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dc.contributor.authorRahaman, Habibur
dc.contributor.authorDyo, Vladimir
dc.date.accessioned2021-09-13T11:11:13Z
dc.date.available2021-09-13T00:00:00Z
dc.date.available2021-09-13T11:11:13Z
dc.date.issued2021-09-07
dc.identifier.citationRahaman H, Dyo V (2021) 'Tracking human motion direction with commodity wireless networks', IEEE Sensors Journal, 21 (20), pp.23344 -23351.en_US
dc.identifier.issn1530-437X
dc.identifier.doi10.1109/JSEN.2021.3111132
dc.identifier.urihttp://hdl.handle.net/10547/625101
dc.description.abstractDetecting when a person leaves a room, or a house is essential to create a safe living environment for people suffering from dementia or other mental disorders. The approaches based on wearable devices, e.g. GPS bracelets may detect such events require periodic maintenance to recharge or replace batteries, and therefore may not be suitable for certain types of users. On the other hand, camera-based systems require illumination and raise potential privacy concerns. In this paper, we propose a device-free walking direction detection approach based on RF-sensing, which does not require a person to wear any equipment. The proposed approach monitors the signal strength fluctuations caused by the human body on ambient wireless links and analyses its spatial patterns using a convolutional neural network to identify the walking direction. The approach has been evaluated experimentally to achieve up to 98% classification accuracy depending on the environment.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.urlhttps://ieeexplore.ieee.org/document/9530661
dc.rightsGreen - can archive pre-print and post-print or publisher's version/PDF
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectwireless communicationsen_US
dc.subjectactivity recognitionen_US
dc.subjectmachine learningen_US
dc.subjectInternet of Thingsen_US
dc.subjectRF-sensingen_US
dc.subjectSubject Categories::G760 Machine Learningen_US
dc.titleTracking human motion direction with commodity wireless networksen_US
dc.typeArticleen_US
dc.contributor.departmentUniversity of Bedfordshireen_US
dc.identifier.journalIEEE Sensors Journalen_US
dc.date.updated2021-09-13T11:07:18Z
dc.description.notegold OA with creative commons licence. pub date can be updated when known.


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