Investigating causality in human behavior from smartphone sensor data: a quasi-experimental approach

Smartphones and wearables have become an indispensable part of our daily life. Their improved sensing and computing capabilities bring new opportunities for human behavior monitoring and analysis. Most work so far has been focused on detecting correlation rather than causation among features extracted from smartphone data. However, pure correlation analysis does not offer sufficient understanding of human behavior. Moreover, causation analysis could allow scientists to identify factors that have a causal effect on health and well-being issues, such as obesity, stress, depression and so on and suggest actions to deal with them. Finally, detecting causal relationships in this kind of observational data is challenging since, in general, subjects cannot be randomly exposed to an event. In this article, we discuss the design, implementation and evaluation of a generic quasi-experimental framework for conducting causation studies on human behavior from smartphone data. We demonstrate the effectiveness of our approach by investigating the causal impact of several factors such as exercise, social interactions and work on stress level. Our results indicate that exercising and spending time outside home and working environment have a positive effect on participants stress level while reduced working hours only slightly impact stress

Predicting students' happiness from physiology, phone, mobility, and behavioral data

In order to model students' happiness, we apply machine learning methods to data collected from undergrad students monitored over the course of one month each. The data collected include physiological signals, location, smartphone logs, and survey responses to behavioral questions. Each day, participants reported their wellbeing on measures including stress, health, and happiness. Because of the relationship between happiness and depression, modeling happiness may help us to detect individuals who are at risk of depression and guide interventions to help them. We are also interested in how behavioral factors (such as sleep and social activity) affect happiness positively and negatively. A variety of machine learning and feature selection techniques are compared, including Gaussian Mixture Models and ensemble classification. We achieve 70% classification accuracy of self-reported happiness on held-out test data.MIT Media Lab ConsortiumRobert Wood Johnson Foundation (Wellbeing Initiative)National Institutes of Health (U.S.) (Grant R01GM105018)Samsung (Firm)Natural Sciences and Engineering Research Council of Canad

MyTraces: Investigating Correlation and Causation between Users' Emotional States and Mobile Phone Interaction

Most of the existing work concerning the analysis of emotional states and mobile phone interaction has been based on correlation analysis. In this paper, for the first time, we carry out a causality study to investigate the causal links between users’ emotional states and their interaction with mobile phones, which could provide valuable information to practitioners and researchers. The analysis is based on a dataset collected in-the-wild. We recorded 5,118 mood reports from 28 users over a period of 20 days. Our results show that users’ emotions have a causal impact on different aspects of mobile phone interaction. On the other hand, we can observe a causal impact of the use of specific applications, reflecting the external users’ context, such as socializing and traveling, on happiness and stress level. This study has profound implications for the design of interactive mobile systems since it identifies the dimensions that have causal effects on users’ interaction with mobile phones and vice versa. These findings might lead to the design of more effective computing systems and services that rely on the analysis of the emotional state of users, for example for marketing and digital health applications

Sensing sociability: Individual differences in young adults' conversation, calling, texting, and app use behaviors in daily life.

Sociability as a disposition describes a tendency to affiliate with others (vs. be alone). Yet, we know relatively little about how much social behavior people engage in during a typical day. One challenge to documenting social behavior tendencies is the broad number of channels over which socializing can occur, both in-person and through digital media. To examine individual differences in everyday social behavior patterns, here we used smartphone-based mobile sensing methods (MSMs) in four studies (total N = 926) to collect real-world data about young adults' social behaviors across four communication channels: conversations, phone calls, text messages, and use of messaging and social media applications. To examine individual differences, we first focused on establishing between-person variability in daily social behavior, examining stability of and relationships among daily sensed social behavior tendencies. To explore factors that may explain the observed individual differences in sensed social behavior, we then expanded our focus to include other time estimates (e.g., times of the day, days of the week) and personality traits. In doing so, we present the first large-scale descriptive portrait of behavioral sociability patterns, characterizing the degree to which young adults engaged in social behaviors and mapping these behaviors onto self-reported personality dispositions. Our discussion focuses on how the observed sociability patterns compare to previous research on young adults' social behavior. We conclude by pointing to areas for future research aimed at understanding sociability using mobile sensing and other naturalistic observation methods for the assessment of social behavior. (PsycInfo Database Record (c) 2020 APA, all rights reserved)

Passive Assessment of Longitudinal Behaviors Associated with Mindfulness

The assessment of mindfulness (i.e., a general receptivity and full engagement in the present moment) has historically been conducted via self-report questionnaires and interviews (Brown, Ryan, & Creswell, 2007), and there has been limited research into objective and behavioral–based assessment tools. Phone-sensing is a novel data collection method that has been shown to detect long-term behavioral patterns associated with personality traits (De Montjoye, Quoidbach, Robic, & Pentland, 2013), mental health status (Ben-Zeev et al., 2016), and physical health (Eagle & Sandy Pentland, 2006) by passively collecting data from smartphone sensors and software. The purpose of the current study was to determine if there is an association between patterns of phone sensing data and reported level of mindfulness. Findings suggest that there is significant positive relationship between mindfulness and predictability of the participant’s location each day, predictability of face-to-face interactions each day, number of face-to-face interactions on nights and weekends. Among the phone sensing predictors, predictability of face-to-face interactions measured via detecting Bluetooth signals explained the most unique variance, and the combination of all three associated predictors explained a medium-to-large amount of the variance in mindfulness scores. The current study’s findings support the possible utility phone sensing methods may have in measuring longitudinal behaviors and quantifying long-term fluctuations in behaviors that are traditionally difficult to quantify.M.S., Clinical Psychology -- Drexel University, 201

Semantic architecture for sensors

Based on the report for the unit “Sociology of New Information Technologies” of the Master on Computer Sciences at FCT/University Nova Lisbon in 2015-16. The responsible of this curricular unit is Prof. António MonizTechnological progress in recent years and the increase of Internet of things (IoT) in our daily life brought a huge flood of data that can only be handle, processed and exploited in real-time with the help of Information and Communication Technologies (ICT). The ICT is one main element in order to achieve more efficient and sustainable an environment resource management, while the needs of the citizens are satisfied, creating new applications to improve citizen’s quality life. The creation of new systems that allow the acquisition of context information, automatically and transparently, and give that information to decision support systems are important aspects for information societies. In this paper it will be presented the usability and importance of sensors to get information from our environment in order to know what and when happen changes around us as well as the importance of ontologies in the structure and organization of the systems, to acquire new knowledge