Show simple record

dc.contributor.authorKashyap, Venkatesh Subramanya
dc.date.accessioned2020-04-17T02:15:03Z
dc.date.available2020-04-17T02:15:03Z
dc.date.issued2020
dc.identifier.urihttps://hdl.handle.net/10652/4914
dc.description.abstractWorld’s population is ageing rapidly. There have been various efforts to improve the quality of life for elderly. Ambient assisted living is one possible solution which enables elderly or disabled people to live a better lifestyle. Currently there are smart home systems that utilize a wide range of sensors to predict our everyday activities. However, research into activity recognition and resident identification using ultrasonic sensors are limited. This work introduces machine learning techniques with ultrasonic sensors to predict the activities of one and two person in the smart home environment. The proposed system is capable of recognising the activities and identifying the residents without the need to manually label the prior activities. Our evaluation demonstartes that the proposed approach can predict resident’s activities with high accuracy. The trained model could be used to predict other resident’s activities and also identify resident’s from each other. This research enables the smart home system to be widely adopted in people’s houses with minimal training and also enable people who need support, to live independently with less interference from caregivers which in turn enables caregivers to manage more people at the same time.en_NZ
dc.language.isoenen_NZ
dc.rightsAll rights reserveden_NZ
dc.subjectNew Zealanden_NZ
dc.subjectsmart homesen_NZ
dc.subjectpeople with supported needsen_NZ
dc.subjectbehaviour trackingen_NZ
dc.subjectultrasonic sensorsen_NZ
dc.subjectmachine learningen_NZ
dc.subjectolder peopleen_NZ
dc.subjectaged careen_NZ
dc.subjectresident identificationen_NZ
dc.titleActivity recognition and resident identification in smart home environmenten_NZ
dc.typeMasters Thesisen_NZ
dc.rights.holderAuthoren_NZ
thesis.degree.nameMaster of Computingen_NZ
thesis.degree.levelMastersen_NZ
thesis.degree.grantorUnitec Institute of Technologyen_NZ
dc.subject.marsden080101 Adaptive Agents and Intelligent Roboticsen_NZ
dc.subject.marsden111702 Aged Health Careen_NZ
dc.identifier.bibliographicCitationKashyap, V. S. (2020). Activity recognition and resident identification in smart home environment. (Unpublished document submitted in partial fulfilment of the requirements for the degree of Master of Computing). Unitec Institute of Technology, Auckland, New Zealand. Retrieved from https://hdl.handle.net/10652/4914en
unitec.pages64en_NZ
dc.contributor.affiliationUnitec Institute of Technologyen_NZ
unitec.publication.placeAuckland, New Zealand
unitec.advisor.principalBarmada, Bashar
unitec.advisor.associatedRamirez-Prado, Guillermo
unitec.advisor.associatedLiesaputra, Veronica
unitec.institution.studyareaComputing


Files in this item

Thumbnail

This item appears in

Show simple record


 Unitec Institute of Technology, Private Bag 92025, Victoria Street West, Auckland 1142