Memento: An Emotion-driven Lifelogging System with Wearables

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 emerginglifestyle. We see great potential of lifelogging applications along with rapid recent growth of the wearablesmarket, where more sensors are introduced to wearables, i.e., electroencephalogram (EEG) sensors, that canfurther 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 sensorswith 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 toinfer 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 sensorfusion based approach to estimate signal quality. We present a customized two-phase emotion recognitionarchitecture, considering both the affordability and efficiency of wearable-class devices. We also discuss theoptimization 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 thatMemento is responsive, efficient, and user-friendly on wearables.