{rfName}
Ac

Indexed in

License and Use

Icono OpenAccess

Citations

51

Altmetrics

Analysis of institutional authors

Trujillo Guerrero, María FernandaCorresponding AuthorRoman-Niemes S.AuthorJaén-Vargas MAuthorSerrano-Olmedo JjAuthor

Share

October 16, 2023
Publications
>
Article
Sí

Accuracy comparison of CNN, LSTM, and Transformer for activity recognition using IMU and visual markers

Publicated to: IEEE Access. 11 106650-106669 - 2023-01-01 11(), DOI: 10.1109/ACCESS.2023.3318563

Authors:

Trujillo-Guerrero, MF; Román-Niemes, S; Jaén-Vargas, M; Cadiz, A; Fonseca, R; Serrano-Olmedo, JJ
[+]

Affiliations

Digevo - Author
Digevo, Santiago 7560941, Chile - Author
Inst Salud Carlos III, Ctr Invest Biomed Red Bioingn Biomat & Nanomed, Madrid 28029, Spain - Author
Univ Politecn Madrid, Ctr Biomed Technol CTB, Madrid 28223, Spain - Author
Universidad Politécnica de Madrid - Author
Yachay Tech Univ, Sch Math & Computat Sci, Urcuqui 170522, Imbabura, Ecuador - Author
Yachay University for Experimental Technology and Research (Yachay Tech) - Author
See more

Abstract

Human activity recognition (HAR) has applications ranging from security to healthcare. Typically these systems are composed of data acquisition and activity recognition models. In this work, we compared the accuracy of two acquisition systems: Inertial Measurement Units (IMUs) vs Movement Analysis Systems (MAS). We trained models to recognize arm exercises using state-of-the-art deep learning architectures and compared their accuracy. MAS uses a camera array and reflective markers. IMU uses accelerometers, gyroscopes, and magnetometers. Sensors of both systems were attached to different locations of the upper limb. We captured and annotated 3 datasets, each one using both systems simultaneously. For activity recognition, we trained 8 architectures, each one with different operations and layers configurations. The best architectures were a combination of CNN, LSTM, and Transformer achieving test accuracy from 89% to 99% on average. We evaluated how feature selection reduced the sensors required. We found IMU and MAS data were able to distinguish correctly the arm exercises. CNN layers at the beginning produced better accuracy on challenging datasets. IMU had advantages over other aquisition systems for activity recognition. We analyzed the relations between models accuracy, signal waveforms, signals correlation, sampling rate, exercise duration, and window size. Finally, we proposed the use of a single IMU located at the wrist and a variable-size window extraction.
[+]

Keywords

arm exercisesclassificationcnnimulstmmovement analysis systemneurodegenerative diseasestransformervisual markerAccelerometersArm exercisesCamerasCnnConvolutional neural networksFeature extractionGait analysisHuman activity recognitionImuLstmMovement analysis systemSensorsTransformerVisual markerVisualization

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal IEEE Access 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, 2023, it was in position , thus managing to position itself as a Q1 (Primer Cuartil), in the category Engineering (Miscellaneous).

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 2.79. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 13, 2025)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Weighted Average of Normalized Impact by the Scopus agency: 3.97 (source consulted: FECYT Mar 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2026-04-09, the following number of citations:

  • WoS: 14
  • Scopus: 23
  • Google Scholar: 10
[+]

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 2026-04-09:

  • 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: 58 (PlumX).

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

    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/85448/

    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: 232
    • Downloads: 83
    [+]

    Leadership analysis of institutional authors

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

    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 (Trujillo-Guerrero MF) and Last Author (SERRANO OLMEDO, JOSE JAVIER).

    the authors responsible for correspondence tasks have been TRUJILLO GUERRERO, MARÍA FERNANDA and Trujillo-Guerrero MF.

    [+]