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dc.contributor.authorSikandar, Tasriva
dc.contributor.authorRabbi, Mohammad F
dc.contributor.authorGhazali, Kamarul H
dc.contributor.authorAltwijri, Omar
dc.contributor.authorAlqahtani, Mahdi
dc.contributor.authorAlmijalli, Mohammed
dc.contributor.authorAltayyar, Saleh
dc.contributor.authorAhamed, Nizam U
dc.date.accessioned2021-04-27T03:20:45Z
dc.date.available2021-04-27T03:20:45Z
dc.date.issued2021
dc.identifier.issn1424-8220
dc.identifier.doi10.3390/s21082836
dc.identifier.urihttp://hdl.handle.net/10072/403989
dc.description.abstractHuman body measurement data related to walking can characterize functional movement and thereby become an important tool for health assessment. Single-camera-captured two-dimensional (2D) image sequences of marker-less walking individuals might be a simple approach for estimating human body measurement data which could be used in walking speed-related health assessment. Conventional body measurement data of 2D images are dependent on body-worn garments (used as segmental markers) and are susceptible to changes in the distance between the participant and camera in indoor and outdoor settings. In this study, we propose five ratio-based body measurement data that can be extracted from 2D images and can be used to classify three walking speeds (i.e., slow, normal, and fast) using a deep learning-based bidirectional long short-term memory classification model. The results showed that average classification accuracies of 88.08% and 79.18% could be achieved in indoor and outdoor environments, respectively. Additionally, the proposed ratio-based body measurement data are independent of body-worn garments and not susceptible to changes in the distance between the walking individual and camera. As a simple but efficient technique, the proposed walking speed classification has great potential to be employed in clinics and aged care homes.
dc.description.peerreviewedYes
dc.languageen
dc.publisherMDPI AG
dc.relation.ispartofpagefrom2836
dc.relation.ispartofpageto2836
dc.relation.ispartofissue8
dc.relation.ispartofjournalSensors
dc.relation.ispartofvolume21
dc.subject.fieldofresearchBiomechanics
dc.subject.fieldofresearchElectrical engineering
dc.subject.fieldofresearchExercise physiology
dc.subject.fieldofresearchSports science and exercise not elsewhere classified
dc.subject.fieldofresearchAnalytical chemistry
dc.subject.fieldofresearchEcology
dc.subject.fieldofresearchDistributed computing and systems software
dc.subject.fieldofresearchcode420701
dc.subject.fieldofresearchcode4008
dc.subject.fieldofresearchcode420702
dc.subject.fieldofresearchcode420799
dc.subject.fieldofresearchcode3401
dc.subject.fieldofresearchcode3103
dc.subject.fieldofresearchcode4606
dc.titleUsing a Deep Learning Method and Data from Two-Dimensional (2D) Marker-Less Video-Based Images for Walking Speed Classification
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationSikandar, T; Rabbi, MF; Ghazali, KH; Altwijri, O; Alqahtani, M; Almijalli, M; Altayyar, S; Ahamed, NU, Using a Deep Learning Method and Data from Two-Dimensional (2D) Marker-Less Video-Based Images for Walking Speed Classification, Sensors, 21 (8), pp. 2836-2836
dcterms.licensehttps://creativecommons.org/licenses/by/4.0/
dc.date.updated2021-04-26T00:56:37Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorRabbi, Fazle


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