Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - In-House Deep Environmental Sentience for Smart Homecare Solutions Toward Ageing Society.
AU - Easom, Philip
AU - Bouridane, Ahmed
AU - Qiang, Feiyu
AU - Zhang, Li
AU - Downs, Carolyn
AU - Jiang, Richard
N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2020/12/2
Y1 - 2020/12/2
N2 - With an increasing amount of elderly people needing home care around the clock, care workers are not able to keep up with the demand of providing maximum support to those who require it. As medical costs of home care increase the quality is care suffering as a result of staff shortages, a solution is desperately needed to make the valuable care time of these workers more efficient. This paper proposes a system that is able to make use of the deep learning resources currently available to produce a base system that could provide a solution to many of the problems that care homes and staff face today. Transfer learning was conducted on a deep convolutional neural network to recognize common household objects was proposed. This system showed promising results with an accuracy, sensitivity and specificity of 90.6%, 0.90977 and 0.99668 respectively. Real-time applications were also considered, with the system achieving a maximum speed of 19.6 FPS on an MSI GTX 1060 GPU with 4GB of VRAM allocated.
AB - With an increasing amount of elderly people needing home care around the clock, care workers are not able to keep up with the demand of providing maximum support to those who require it. As medical costs of home care increase the quality is care suffering as a result of staff shortages, a solution is desperately needed to make the valuable care time of these workers more efficient. This paper proposes a system that is able to make use of the deep learning resources currently available to produce a base system that could provide a solution to many of the problems that care homes and staff face today. Transfer learning was conducted on a deep convolutional neural network to recognize common household objects was proposed. This system showed promising results with an accuracy, sensitivity and specificity of 90.6%, 0.90977 and 0.99668 respectively. Real-time applications were also considered, with the system achieving a maximum speed of 19.6 FPS on an MSI GTX 1060 GPU with 4GB of VRAM allocated.
U2 - 10.1109/ICMLC51923.2020.9469531
DO - 10.1109/ICMLC51923.2020.9469531
M3 - Conference contribution/Paper
T3 - Proceedings - International Conference on Machine Learning and Cybernetics
SP - 261
EP - 266
BT - Proceedings of 2020 International Conference on Machine Learning and Cybernetics, ICMLC 2020
ER -