Recognition of Finger Motions Based on Surface Electromyographic Signals and Artificial Neural Network

Yu, Xuelian; Qian, Jinwu; Zhang, Zhen; Wu, Yitian; Zhang, Zheng

doi:10.1109/robio49542.2019.8961687

Cited by 1 publication

(2 citation statements)

References 21 publications

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“…Although still little explored by researchers compared to the classification stage, there is a considerable amount of work published in recent years focusing on the feature selection step. They can be grouped into the search for the best channels set (Batayneh et al 2020;Hua et al 2019;Krasoulis et al 2020;Sun et al 2020;Wang et al 2019;Yu et al 2019), features set (Luo et al 2020;Phukan et al 2019;Yang et al 2020;Ye et al 2019;Zhang et al 2019;Zhou et al 2019) and individual pairs formed by channels and features (Castiblanco et al 2020;Jair et al 2020;Jitaree & Phukpattaranont 2019;Tosin et al 2020b;Tosin et al 2020a;Tosin et al 2017;Wu et al 2019). Table 4 summarizes the most recent strategies for feature selection stage and the average accuracy presented refer to the best scenario reported by the authors in terms of feature selection method and classifier.…”

Section: Feature Selectionmentioning

confidence: 99%

See 1 more Smart Citation

sEMG-Based Upper Limb Movement Classifier: Current Scenario and Upcoming Challenges

Tosin

Machado²,

Balbinot³

2022

jair

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Despite achieving accuracies higher than 90% on recognizing upper-limb movements through sEMG (surface Electromyography) signal with the state of art classifiers in the laboratory environment, there are still issues to be addressed for a myo-controlled prosthesis achieve similar performance in real environment conditions. Thereby, the main goal of this review is to expose the latest researches in terms of strategies in each block of the system, giving a global view of the current state of academic research. A systematic review was conducted, and the retrieved papers were organized according to the system step related to the proposed method. Then, for each stage of the upper limb motion recognition system, the works were described and compared in terms of strategy, methodology and issue addressed. An additional section was destined for the description of works related to signal contamination that is often neglected in reviews focused on sEMG based motion classifiers. Therefore, this section is the main contribution of this paper. Deep learning methods are a current trend for classification stage, providing strategies based on time-series and transfer learning to address the issues related to limb position, temporal/inter-subject variation, and electrode displacement. Despite the promising strategies presented for contaminant detection, identification, and removal, there are still some factors to be considered, such as the occurrence of simultaneous contaminants. This review exposes the current scenario of the movement classification system, providing valuable information for new researchers and guiding future works towards myo-controlled devices.

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Section: Feature Selectionmentioning

confidence: 99%

“…In (Krasoulis et al 2020;Wang et al 2019;Yu et al 2019) the wrapper strategy was used to select the ideal number of electrodes. Krasoulis et al (2020) considered the accuracy of a LDAbased classifier to determine the most relevant channels in identifying six movements represented by seven time domain features.…”

Section: Feature Selectionmentioning

confidence: 99%