Channel selection in multi-channel surface electromyogram based hand activity classifier

Gupta, Rinki; Saxena, Shantanu; Sazid, Abdul

doi:10.1109/ciact.2018.8480172

Cited by 5 publications

(1 citation statement)

References 12 publications

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“…Similar work by Oskoei et al [ 14 ] employed a multiobjective genetic searching algorithm with the objective function of data separability index or classification rate. Besides, filter methods have also been applied to rank the channels, where the minimum Redundancy Maximum Relevance (mRMR) [ 15 ] was used by Liu et al [ 16 ] and Gupta et al [ 17 ], the Relief-F by Qu et al [ 18 ], and the Markov random field (MRF) by Qu et al [ 16 ] as well.…”

Section: Related Research and Motivationmentioning

confidence: 99%

Reduce Surface Electromyography Channels for Gesture Recognition by Multitask Sparse Representation and Minimum Redundancy Maximum Relevance

Shang

et al. 2021

Journal of Healthcare Engineering

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Surface electromyography- (sEMG-) based gesture recognition is widely used in rehabilitation training, artificial prosthesis, and human-computer interaction. The purpose of this study is to simplify the sEMG devices by reducing channels while achieving comparably high gesture recognition accuracy. We propose a compound channel selection scheme by combining the variable selection algorithms based on multitask sparse representation (MTSR) and minimum Redundancy Maximum Relevance (mRMR). Specifically, channelwise features are first extracted to compose channel-feature paired variables, for which variable selection procedures by MTSR and mRMR are carried out, respectively. Then, we rank all the channels according to their occurrences in each variable selection procedure and figure out a certain number of informative channels by fusing these rankings of channels. Finally, the gesture classification performance using the selected channels is evaluated by the support vector machine (SVM) classifier. Experiment results validate the effectiveness of this proposed method.

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Section: Related Research and Motivationmentioning

confidence: 99%

Reduce Surface Electromyography Channels for Gesture Recognition by Multitask Sparse Representation and Minimum Redundancy Maximum Relevance

Shang

et al. 2021

Journal of Healthcare Engineering

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The Effects of Channel Number on Classification Performance for sEMG-based Speech Recognition

Wang

Zhu

Cui

et al. 2020

2020 42nd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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A Comprehensive Channel and Feature Selection Method for Myoelectric Pattern Recognition

Zheng

et al. 2021

2020 IEEE International Conference on E-Health Networking, Application &Amp; Services (HEALTHCOM)

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Channel selection in multi-channel surface electromyogram based hand activity classifier

Cited by 5 publications

References 12 publications

Reduce Surface Electromyography Channels for Gesture Recognition by Multitask Sparse Representation and Minimum Redundancy Maximum Relevance

Reduce Surface Electromyography Channels for Gesture Recognition by Multitask Sparse Representation and Minimum Redundancy Maximum Relevance

The Effects of Channel Number on Classification Performance for sEMG-based Speech Recognition

A Comprehensive Channel and Feature Selection Method for Myoelectric Pattern Recognition

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