In-house deep environmental sentience for smart homecare solutions toward ageing society.

Easom, Philip; Bouridane, Ahmed; Qiang, Feiyu; Zhang, Li; Downs, Carolyn; Jiang, Richard

Abstract

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.

Citation

EASOM, P., BOURIDANE, A., QIANG, F., DOWNS, C. and JIANG, R. 2020. In-house deep environmental sentience for smart homecare solutions toward ageing society. In Proceedings of 2020 International conference machine learning and cybernetics (ICMLC 2020), 4 December 2020, [virtual conference]. Piscataway: IEEE [online], pages 261-266. Available from: https://doi.org/10.1109/ICMLC51923.2020.9469531

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2020 International conference on machine learning and cybernetics (ICMLC 2020)
• Start Date: Dec 4, 2020
• Acceptance Date: Nov 15, 2020
• Online Publication Date: Dec 2, 2020
• Publication Date: Jul 5, 2021
• Deposit Date: Jul 27, 2021
• Publicly Available Date: Jul 27, 2021
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Pages: 261-266
• Series ISSN: 2160-1348
• ISBN: 9780738124261
• DOI: https://doi.org/10.1109/icmlc51923.2020.9469531
• Keywords: Smart homecare; AIoT; Care surveillance
• Public URL: https://rgu-repository.worktribe.com/output/1385938