Lifelog and Information Retrieval from Daily Digital Data

Dear Colleagues,

The increasing number of mobile and wearable devices is dramatically changing the way we collect data about a person’s life. This data comes from everywhere—sensors used to gather biomedical information, digital pictures and videos, posts to social media sites, purchase transaction records, and cell phone GPS signals, to name a few. A simple smartphone can be a digital camera, location sensor, and a computer, all in one. Additionally, wearable sensors are becoming more ubiquitous, and can deliver a great wealth of physiological data. Using these rich data, the entire lifetime of an individual can be reconstructed.

Lifelogging is defined as a form of pervasive computing consisting of a unified digital record of the totality of an individual’s experiences, captured multi-modally through digital sensors and stored permanently as a personal multimedia archive. Personal lifelogs have great potential in numerous applications, including information retrieval, memory and moments retrieval, daily living understanding, diet monitoring, disease diagnosis, and other emerging application areas.

Lifelogging has significant challenges related to managing large amounts of data collected daily from heterogeneous sources, as well as to the ethical issues raised by accessing and processing such personal data. Applications of lifelogging ideally require combining advances in several research areas, such as image and information retrieval, knowledge extraction, image understanding, and sentiment analyses, just to name a few. Therefore, it comes as no surprise that these complexity and interdisciplinary challenges have raised increasing attention on this subject from the research community. Although in an initial development stage, this research area has the potential to transform our society.

The main aim of this Special Issue is to present novel approaches and results focusing on all of the interdisciplinary research topics involved in lifelog applications—data acquisition, semantic integration, data processing and mining, data categorization and summarization, and information retrieval data privacy and security, just to name a few. Contributions that explore all of these topics are welcome.

Prof. Dr. António J. R. Neves
Guest Editor

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