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Detection of semantic risk situations in lifelog data for improving life of frail people

Yebda, Thinhinane, Benois-Pineau, Jenny ORCID: 0000-0003-0659-8894, Pech, Marion, Amièva, Hélène and Gurrin, Cathal ORCID: 0000-0003-2903-3968 (2020) Detection of semantic risk situations in lifelog data for improving life of frail people. In: 2020 International Conference on Multimedia Retrieval (ICMR'20), 26-29 June 2020, Dublin, Ireland. ISBN 978-1-4503-7087-5

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Abstract

The automatic recognition of risk situations for frail people is an urgent research topic for the interdisciplinary artificial intelligence and multimedia community. Risky situations can be recognized from lifelog data recorded with wearable devices. In this paper, we present a new approach for the detection of semantic risk situations for frail people in lifelog data. Concept matching between general lifelog and risk taxonomies was realized and tuned AlexNet was deployed for detection of two semantic risks situations such as risk of domestic accident and risk of fraud with promising results.

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:neural network; classification; risk situations detection; CNN networks
Subjects:UNSPECIFIED
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Published in: Proceedings of the 2020 International Conference on Multimedia Retrieval. . Association for Computing Machinery (ACM). ISBN 978-1-4503-7087-5
Publisher:Association for Computing Machinery (ACM)
Official URL:https://doi.org/10.1145/3372278.3391931
Copyright Information:© 2020 The Authors
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:French national ANRT grant:, AAP 2019 "Digital Health Challenge" grant, ALLOCATION: SSESE1902GA and InflexSys project
ID Code:24629
Deposited On:17 Jun 2020 12:06 by Cathal Gurrin . Last Modified 15 Dec 2021 15:38

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