Feature Analysis for Classification of Physical Actions Using Surface EMG Data

Turlapaty, Anish C.; Gokaraju, Balakrishna

doi:10.1109/jsen.2019.2937979

Cited by 26 publications

(9 citation statements)

References 35 publications

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“…The feature extraction of sEMG signals is a key step in the classification of hand movements based on sEMG signals [ 40 ]. In this work, four features are extracted from the sub-band signals obtained by decomposing the sEMG signal by WPT to classify hand movements [ 41 , 42 ].…”

Section: Methodsmentioning

confidence: 99%

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A Novel Method for Hand Movement Recognition Based on Wavelet Packet Transform and Principal Component Analysis with Surface Electromyogram

Huo

Qin

et al. 2022

Computational Intelligence and Neuroscience

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As an input method of signal language, the hand movement classification technology has developed into one of the ways of natural human-computer interaction. The surface electromyogram (sEMG) signal contains abundant human movement information and has significant advantages as the input signal of human-computer interaction. However, how to effectively extract components from sEMG signals to improve the accuracy of hand motion classification is a difficult problem. Therefore, this work proposes a novel method based on wavelet packet transform (WPT) and principal component analysis (PCA) to classify six kinds of hand motions. The method applies WPT to decompose the sEMG signal into multiple sub-band signals. To efficiently extract the intrinsic components of the sEMG signal, the classification performance of different wavelet packet basis functions is evaluated. The PCA algorithm is used to reduce the dimension of the feature space composed of the features reflecting hand motions extracted from each sub-band signal. Besides, to ensure higher classification performance while reducing the dimension of the feature space by the PCA algorithm, the classification performance of different dimensions of the feature space is compared. In addition, the effects of the variability of the sEMG signal and the size of the window on the proposed method are further analyzed. The proposed method was tested on the sEMG for Basic Hand Movements Data Set and achieved an average accuracy of 96.03%. Compared with the existing research, the proposed method has better classification performance, which indicates that the research results can be applied to the fields of exoskeleton robot, rehabilitation training, and intelligent prosthesis.

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Section: Methodsmentioning

confidence: 99%

“…Feature extraction plays a vital role in the classification of hand movements based on sEMG signals [ 40 ]. Feature extraction is to convert the sEMG signal into a compact and information-rich feature space.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Hand Movement Recognition Based on Wavelet Packet Transform and Principal Component Analysis with Surface Electromyogram

Huo

Qin

et al. 2022

Computational Intelligence and Neuroscience

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“…Next, the extracted features are fed into MC-LS-SVM (multi class least square support vector machine) with RBF kernel for the discrimination of 10 normal activities and the system achieved an accuracy of 98.17% as compared to existing methods. In [18], Subasi, et al Furthermore, an improved classification framework for the multi-class problem is proposed in [21]. The EMG dataset has been taken from machine learning repository.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning and Signal Processing Based Analysis of sEMG Signals for Daily Action Classification

et al. 2022

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The way of living of many individuals around the world endures because of mental and physical disability associated with the movement of limbs. The usage of the assistive technology and systems will enhance the quality of affected people. In this situation, you can pave the way for a solution by transforming the movement of physical activities into computer-assisted applications. Surface Electromyography (sEMG) introduced the non-intervention procedure that can transform physical activities into signals for classification purposes and then practice it in applications. In this study, we suggest a scheme based on machine learning for the identification of 20 physical movements. This scheme follows up on the distinct characteristics from numerous signatures that include time-domain features, frequency-domain features, and inter-channel statistics of an sEMG signal. Afterward, we performed a thorough comparative examination of the k-NN and SVM classifier by considering the group of features for multiple normal and aggressive activities. The impact of different arrangements of dimensionalities has been recorded as well. Eventually, the SVM classifier gives 100% accuracy for 10 normal actions whereas 1-NN for a subgroup of features achieves 98.91% accuracy for 10 aggressive actions respectively. Additionally, we combine both SVM and 1-NN to propose a hybrid approach to classify 20 physical actions. The hybrid classifier gives an accuracy of 98.97% respectively. These recommendations are valuable for algorithm designers to select the finest approach by considering the resources available for the execution of an algorithm.

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“…In the research [41], the performance of the proposed technique was evaluated using online testing but system was not realized on embedded platform. In the studies [39,40,9], only off-line analysis was performed and no real-time adaptation for the system. Moreover, the complex classification algorithms such as wavelet neural network and convolutional neural network were adopted.…”

Section: Motion Efficacymentioning

confidence: 99%

Novel Time Domain Based Upper-Limb Prosthesis Control using Incremental Learning Approach

Pancholi¹,

Joshi²

2021

Preprint

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The upper limb of the body is a vital for various kind of activities for human. The complete or partial loss of the upper limb would lead to a significant impact on daily activities of the amputees. EMG carries important information of human physique which helps to decode the various functionalities of human arm. EMG signal based bionics and prosthesis have gained huge research attention over the past decade. Conventional EMG-PR based prosthesis struggles to give accurate performance due to off-line training used and incapability to compensate for electrode position shift and change in arm position. This work proposes online training and incremental learning based system for upper limb prosthetic application. This system consists of ADS1298 as AFE (analog front end) and a 32 bit arm cortex-m4 processor for DSP (digital signal processing). The system has been tested for both intact and amputated subjects. Time derivative moment based features have been implemented and utilized for effective pattern classification. Initially, system have been trained for four classes using the on-line training process later on the number of classes have been incremented on user demand till eleven, and system performance has been evaluated. The system yielded a completion rate of 100% for healthy and amputated subjects when four motions have been considered. Further 94.33% and 92% completion rate have been showcased by the system when the number of classes increased to eleven for healthy and amputees respectively. The motion efficacy test is also evaluated for all the subjects. The highest efficacy rate of 91.23% and 88.64% are observed for intact and amputated subjects respectively.

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Feature Analysis for Classification of Physical Actions Using Surface EMG Data

Cited by 26 publications

References 35 publications

A Novel Method for Hand Movement Recognition Based on Wavelet Packet Transform and Principal Component Analysis with Surface Electromyogram

A Novel Method for Hand Movement Recognition Based on Wavelet Packet Transform and Principal Component Analysis with Surface Electromyogram

Machine Learning and Signal Processing Based Analysis of sEMG Signals for Daily Action Classification

Novel Time Domain Based Upper-Limb Prosthesis Control using Incremental Learning Approach

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