Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits

Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits Andrey Bogomolov Bruno Lepri Michela Ferron University of Trento, Fondazione Bruno Kessler Fondazione Bruno Kessler SKIL Telecom Italia Lab Via Sommarive, 18 via Sommarive, 18 Via Sommarive, 5 I-38123 Povo - Trento, Italy I-38123 Povo - Trento, Italy I-38123 Povo - Trento, Italy [email protected] [email protected] [email protected] Fabio Pianesi Alex (Sandy) Pentland Fondazione Bruno Kessler MIT Media Lab Via Sommarive, 18 20 Ames Street I-38123 Povo - Trento, Italy Cambridge, MA, USA [email protected] [email protected] ABSTRACT Keywords Research has proven that stress reduces quality of life and stress recognition; mobile sensing; pervasive computing causes many diseases. For this reason, several researchers devised stress detection systems based on physiological parameters. However, these systems require that obtrusive 1. INTRODUCTION sensors are continuously carried by the user. In our pa- Nowadays, the number of mobile phones in use worldwide per, we propose an alternative approach providing evidence is about 5 billion, with millions of new subscribers everyday 1 that daily stress can be reliably recognized based on be- . Mobile phones allow for unobtrusive and cost-efficient ac- havioral metrics, derived from the user's mobile phone ac- cess to huge streams of previously inaccessible data related tivity and from additional indicators, such as the weather to daily social behavior [29]. These devices are able to sense conditions (data pertaining to transitory properties of the a wealth of behavioral data such as (i) location, (ii) other environment) and the personality traits (data concerning devices in physical proximity through Bluetooth scanning, permanent dispositions of individuals). Our multifactorial (iii) communication data, including both metadata (logs of statistical model, which is person-independent, obtains the who, when, and duration) of phone calls and text messages accuracy score of 72.28% for a 2-class daily stress recog- (sms), etc. Correspondingly, the availability is continuously nition problem. The model is efficient to implement for growing of huge streams of personal data related to activ- most of multimedia applications due to highly reduced low- ities, routines and social interactions [11, 29] which repre- dimensional feature space (32d). Moreover, we identify and sent a novel opportunity to address fundamental problems discuss the indicators which have strong predictive power. of our societies in different fields, such as mobility and ur- ban planning [19], finance [46], healthy living and subjective well-being [31, 33]. In this work, we focus on one of the most widespread and Categories and Subject Descriptors debilitating problem of our subjective well-being and our I.5 [Computing Methodologies]: PATTERN RECOG- society: stress. Stress is a well-known condition in modern NITION|Models, Design Methodology, Implementation; J.4 life and research has shown that the amount of cumulative [Computer Applications]: SOCIAL AND BEHAVIORAL stress plays a role in a broad range of physical, psycholog- arXiv:1410.5816v1 [cs.CY] 21 Oct 2014 SCIENCES|Sociology, Psychology ical and behavioural conditions, such as anxiety, low self- esteem, depression, social isolation, cognitive impairments, sleep and immunological disorders, neurodegenerative dis- General Terms eases and other medical conditions [8], while also signif- icantly contributing to healthcare costs. Hence, measur- Algorithms; Experimentation; Measurement; Theory ing stress in daily life situations has become an important challenge [39]. Today, the availability of huge and diverse Permission to make digital or hard copies of all or part of this work for personal or streams of pervasive data produced by and about people classroom use is granted without fee provided that copies are not made or distributed allows for automatic, unobtrusive, and fast recognition of for profit or commercial advantage and that copies bear this notice and the full cita- daily stress levels. An early prediction of stress symptoms tion on the first page. Copyrights for components of this work owned by others than can indeed help to prevent situations that are risky for hu- ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or re- man life [23]. publish, to post on servers or to redistribute to lists, requires prior specific permission Several studies have produced interesting results that sup- and/or a fee. Request permissions from [email protected]. MM’14, November 3–7, 2014, Orlando, Florida, USA. port the feasibility of detecting stress levels through phys- Copyright 2014 ACM 978-1-4503-3063-3/14/11 ...$15.00. 