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Indirect recognition of predefined human activities

dc.contributor.authorGorjani, Ojan Majidzadeh
dc.contributor.authorProto, Antonino
dc.contributor.authorVaňuš, Jan
dc.contributor.authorBilík, Petr
dc.date.accessioned2020-10-26T11:52:52Z
dc.date.available2020-10-26T11:52:52Z
dc.date.issued2020
dc.identifier.citationSensors. 2020, vol. 20, issue 17, art. no. 4829.cs
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/10084/142367
dc.description.abstractThe work investigates the application of artificial neural networks and logistic regression for the recognition of activities performed by room occupants. KNX (Konnex) standard-based devices were selected for smart home automation and data collection. The obtained data from these devices (Humidity, CO2, temperature) were used in combination with two wearable gadgets to classify specific activities performed by the room occupant. The obtained classifications can benefit the occupant by monitoring the wellbeing of elderly residents and providing optimal air quality and temperature by utilizing heating, ventilation, and air conditioning control. The obtained results yield accurate classification.cs
dc.language.isoencs
dc.publisherMDPIcs
dc.relation.ispartofseriesSensorscs
dc.relation.urihttp://doi.org/10.3390/s20174829cs
dc.rights© 2020 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.cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectdeep learningcs
dc.subjectlogistic regressioncs
dc.subjectactivity recognitioncs
dc.subjectpredictioncs
dc.subjectclassificationcs
dc.subjectartificial neural networkcs
dc.subjectsmart homescs
dc.subjectintelligent buildingscs
dc.titleIndirect recognition of predefined human activitiescs
dc.typearticlecs
dc.identifier.doi10.3390/s20174829
dc.rights.accessopenAccesscs
dc.type.versionpublishedVersioncs
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume20cs
dc.description.issue17cs
dc.description.firstpageart. no. 4829cs
dc.identifier.wos000569777900001


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© 2020 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.
Kromě případů, kde je uvedeno jinak, licence tohoto záznamu je © 2020 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.