SensibleSleep: A Bayesian Model for Learning Sleep Patterns from Smartphone Events

We propose a Bayesian model for extracting sleep patterns from smartphone events. Our method is able to identify individuals' daily sleep periods and their evolution over time, and provides an estimation of the probability of sleep and wake transitions. The model is fitted to more than 400 participants from two different datasets, and we verify the results against ground truth from dedicated armband sleep trackers. We show that the model is able to produce reliable sleep estimates with an accuracy of 0.89, both at the individual and at the collective level. Moreover the Bayesian model is able to quantify uncertainty and encode prior knowledge about sleep patterns. Compared with existing smartphone-based systems, our method requires only screen on/off events, and is therefore much less intrusive in terms of privacy and more battery-efficient

Active Self-Tracking of Subjective Experience with a One-Button Wearable: A Case Study in Military PTSD

We describe a case study with the participation of a Danish veteran suffering from post-traumatic stress disorder (PTSD). As part of psychotherapeutic treatment the participant and therapist have used our novel technique for instrumenting self-tracking of select aspects of subjective experience using a one-button wearable device. The instrumentation system is described along with the specific self-track- ing protocol which defined the participant's self-tracking of a single symptom, namely the occurrences of a bodily experienced precursor to hyperarousal. Results from the case study demonstrate how self-tracking data on a single symptom collected by a patient can provide valuable input to the therapeutic process. Specifically, it facilitated identification of crucial details otherwise unavailable from the clinical assessment and even became decisive in disentangling different symptoms and their causes.Comment: 5 pages, 4 figures, 2nd Symposium Computing and Mental Health at ACM CHI Conference on Human Factors in Computing Systems 201

New Frontiers of Quantified Self: Finding New Ways for Engaging Users in Collecting and Using Personal Data

In spite of the fast growth in the market of devices and applications that allow people to collect personal information, Quantified Self (QS) tools still present a variety of issues when they are used in everyday lives of common people. In this workshop we aim at exploring new ways for designing QS systems, by gathering different researchers in a unique place for imagining how the tracking, management, interpretation and visualization of personal data could be addressed in the future

Crowds, Bluetooth and Rock’n’Roll: Understanding Music Festival Participant Behavior

In this paper we present a study of sensing and analyzing an offline social network of participants at a large-scale music festival (8 days, 130,000+ participants). We place 33 fixed-location Bluetooth scanners in strategic spots around the festival area to discover Bluetooth-enabled mobile phones carried by the participants, and thus collect spatio-temporal traces of their mobility and interactions. We subsequently analyze the data on two levels. On the micro level, we run a community detection algorithm to reveal a variety of groups the festival participants form. On the macro level, we employ an Infinite Relational Model (IRM) in order to recover the structure of the social network related to participants' music preferences. The obtained structure in the form of clusters of concerts and participants is then interpreted using meta-information about music genres, band origins, stages, and dates of performances. We show that most of the concerts clusters can be described by one or more of the meta-features, effectively revealing preferences of participants (e.g. a cluster of US bands) and discuss the significance of the findings and the potential and limitations of the used method. Finally, we discuss the possibility of employing the described method and techniques for creating user-oriented applications and extending the sensing capabilities during large-scale events by introducing user involvement.Comment: Presented at Sunbelt 2013 in Hamburg on May, 201