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This work was supported by the projects "SensorSportLab III" and "SensorSportLab IV", (Redes de Investigacion en Ciencias del Deporte 2024 and 2025) by Consejo Superior de Deportes (Ministerio de Cultura y Deporte); P. Escobedo thanks the project IJC2020-043307-I funded by MCIN/AEI/10.13039/501100011033 and by 'European Union NextGenerationEU/PRTR'.

Analysis of institutional authors

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Article

A Comparative Study of Plantar Pressure and Inertial Sensors for Cross-Country Ski Classification Using Deep Learning

Publicated to:SENSORS. 25 (5): 1500- - 2025-03-01 25(5), DOI: 10.3390/s25051500

Authors: Polo-Rodriguez, Aurora; Escobedo, Pablo; Martinez-Marti, Fernando; Marcen-Cinca, Noel; Carvajal, Miguel A; Medina-Quero, Javier; Martinez-Garcia, Maria Sofia

Affiliations

Univ Autonoma Madrid, HCTLab Res Grp, Madrid 28049, Spain - Author
Univ Granada, Res Ctr Informat & Commun Technol CIT UGR, Dept Comp Engn Automat & Robot, Granada 18014, Spain - Author
Univ Granada, Sport & Hlth Univ Res Inst iMUDS, Sch Technol & Telecommun Engn ETSIIT, Dept Elect & Comp Technol,ECsens, Granada 18014, Spain - Author
Univ San Jorge, Dept Hlth Sci, Zaragoza 50003, Spain - Author

Abstract

This work presents a comparative study of low cost and low invasiveness sensors (plantar pressure and inertial measurement units) for classifying cross-country skiing techniques. A dataset was created for symmetrical comparative analysis, with data collected from skiers using instrumented insoles that measured plantar pressure, foot angles, and acceleration. A deep learning model based on CNN and LSTM was trained on various sensor combinations, ranging from two specific pressure sensors to a full multisensory array per foot incorporating 4 pressure sensors and an inertial measurement unit with accelerometer, magnetometer, and gyroscope. Results demonstrate an encouraging performance with plantar pressure sensors and classification accuracy closer to inertial sensing. The proposed approach achieves a global average accuracy of 94% to 99% with a minimal sensor setup, highlighting its potential for low-cost and precise technique classification in cross-country skiing and future applications in sports performance analysis.

Keywords

Quality index

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 2025-06-26:

  • 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: 2.
  • 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: 2 (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: 2.7.
  • The number of mentions on the social network X (formerly Twitter): 3 (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.

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: Last Author (Martinez-Garcia, Maria Sofia).

the author responsible for correspondence tasks has been Martinez-Garcia, Maria Sofia.