Doctor of Philosophy
School of Electrical, Computer and Telecommunications Engineering
Recent studies conducted by the World Health Organization reveal that approximately 50 million people are affected by dementia. Such individuals require special care that translates to high social costs. In the last decade, we assisted to the introduction of dementia assistive technologies that aimed at improving the quality of life of residents, as well as facilitating the work of caregivers. Merging the significance of both the alleviation in coping with dementia with the perceptible popularity of assistive technology and smart home devices, the main focus of this work is to further improve home organization and management of individuals living with dementia and their caregivers through the use of technology and artificial intelligence. In particular, we aim at developing an effective but non-invasive environment monitoring solution.
This thesis proposes a novel strategy to detect, classify, and estimate the location of household-related acoustic scenes and events, enabling a less intrusive monitoring system for the assistance and supervision of dementia residents. The proposed approach is based on classification of multi-channel acoustical data acquired from omnidirectional microphone arrays (nodes), which consists of four linearly arranged microphones, placed on four corner locations across each room. The development of a customized synthetic database that reflects real-life recordings relevant to dementia healthcare is also explored, in order to improve and assess the overall robustness of the system. A combination of spectro-temporal acoustic features extracted from the raw digitized-acoustic data will be used for detection and classification purposes. Alongside this, spectral-based phase information is utilized in order to estimate the sound node location.
Copiaco, Abigail, Domestic Multi-channel Sound Detection and Classification for the Monitoring of Dementia Residents’ Safety and Well-being using Neural Networks, Doctor of Philosophy thesis, School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, 2021. https://ro.uow.edu.au/theses1/1213
FoR codes (2008)
0801 ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING, 080604 Database Management, 090609 Signal Processing, 111702 Aged Health Care
Unless otherwise indicated, the views expressed in this thesis are those of the author and do not necessarily represent the views of the University of Wollongong.