 Tis paper 1 IMU MCN 96.08…”

Section: Referencesmentioning

confidence: 99%

Human Activity Recognition Based on a Modified Capsule Network

Zhu

Chen

Liu

et al. 2023

Mobile Information Systems

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Human activity recognition (HAR) has attracted considerable research attention in the past decade with the development of wearable sensor technology and deep learning algorithms. However, most of the existing HAR methods ignored the spatial relationship of features, which may lead to recognition errors. In this paper, a novel model based on a modified capsule network (MCN) is proposed to accurately recognize various human activities. This novel model is composed of a convolution block and a capsule block, which can achieve end-to-end intelligent recognition. In the meantime, the spatial information among features is preserved through a dynamic routing process. To validate the effectiveness of the model, a human activity dataset is constructed by placing an inertial measurement unit (IMU) on the calf of the volunteers to collect their activity data in daily life, including walking, jogging, upstairs, downstairs, up-ramps, and down-ramps. The recognition accuracy of this novel approach can reach 96.08%, which performs better than the convolutional neural network (CNN) with an accuracy of 91.62%. In addition, it is evaluated on two public datasets named WISDM and UCI-HAR, and the accuracies achieve 98.21% and 95.28%, respectively, which presents higher accuracy than the reported results obtained from benchmark algorithms like CNN. The experimental results show that the proposed model has better activity detection capability and achieves outstanding performance for HAR.

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“…[ 24 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ] Moreover, EMG and sEMG have also been combined with inertial sensors to obtain more information. [ 34 , 35 , 36 , 37 ] sEMG, in particular, is more widely used than EMG due to its noninvasiveness and ease of use. Recently, in addition to traditional bipolar sEMG electrodes, multichannel sEMG devices with electrodes distributed in an array have been developed for applications in the detection of various human motions [ 6 , 38 , 39 ] and gesture recognition systems.…”

Section: Introductionmentioning

confidence: 99%

AI‐Enabled Soft Sensing Array for Simultaneous Detection of Muscle Deformation and Mechanomyography for Metaverse Somatosensory Interaction

Suo,

Liu,

Wang

et al. 2024

Advanced Science

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Motion recognition (MR)‐based somatosensory interaction technology, which interprets user movements as input instructions, presents a natural approach for promoting human‐computer interaction, a critical element for advancing metaverse applications. Herein, this work introduces a non‐intrusive muscle‐sensing wearable device, that in conjunction with machine learning, enables motion‐control‐based somatosensory interaction with metaverse avatars. To facilitate MR, the proposed device simultaneously detects muscle mechanical activities, including dynamic muscle shape changes and vibrational mechanomyogram signals, utilizing a flexible 16‐channel pressure sensor array (weighing ≈0.38 g). Leveraging the rich information from multiple channels, a recognition accuracy of ≈96.06% is achieved by classifying ten lower‐limb motions executed by ten human subjects. In addition, this work demonstrates the practical application of muscle‐sensing‐based somatosensory interaction, using the proposed wearable device, for enabling the real‐time control of avatars in a virtual space. This study provides an alternative approach to traditional rigid inertial measurement units and electromyography‐based methods for achieving accurate human motion capture, which can further broaden the applications of motion‐interactive wearable devices for the coming metaverse age.

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Analysis and Recognition of Human Lower Limb Motions Based on Electromyography (EMG) Signals

Cited by 28 publications

References 19 publications

Non-invasive dual attention TCN for electromyography and motion data fusion in lower limb ambulation prediction

Non-invasive dual attention TCN for electromyography and motion data fusion in lower limb ambulation prediction

Human Activity Recognition Based on a Modified Capsule Network

AI‐Enabled Soft Sensing Array for Simultaneous Detection of Muscle Deformation and Mechanomyography for Metaverse Somatosensory Interaction

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