Analysis and recognition of operations using SEMG from upper arm muscles

Veer, Karan; Vig, Renu

doi:10.1111/exsy.12221

Cited by 8 publications

(3 citation statements)

References 25 publications

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“…Veer et al applied neural network classifier to implement the classification of sEMG signals from upper arm muscles, and the best classification accuracy was 89.3% [ 24 ]. Zhang et al recognized sEMG signals based on human motion intention, and the classification accuracy of upper limb signals based on SVM classifier was improved, ranging from 90.33% to 91.1% [ 25 ].…”

Section: Resultsmentioning

confidence: 99%

Human Posture Detection Method Based on Wearable Devices

Zhou

et al. 2021

Journal of Healthcare Engineering

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The dynamic detection of human motion is important, which is widely applied in the fields of motion state capture and rehabilitation engineering. In this study, based on multimodal information of surface electromyography (sEMG) signals of upper limb and triaxial acceleration and plantar pressure signals of lower limb, the effective virtual driving control and gait recognition methods were proposed. The effective way of wearable human posture detection was also constructed. Firstly, the moving average window and threshold comparison were used to segment the sEMG signals of the upper limb. The standard deviation and singular values of wavelet coefficients were extracted as the features. After the training and classification by optimized support vector machine (SVM) algorithm, the real-time detection and analysis of three virtual driving actions were performed. The average identification accuracy was 90.90%. Secondly, the mean, standard deviation, variance, and wavelet energy spectrum of triaxial acceleration were extracted, and these parameters were combined with plantar pressure as the gait features. The optimized SVM was selected for the gait identification, and the average accuracy was 90.48%. The experimental results showed that, through different combinations of wearable sensors on the upper and lower limbs, the motion posture information could be dynamically detected, which could be used in the design of virtual rehabilitation system and walking auxiliary system.

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Section: Resultsmentioning

confidence: 99%

Human Posture Detection Method Based on Wearable Devices

Zhou

et al. 2021

Journal of Healthcare Engineering

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“…The performance of surface electromyography sEMG-based hand movement recognition systems is predominately affected and limited by: (1) acquisition setup, (2) protocol design, (3) signal pre-processing, (4) feature extraction, and (5) classifier design, and to date researchers have offered numerous advances in order to enhance system functionality [1][2][3][4][5][6][7][8][9][10].…”

Section: Introduction *mentioning

confidence: 99%

Effect of the sEMG electrode (re)placement and feature set size on the hand movement recognition

Miljković

Isaković

2021

Biomedical Signal Processing and Control

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“…Three types of noise occur: power line interference, white Gaussian noise, and baseline wandering. 5 Therefore, denoising sEMG signals is a prerequisite 6 for analyzing and applying sEMG information.…”

Section: Introductionmentioning

confidence: 99%

Denoising of surface electromyogram based on complementary ensemble empirical mode decomposition and improved interval thresholding

Zhang

Zhao

et al. 2019

Review of Scientific Instruments

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Surface electromyogram (sEMG) signals are physiological signals that are widely applied in certain fields. However, sEMG signals are frequently corrupted by noise, which can lead to catastrophic consequences. A novel scheme based on complementary ensemble empirical mode decomposition (CEEMD), improved interval thresholding (IT), and component correlation analysis is developed in this study to reduce noise contamination. To solve the problem of losing desired information from sEMG, an sEMG signal is first decomposed using CEEMD to obtain intrinsic mode functions (IMFs). Subsequently, IMFs are selected via component correlation analysis, which is a measure used to select relevant modes. Thus, each selected IMF is modified through improved IT. Finally, the sEMG signal is reconstructed using the processed and residual IMFs. Root-mean-square error (RMSE) and signal-to-noise ratio (SNR) are introduced as evaluation criteria for the sEMG signal from the standard database. With SNR varying from 1 dB to 25 dB, the proposed method increases SNR by at least 1 dB and reduces RMSE compared with stationary wavelet transform and other denoising algorithms based on empirical mode decomposition. Moreover, the proposed method is applied to hand motion recognition. Results show that the rate of the denoised sEMG signal is higher than that of the raw sEMG signal.

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Analysis and recognition of operations using SEMG from upper arm muscles

Cited by 8 publications

References 25 publications

Human Posture Detection Method Based on Wearable Devices

Human Posture Detection Method Based on Wearable Devices

Effect of the sEMG electrode (re)placement and feature set size on the hand movement recognition

Denoising of surface electromyogram based on complementary ensemble empirical mode decomposition and improved interval thresholding

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