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Analysis of institutional authors

Zhang, KexinAuthorBadesa, Francisco JAuthorPérez, Manuel FerreCorresponding Author

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June 24, 2024
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Dual Stream Long Short-Term Memory Feature Fusion Classifier for Surface Electromyography Gesture Recognition

Publicated to:Sensors. 24 (11): 3631- - 2024-06-01 24(11), DOI: 10.3390/s24113631

Authors: Zhang, KX; Badesa, FJ; Liu, YL; Pérez, MF

Affiliations

Univ Macao, State Key Lab Internet Things Smart City, Macau, Peoples R China - Author
Univ Politecn Madrid UPM, Ctr Automat & Robot CAR UPM CSIC, Madrid 28006, Spain - Author

Abstract

Gesture recognition using electromyography (EMG) signals has prevailed recently in the field of human-computer interactions for controlling intelligent prosthetics. Currently, machine learning and deep learning are the two most commonly employed methods for classifying hand gestures. Despite traditional machine learning methods already achieving impressive performance, it is still a huge amount of work to carry out feature extraction manually. The existing deep learning methods utilize complex neural network architectures to achieve higher accuracy, which will suffer from overfitting, insufficient adaptability, and low recognition accuracy. To improve the existing phenomenon, a novel lightweight model named dual stream LSTM feature fusion classifier is proposed based on the concatenation of five time-domain features of EMG signals and raw data, which are both processed with one-dimensional convolutional neural networks and LSTM layers to carry out the classification. The proposed method can effectively capture global features of EMG signals using a simple architecture, which means less computational cost. An experiment is conducted on a public DB1 dataset with 52 gestures, and each of the 27 subjects repeats every gesture 10 times. The accuracy rate achieved by the model is 89.66%, which is comparable to that achieved by more complex deep learning neural networks, and the inference time for each gesture is 87.6 ms, which can also be implied in a real-time control system. The proposed model is validated using a subject-wise experiment on 10 out of the 40 subjects in the DB2 dataset, achieving a mean accuracy of 91.74%. This is illustrated by its ability to fuse time-domain features and raw data to extract more effective information from the sEMG signal and select an appropriate, efficient, lightweight network to enhance the recognition results.

Keywords

AlgorithmsDeep learningDual stream lstmElectromyographyFeature fusioFeature fusionGesture recognitionGesturesHandHumansMachine learningMemory, short-termNeural networks, computerPattern recognition, automatedSignal processing, computer-assisted

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Sensors due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2024 there are still no calculated indicators, but in 2023, it was in position , thus managing to position itself as a Q1 (Primer Cuartil), in the category Instrumentation.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2025-11-10:

  • WoS: 2
  • Scopus: 6

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-11-10:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 17.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 17 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 0.5.
  • The number of mentions on the social network X (formerly Twitter): 1 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/83798/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 166
  • Downloads: 63

Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: China; Macao.

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (ZHANG, KEXIN) and Last Author (FERRE PEREZ, MANUEL).

the author responsible for correspondence tasks has been FERRE PEREZ, MANUEL.