Stress is considered to be a modern day "global epidemic"; so given the widespread nature of this problem, it would be beneficial if solutions that help people to learn how to cope better with stress were scalable beyond what individual or group therapies can provide today. Therefore, in this work, we study the potential of smart-phones as a pervasive medium to provide "crowd therapy". The work melds two novel contributions: first, a microintervention authoring process that focuses on repurposing popular web applications as stress management interventions; and second, a machine-learning based intervention recommender system that learns how to match interventions to individuals and their temporal circumstances over time. After four weeks, participants in our user study reported higher self-awareness of stress, lower depression-related symptoms and having learned new simple ways to deal with stress. Furthermore, participants receiving the machine-learning recommendations without option to select different ones showed a tendency towards using more constructive coping behaviors.

We describe an end-to-end system that capitalizes on geographic location tags for digital photographs. The World Wide Media eXchange (WWMX) database indexes large collections of image media by several pieces of metadata including timestamp, owner, and critically, location stamp. The location where a photo was shot is important because it says much about its semantic content, while being relatively easy to acquire, index, and search. The process of building, browsing, and writing applications for such a database raises issues that have heretofore been unaddressed in either the multimedia or the GIS community. This paper brings all of these issues together, explores different options, and offers novel solutions where necessary. Topics include acquisition of location tags for image media, data structures for location tags on photos, database optimization for location-tagged image media, and an intuitive UI for browsing a massive location-tagged image database. We end by describing an application built on top of the WWMX, a lightweight travelogue-authoring tool that automatically creates appropriate context maps for a slideshow of location-tagged photographs.

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Abstract-Behavior modification in health is difficult, as habitual behaviors are extremely well-learned, by definition. This research is focused on building a persuasive system for behavior modification around emotional eating. In this paper, we make strides towards building a just-in-time support system for emotional eating in three user studies. The first two studies involved participants using a custom mobile phone application for tracking emotions, food, and receiving interventions. We found lots of individual differences in emotional eating behaviors and that most participants wanted personalized interventions, rather than a pre-determined intervention. Finally, we also designed a novel, wearable sensor system for detecting emotions using a machine learning approach. This system consisted of physiological sensors which were placed into women's brassieres. We tested the sensing system and found positive results for emotion detection in this mobile, wearable system.