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dc.contributorDepartment of Rehabilitation Sciences-
dc.creatorNaser, A-
dc.creatorLotfi, A-
dc.creatorZhong, J-
dc.rights© The Author(s) 2021en_US
dc.rightsOpen Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit
dc.rightsThe following publication Naser, A., Lotfi, A. & Zhong, J. Towards human distance estimation using a thermal sensor array. Neural Comput & Applic (2021) is available at
dc.subjectAdaptive systemen_US
dc.subjectArtificial neural networken_US
dc.subjectDistance estimationen_US
dc.subjectHuman-centred approachen_US
dc.subjectSemantic segmentationen_US
dc.subjectThermal sensor arrayen_US
dc.titleTowards human distance estimation using a thermal sensor arrayen_US
dc.typeJournal/Magazine Articleen_US
dcterms.abstractHuman distance estimation is essential in many vital applications, specifically, in human localisation-based systems, such as independent living for older adults applications, and making places safe through preventing the transmission of contagious diseases through social distancing alert systems. Previous approaches to estimate the distance between a reference sensing device and human subject relied on visual or high-resolution thermal cameras. However, regular visual cameras have serious concerns about people’s privacy in indoor environments, and high-resolution thermal cameras are costly. This paper proposes a novel approach to estimate the distance for indoor human-centred applications using a low-resolution thermal sensor array. The proposed system presents a discrete and adaptive sensor placement continuous distance estimators using classification techniques and artificial neural network, respectively. It also proposes a real-time distance-based field of view classification through a novel image-based feature. Besides, the paper proposes a transfer application to the proposed continuous distance estimator to measure human height. The proposed approach is evaluated in different indoor environments, sensor placements with different participants. This paper shows a median overall error of ± 0.2 m in continuous-based estimation and 96.8 % achieved-accuracy in discrete distance estimation.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationNeural computing and applications, 2021, Online first,
dcterms.isPartOfNeural computing and applications-
dc.description.validate202110 bcvc-
dc.description.oaVersion of Recorden_US
dc.description.pubStatusEarly releaseen_US
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