Andrea Cuttone

Measuring Large-Scale Social Networks with High Resolution

This paper describes the deployment of a large-scale study designed to measure human interactions across a variety of communication channels, with high temporal resolution and spanning multiple years—the Copenhagen Networks Study. Specifically, we collect data on face-to-face interactions, telecommunication, social networks, location, and background information (personality, demographics, health, politics) for a densely connected population of 1 000 individuals, using state-of-the-art smartphones as social sensors. Here we provide an overview of the related work and describe the motivation and research agenda driving the study. Additionally, the paper details the data-types measured, and the technical infrastructure in terms of both backend and phone software, as well as an outline of the deployment procedures. We document the participant privacy procedures and their underlying principles. The paper is concluded with early results from data analysis, illustrating the importance of multi-channel high-resolution approach to data collection.

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.

A mobile personal informatics system with interactive visualizations of mobility and social interactions

We describe a personal informatics system for Android smartphones that provides personal data on mobility and social interactions through interactive visualization interfaces. The mobile app has been made available to N=136 first year university students as part of a study of social network interactions in a university campus setting. The design of the interactive visualization interfaces enabling the participants to gain insights into own behaviors is described. We report initial findings based on device logging of participant interactions with the interactive visualization app on the smartphone and from a survey on usage with response from 45 (33%) of the participants indicating that the system allowed new insights into behavioral patterns.

Four Data Visualization Heuristics to Facilitate Reflection in Personal Informatics

In this paper we discuss how to facilitate the process of reflection in Personal Informatics and Quantified Self systems through interactive data visualizations. Four heuristics for the design and evaluation of such systems have been identified through analysis of self-tracking devices and apps. Dashboard interface paradigms in specific self-tracking devices (Fitbit and Basis) are discussed as representative examples of state of the art in feedback and reflection support. By relating to existing work in other domains, such as event related representation of time series multivariate data in financial analytics, it is discussed how the heuristics could guide designs that would further facilitate reflection in self-tracking personal informatics systems.

The long tail issue in large scale deployment of personal informatics

We describe the challenges and the open questions arising during the design and deployment of SensibleJournal, a mobile personal informatics system with interactive visualizations of mobility and social interactions based on data acquired from embedded smartphone sensors. The SensibleJournal system was evaluated in a large scale (N=136) mobile sensing field study. We report issues in deployment, limitations in user engagement and uptake, and the challenges in measuring the effect of the system.