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Analysis of institutional authors

Ordieres-Mere, JoaquinCorresponding AuthorGrijalvo, MercedesAuthor

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December 25, 2025
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Article
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Remote Gait Monitoring System to Facilitate Assessment of People With Multiple Sclerosis

Publicated to: IEEE Internet of Things Journal. 12 (24): 54348-54367 - 2025-12-15 12(24), DOI: 10.1109/JIOT.2025.3620236

Authors:

Ordieres-Mere, Joaquin; Grijalvo, Mercedes; Martin-Avila, Guillermo; Aladro, Yolanda
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Affiliations

Hosp Univ Getafe, Getafe 28905, Spain - Author
Hosp Univ Getafe, Multiple Sclerosis Unit, Getafe 28905, Spain - Author
IdiPaz, Res Grp, Madrid 28029, Spain - Author
Univ Europea Madrid, Madrid 28670, Spain - Author
Univ Politecn Madrid, Madrid 28006, Spain - Author
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Abstract

Gait impairment is among the most common and affecting symptoms of multiple sclerosis (MS), occurring in more than 90% of patients as the disease progresses. Conventional clinical tests, such as the timed 25-foot walk (T25FW), are not always able to capture the entire richness of gait impairment, especially in everyday settings. To overcome these shortcomings, this article introduces a new remote gait monitoring system based on wearable smart socks embedded with inertial sensors. The system continuously receives high-frequency motion data and, therefore, enables gait auto-recognition and can improve the classification of MS-associated gait impairment. An end-to-end pipeline for data processing was developed, which involves sensor fusion techniques, semantic gait modeling, and machine learning classification. The segmentation and characterization of gait are performed using the spectral analysis of accelerometer and gyroscope signals, with short-time Fourier transform (STFT)-based feature extraction to identify the periodicity and quality of gait. In addition, a deep learning (DL) approach based on the combination of convolutional neural networks (CNNs) and long short-term memory (LSTM) networks is used to discriminate walking patterns with high precision that help detect abnormalities related to MS. Experimental validation was carried out on a population of people with MS (PwMS) and healthy controls, with our model achieving an average accuracy of 97.10% and an area under the curve (AUC) of 0.99 for severe MS classification. The Internet of Wearable Things (IoWT) paradigm introduced here continuous data acquisition and integration with other wearable sensors and offers a noninvasive and scalable solution for continuous gait monitoring. The results highlight the potential of this approach to improve clinical examination, enable early detection of mobility decline, and support individualized rehabilitation planning. Future studies will explore the incorporation of transformer-based artificial intelligence (AI) models to further improve the classification of MS disability.
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Keywords

Ats statementAttitudeBiomedical monitoringClassification (of information)Clinical testsData acquisitionData handlingDeep learningDiagnosisFatigueFeature extractionFrequency motionsGait analysisHigh frequency hfInertial sensorInertial sensorsInternet of thingsInternet of wearable thingInternet of wearable thingsInternet of wearable things (iowt)Learning systemsLegged locomotionMonitoringMonitoring systemMotorsMsMultiple sclerosisMultiple sclerosis (ms)OntologyParkinsons-diseasePatient monitoringPulse width modulationReliabilityRemote gait monitoringSemantic structureSemantic structuresSemanticsSensor data fusionSensor fusionSensorsSpectrum analysisTimed 25-footWalkWearable computersWearable sensors

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal IEEE Internet of Things Journal due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2025, it was in position 11/258, thus managing to position itself as a Q1 (Primer Cuartil), in the category Computer Science, Information Systems. Notably, the journal is positioned above the 90th percentile.

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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 2026-04-25:

  • 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: 8 (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/93356/

    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: 45
    • Downloads: 40
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    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: First Author (ORDIERES MERE, JOAQUIN BIENVENIDO) .

    the author responsible for correspondence tasks has been ORDIERES MERE, JOAQUIN BIENVENIDO.

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    Awards linked to the item

    The work of Joaqu & imath;n Ordieres-Mere was supported by Spanish "Agencia Estatal de Investigacion (AEI)" funded by Ministerio de Ciencia e Innovacion (MCIN)/AEI/10.13039/501100011033 under Grant PID2022-137748OB-C31 and in part by "European Regional Development Fund (ERDF) A way of making Europe."
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