Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/150811
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dc.contributor.authorJiang, Shiqien_US
dc.contributor.authorLi, Zhenjiangen_US
dc.contributor.authorZhou, Pengfeien_US
dc.contributor.authorLi, Moen_US
dc.date.accessioned2021-06-14T02:34:40Z-
dc.date.available2021-06-14T02:34:40Z-
dc.date.issued2019-
dc.identifier.citationJiang, S., Li, Z., Zhou, P. & Li, M. (2019). Memento : an emotion-driven lifelogging system with wearables. ACM Transactions On Sensor Networks, 15(1), 8-. https://dx.doi.org/10.1145/3281630en_US
dc.identifier.issn1550-4859en_US
dc.identifier.urihttps://hdl.handle.net/10356/150811-
dc.description.abstractDue to the increasing popularity of mobile devices, the usage of lifelogging has dramatically expanded. People collect their daily memorial moments and share with friends on the social network, which is an emerging lifestyle. We see great potential of lifelogging applications along with rapid recent growth of the wearables market, where more sensors are introduced to wearables, i.e., electroencephalogram (EEG) sensors, that can further sense the user’s mental activities, e.g., emotions. In this article, we present the design and implementation of Memento, an emotion-driven lifelogging system on wearables. Memento integrates EEG sensors with smart glasses. Since memorable moments usually coincides with the user’s emotional changes, Memento leverages the knowledge from the brain-computer-interface domain to analyze the EEG signals to infer emotions and automatically launch lifelogging based on that. Towards building Memento on Commercial off-the-shelf wearable devices, we study EEG signals in mobility cases and propose a multiple sensor fusion based approach to estimate signal quality. We present a customized two-phase emotion recognition architecture, considering both the affordability and efficiency of wearable-class devices. We also discuss the optimization framework to automatically choose and configure the suitable lifelogging method (video, audio, or image) by analyzing the environment and system context. Finally, our experimental evaluation shows that Memento is responsive, efficient, and user-friendly on wearables.en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.description.sponsorshipNanyang Technological Universityen_US
dc.language.isoenen_US
dc.relationMOE2016-T2-2-023en_US
dc.relation2017-T1-002-047en_US
dc.relationM4081879en_US
dc.relation.ispartofACM Transactions on Sensor Networksen_US
dc.rights© 2019 Association for Computing Machinery. All rights reserved.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleMemento : an emotion-driven lifelogging system with wearablesen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.identifier.doi10.1145/3281630-
dc.identifier.scopus2-s2.0-85060872810-
dc.identifier.issue1en_US
dc.identifier.volume15en_US
dc.identifier.spage8en_US
dc.subject.keywordsElectroencephalogramen_US
dc.subject.keywordsEmotion Recognitionen_US
dc.description.acknowledgementThis work is support by Singapore MOE Tier 2 grant MOE2016-T2-2-023, Tier 1 grant 2017-T1-002-047, NTU CoE grant M4081879. This work is also supported by an ECS grant from Research Grants Council of Hong Kong (Project No. CityU 21203516), and a GRF grant from Research Grants Council of Hong Kong (Project No. CityU 11217817).en_US
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