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Gait cycle duration analysis in lower limb amputees using an IoT-based photonic wearable sensor: a preliminary proof-of-concept study

2025-11-23Journal article Open access
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authorProfile.emailbiblioteca@isel.pt
datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
dc.contributor.authorAlves, Bruna
dc.contributor.authorFantoni, Alessandro
dc.contributor.authorMatos, José
dc.contributor.authorCosta, João
dc.contributor.authorVieira, Manuela
dc.date.accessioned2026-01-07T11:24:59Z
dc.date.available2026-01-07T11:24:59Z
dc.date.issued2025-11-23
dc.description.abstractThis study represents a preliminary proof of concept intended to demonstrate the feasibility of using a single-point LiDAR sensor for wearable gait analysis. The study presents a low-cost wearable sensor system that integrates a single-point LiDAR module and IoT connectivity to assess Gait Cycle Duration (GCD) and gait symmetry in real time. The device is positioned on the medial side of the calf to detect the contralateral limb crossing—used as a proxy for mid-stance—enabling the computation of GCD for both limbs and the derivation of the Symmetry Ratio and Symmetry Index. This was conducted under simulated walking at three cadences (slow, normal and fast). GCD estimated by the sensor was compared against the visual annotation with Kinovea®, showing reasonable agreement, with most cycle-wise relative differences below approximately 13% and both methods capturing similar symmetry trends. The wearable system operated reliably across different speeds, with an estimated materials cost of under 100 € and wireless data streaming to a cloud dashboard for real-time visualization. Although the validation is preliminary and limited to a single healthy participant and a video-based reference, the results support the feasibility of a photonic, IoT-based approach for portable and objective gait assessment, motivating future studies with larger and clinical cohorts and gold-standard references to quantify accuracy, repeatability and clinical utility.por
dc.identifier.citationAlves, B., Fantoni, A., Matos, J. P., Costa, J., & Vieira, M. (2025). Gait cycle duration analysis in lower limb amputees using an IoT-based photonic wearable sensor: a preliminary proof-of-concept study. Sensors, 25(23), 7148. https://doi.org/10.3390/s25237148
dc.identifier.doi10.3390/s25237148
dc.identifier.eissn1424-8220
dc.identifier.urihttp://hdl.handle.net/10400.21/22451
dc.language.isoeng
dc.peerreviewedyes
dc.publisherMDPI AG
dc.relation.hasversionhttps://www.mdpi.com/1424-8220/25/23/7148
dc.relation.ispartofSensors
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectGait symmetry
dc.subjectGait cycle duration
dc.subjectWearable sensor
dc.subjectLiDAR
dc.subjectProsthesis
dc.subjectLower limb amputation
dc.subjectIoI
dc.subjectRehabilitation
dc.subjectGait analysis
dc.titleGait cycle duration analysis in lower limb amputees using an IoT-based photonic wearable sensor: a preliminary proof-of-concept studyeng
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage15
oaire.citation.issue23
oaire.citation.startPage1
oaire.citation.titleSensors
oaire.citation.volume25
oaire.versionhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43
person.familyNameFantoni
person.familyNameMatos
person.familyNameCosta
person.familyNameVieira
person.givenNameAlessandro
person.givenNameJosé
person.givenNameJoão
person.givenNameManuela
person.identifier0000000068588745
person.identifier10792
person.identifier.ciencia-id241E-E87C-552F
person.identifier.ciencia-id7C19-72D4-D3DF
person.identifier.ciencia-idEC15-55B2-BCFA
person.identifier.ciencia-id9516-E25E-BB8E
person.identifier.orcid0000-0002-9938-0351
person.identifier.orcid0000-0001-8772-6223
person.identifier.orcid0000-0002-2721-723X
person.identifier.orcid0000-0002-1150-9895
person.identifier.ridK-1105-2016
person.identifier.ridV-7860-2017
person.identifier.scopus-author-id7006535604
person.identifier.scopus-author-id56304302400
person.identifier.scopus-author-id7202140173
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relation.isAuthorOfPublication.latestForDiscovery9f35eb1e-83e4-4342-a86d-265604301499

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