1 http://dx.doi.org/10.1145/2647868.2654933. http://www.ericsson.com/ericsson-mobility-report iological sensors (see [23], [26]). However, the use of phys- obtains an accuracy score of 72.28% for a 2-class daily stress iological sensors is limited by several shortcomings. Stress recognition problem, providing evidence that individual daily detection systems based on physiological measurement such stress can be reliably predicted from the combination of as heart-rate variability or skin conductance are intrusive smartphone usage data, weather conditions and individual and need to be easily wearable to be exploited in natural dispositions (personality traits). Interestingly, if one of these settings; the data they produce can be confounded by daily information sources is dropped, the recognition performances life activities such as speaking or drinking; they exhibit im- decrease drastically. portant between-person differences [39]. In sum, the main contributions of this paper are as follows: Recently, social psychologist Miller wrote \The Smart- phone Psychology Manifesto" in which he argued that the 1. We propose a multi-factorial data-driven approach to smartphones should be seriously considered as new research the prediction of individual daily stress; tools for psychology. In his opinion, these tools could revolu- 2. We validate our approach with a seven-months dataset tionize all fields of psychology and other behavioral sciences collected from 111 subjects; making these disciplines more powerful, sophisticated, and grounded in real-world behavior [36] and [30]. Indeed, sev- 3. We provide a comprehensive analysis of the predic- eral works have started to use smartphone activity data in tive power of the proposed approach and a comparison order to detect and predict personality traits [6, 9, 37, 48], with approaches based only on single families of fea- mood states [31], and daily happiness [38]. Stopczynski et tures (personality, weather conditions, mobile phone al. [49] described the Copenhagen Networks Study, a large- features) or pairwise combinations thereof (personal- scale study designed to measure human interactions span- ity and weather conditions, personality and mobile ning multiple years. phone features, weather conditions and mobile phone Smartphones data can be used to detect stress levels as features). well. Indeed, stress levels are associated with the type of activities people engage in, including those executed at/through 2. RELATED WORK their smartphone (for instance, a high number of phone calls A large body of research on stress detection focused on and/or e-mails from many different people could be associ- physiological measurements to infer stress levels (see [23], ated with higher stress levels). Weather conditions { an [34], [39]). Heart-rate variability, galvanic skin response, environmental transitory property { in turn, have been ar- respiration, muscle activity and temperature are among the gued [24], [41] to be often associated with stress, acting ei- most relevant features. However, despite providing reliable ther directly (as stressors) or indirectly (by affecting individ- insights on stress levels, this approach has major limitations ual sensitivity to stressors). Finally, the impact of all these because it comprises wearable sensors that need to be car- transitory factors { (smartphone) activities and weather con- ried at all times to allow for continuous monitoring. ditions { on stress induction can be expected to be modu- Among the different changes in physiological parameters lated by personal characteristics and differences [50], [52]. that happen during stressful situations, variation in speech For example, a neurotic person could react with higher lev- production has inspired a number of studies using acous- els of stress to a high number of interactions (call, sms or tic sensing on smartphones. Research on stress detection proximity interactions) than an emotionally stable person; based on voice analysis considered different speech char- an extrovert or agreeable person, in turn, might well find acteristics such as pitch, glottal pulse, spectral slope and him/herself at ease with a high number of interactions. phonetic variations. For example, Lu and colleagues [32] In this paper, we approach the automatic recognition of proposed StressSense, an Android application for stress de- daily stress as a 2-class classification problem (non-stressed tection from human voice in real-life conversation, and they vs stressed) based on information concerning different types achieved 81% and 76% accuracy for indoor and outdoor en- of data: a) people activities, as detected through their smart- vironments. phones; b) weather conditions; c) personality traits. The However, these methods depend on sound quality, which is information about

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