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|Title:||Memento : an emotion-driven lifelogging system with wearables||Authors:||Jiang, Shiqi
|Keywords:||Engineering::Computer science and engineering||Issue Date:||2019||Source:||Jiang, 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/10.1145/3281630||Project:||MOE2016-T2-2-023
|Journal:||ACM Transactions on Sensor Networks||Abstract:||Due 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.||URI:||https://hdl.handle.net/10356/150811||ISSN:||1550-4859||DOI:||10.1145/3281630||Rights:||© 2019 Association for Computing Machinery. All rights reserved.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||SCSE Journal Articles|
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