Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/145139
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dc.contributor.authorMuhammad Amirun Fariandie Jumrien_US
dc.date.accessioned2020-12-14T02:29:59Z-
dc.date.available2020-12-14T02:29:59Z-
dc.date.issued2020-
dc.identifier.urihttps://hdl.handle.net/10356/145139-
dc.description.abstractSingapore is currently facing an ageing population whereby the old-age support ratio (OASR) in Singapore had decreased from 7.4 in 2010 to 4.3 in 2020. In addition, elderly aged 65 years and above have higher risks of being diagnosed with eye illness and/or experience deteriorating eyesight over time. Hence, the aim of this project is to develop a web server based system for elderly assisted living which would also involve a python implementation of machine learning technology to assist the elderly to switch on and off the lights automatically by anticipating the needs of the elderly. This project was carried out in two phases - Phase I and Phase 2. A Decawave software was used in Phase I and the implementation of machine learning was carried out in Phase II. This project revealed that the elderly assisted living system could be used to predict the lighting in an area (i.e. living room, dining room, kitchen etc.) in a household. However, this system could not be easily implemented. This is because the positioning of the anchor in the floor plan has to adjusted manually hence it is not user-friendly.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.relationA3310-192en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleElderly assisted living systemen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorLaw Choi Looken_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineering (Electrical and Electronic Engineering)en_US
dc.contributor.supervisoremailECLLAW@ntu.edu.sgen_US
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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