We have recently observed a convergence of technologies to foster the emergence of lifelogging as a mainstream activity. Computer storage has become significantly cheaper, and advancements in sensing technology allows for the efficient sensing of personal activities, locations and the environment. This is best seen in the growing popularity of the quantified self movement, in which life activities are tracked using wearable sensors in the hope of better understanding human performance in a variety of tasks. This review aims to provide a comprehensive summary of lifelogging, to cover its research history, current technologies, and applications. Thus far, most of the lifelogging research has focused predominantly on visual lifelogging, hence we maintain this focus in this review. However, we also reflect on the challenges lifelogging poses for information access and retrieval in general. This review is a suitable reference for those seeking an information retrieval scientist’s perspective on lifelogging and the quantified self.

Over the past three decades the global prevalence of childhood overweight and obesity has increased by 47%. Marketing of energy-dense nutrient-poor foods and beverages contributes to this worldwide increase. Previous research on food marketing to children largely uses self-report, reporting by parents, or third-party observation of children’s environments, with the focus mostly on single settings and/or media. This paper reports on innovative research, Kids’Cam, in which children wore cameras to examine the frequency and nature of everyday exposure to food marketing across multiple media and settings. Kids’Cam was a cross-sectional study of 168 children (mean age 12.6 years, SD = 0.5) in Wellington, New Zealand. Each child wore a wearable camera on four consecutive days, capturing images automatically every seven seconds. Images were manually coded as either recommended (core) or not recommended (non-core) to be marketed to children by setting, marketing medium, and product category. Images in convenience stores and supermarkets were excluded as marketing examples were considered too numerous to count. On average, children were exposed to non-core food marketing 27.3 times a day (95% CI 24.8, 30.1) across all settings. This was more than twice their average exposure to core food marketing (12.3 per day, 95% CI 8.7, 17.4). Most non-core exposures occurred at home (33%), in public spaces (30%) and at school (19%). Food packaging was the predominant marketing medium (74% and 64% for core and non-core foods) followed by signs (21% and 28% for core and non-core). Sugary drinks, fast food, confectionary and snack foods were the most commonly encountered non-core foods marketed. Rates were calculated using Poisson regression. Children in this study were frequently exposed, across multiple settings, to marketing of non-core foods not recommended to be marketed to children. The study provides further evidence of the need for urgent action to reduce children’s exposure to marketing of unhealthy foods, and suggests the settings and media in which to act. Such action is necessary if the Commission on Ending Childhood Obesity’s vision is to be achieved.

Touch Screens for the Older User | InTechOpen, Published on: 2012-03-16. Authors: Niamh Caprani, Noel E. O’Connor and Cathal Gurrin

The Lifelog Search Challenge (LSC) is an international content retrieval competition that evaluates search for personal lifelog data. At the LSC, content-based search is performed over a multi-modal dataset, continuously recorded by a lifelogger over 27 days, consisting of multimedia content, biometric data, human activity data, and information activities data. In this work, we report on the first LSC that took place in Yokohama, Japan in 2018 as a special workshop at ACM International Conference on Multimedia Retrieval 2018 (ICMR 2018). We describe the general idea of this challenge, summarise the participating search systems as well as the evaluation procedure, and analyse the search performance of the teams in various aspects. We try to identify reasons why some systems performed better than others and provide an outlook as well as open issues for upcoming iterations of the challenge.