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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.

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.

This paper presents results of analysis performed on a physiologic time-series dataset that was collected from a wearable ECG monitoring system worn by individuals who suffer from panic disorder. Models are constructed and evaluated for distinguishing between pathologic and non-pathologic states, including panic (during panic attack), pre-panic (preceding panic attack) and non-panic (outside panic attack window). The models presented use data fusion to combine both traditional time and frequency domain heart rate variability analysis together with nonlinear/complexity analysis. The best performing model is shown to be a random forest classifier that achieves an accuracy of 97.2% and 90.7% for recognizing states of panic and pre-panic, respectively. The models presented have application in pervasive and ubiquitous mobile and wearable health management systems.