In this paper, we present first steps towards a mobile and wearable system intended to help people who experience regular and spontaneous panic attacks due to panic disorder. The goal of the system is to predict oncoming panic attacks and to deliver in-the-moment interventions on a smartphone device. Interventions are intended to reduce symptom severity by enabling a user to respond to approaching panic episodes. An initial feasibility study is described where a small real-world data set was collected. Personalized prediction models were trained which take, as input, physiological data and output a binary classification of either pre-panic or non-panic. We demonstrate proof-of-concept of episode prediction on this small dataset.

Panic disorder is a serious condition that affects approximately six million adults in the United States per year. Reducing the severity of panic attack symptoms would allow a better quality of life for panic attack sufferers. This paper presents steps towards a mobile and wearable system that aims to help reduce the severity of symptoms experienced by individuals with this condition. The system provides a way to continuously monitor the physiological data of an individual via a wearable device. Users are able to report when panic attacks take place, along with a rating of the severity of symptoms experienced. Reported episodes provide ground truth data to build panic prediction models. The eventual goal of the system is to make predictions about approaching panic attacks and to deliver interventions that help the individual to cope with the approaching episode. We describe a mobile-based intervention that has been developed, which instructs the individual to perform breathing and relaxation exercises. Presently, the system has been utilized in a small pilot study where 10 individuals who suffer from panic disorder reported 29 panic attacks while collecting physiological data, along with the severity of symptoms. We found that out of 15 symptoms the ones with high severity reported were anxiety, worry and shortness of breath. Furthermore, physiological differences were observed between panic and non-panic intervals.

Searching a large document collection to learn about a broad subject involves the iterative process of figuring out what to ask, filtering the results, identifying useful documents, and deciding when one has covered enough material to stop searching. We are calling this activity "discoverage," discovery of relevant material and tracking coverage of that material. We built a visual analytic tool called Footprints that uses multiple coordinated visualizations to help users navigate through the discoverage process. To support discovery, Footprints displays topics extracted from documents that provide an overview of the search space and are used to construct searches visuospatially. Footprints allows users to triage their search results by assigning a status to each document (To Read, Read, Useful), and those status markings are shown on interactive histograms depicting the user's coverage through the documents across dates, sources, and topics. Coverage histograms help users notice biases in their search and fill any gaps in their analytic process. To create Footprints, we used a highly iterative, user-centered approach in which we conducted many evaluations during both the design and implementation stages and continually modified the design in response to feedback.

As sharing personal media online becomes easier and widely spread, new privacy concerns emerge - especially when the persistent nature of the media and associated context reveals details about the physical and social context in which the media items were created. In a first-of-its-kind study, we use context-aware camerephone devices to examine privacy decisions in mobile and online photo sharing. Through data analysis on a corpus of privacy decisions and associated context data from a real-world system, we identify relationships between location of photo capture and photo privacy settings. Our data analysis leads to further questions which we investigate through a set of interviews with 15 users. The interviews reveal common themes in privacy considerations: security, social disclosure, identity and convenience. Finally, we highlight several implications and opportunities for design of media sharing applications, including using past privacy patterns to prevent oversights and errors.

What happens when you can access all the world's media, but the access is constrained by screen size, bandwidth, attention, and battery life? We present a novel mobile context-aware software prototype that enables access to images on the go. Our prototype utilizes the channel metaphor to give users contextual access to media of interest according to key dimensions: spatial, social, and topical.

The advent of media-sharing sites like Flickr and YouTube has drastically increased the volume of community-contributed multimedia resources available on the web. These collections have a previously unimagined depth and breadth, and have generated new opportunities - and new challenges - to multimedia research. How do we analyze, understand and extract patterns from these new collections? How can we use these unstructured, unrestricted community contributions of media (and annotation) to generate "knowledge".

The availability of map interfaces and location-aware devices makes a growing amount of unstructured, geo-referenced information available on the Web. In particular, over twelve million geo-referenced photos are now available on Flickr, a popular photo-sharing website. We show a method to analyze the Flickr data and generate aggregate knowledge in the form of "representative tags" for arbitrary areas in the world. We display these tags on a map interface in an interactive web application along with images associated with each tag. We then use the implicit feedback of the aggregate user interactions with the tags and images to learn which images best describe the area shown on the map.

Though cameraphones are rapidly becoming the dominant platform for consumer digital photography, users still face difficulties in transferring, managing, and sharing photos captured with cameraphones. The Mobile Media Metadata 2 (MMM2) system removes the difficulty in transferring photos from the device by providing an automatic upload capability and uses metadata about the context in which a photo was captured to simplify photo management and streamline the sharing process. In our MMM2 system, we have leveraged collaborative filtering techniques to infer the likely sharing recipients for photos based on contextual metadata, which allows the system to accurately guess likely share recipients for a photo and present them to the photographer at the time of capture.

The number of people using cameraphones is growing by tens of millions every month. Yet the majority of cameraphone users have difficulty transferring photos off their phone and sharing them with others. PhotoRouter is a software application for cameraphones that makes the photo sharing process destination-centric by allowing users to focus on who the photo should go to, not how it needs to get there. Attempting to produce an application which meets user needs better than current, technology-centric cameraphone photo sharing applications, we designed PhotoRouter. In this paper we describe PhotoRouter's user interface innovations that we will show in our technical demonstration.