Smartphone apps usage patterns as a predictor of perceived stress levels at workplace

Explosion of number of smartphone apps and their diversity has created a fertile ground to study behaviour of smartphone users. Patterns of app usage, specifically types of apps and their duration are influenced by the state of the user and this information can be correlated with the self-reported state of the users. The work in this paper is along the line of understanding patterns of app usage and investigating relationship of these patterns with the perceived stress level within the workplace context. Our results show that using a subject-centric behaviour model we can predict stress levels based on smartphone app usage. The results we have achieved, of average accuracy of 75% and precision of 85.7%, can be used as an indicator of overall stress levels in work environments and in turn inform stress reduction organisational policies, especially when considering interrelation between stress and productivity of workers

Evidence to support common application switching behaviour on smartphones

We find evidence to support common behaviour in smartphone usage based on analysis of application (app) switching. This is an overlooked aspect of smartphone usage that gives additional insight beyond screen time and the particular apps that are accessed. Using a dataset of usage behaviour from 53 participants over a six-week period, we find strong similarity in the structure of networks built from app switching, despite diversity in the apps used, and the volume of app switching. App switch networks exhibit small-world, broad-scale network features, with a rapid popularity decay, suggesting that preferential attachment may drive next-app decision-making

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

Research has proven that stress reduces quality of life and causes many diseases. For this reason, several researchers devised stress detection systems based on physiological parameters. However, these systems require that obtrusive sensors are continuously carried by the user. In our paper, we propose an alternative approach providing evidence that daily stress can be reliably recognized based on behavioral metrics, derived from the user's mobile phone activity and from additional indicators, such as the weather conditions (data pertaining to transitory properties of the environment) and the personality traits (data concerning permanent dispositions of individuals). Our multifactorial statistical model, which is person-independent, obtains the accuracy score of 72.28% for a 2-class daily stress recognition problem. The model is efficient to implement for most of multimedia applications due to highly reduced low-dimensional feature space (32d). Moreover, we identify and discuss the indicators which have strong predictive power.Comment: ACM Multimedia 2014, November 3-7, 2014, Orlando, Florida, US

Genetic Programming for Smart Phone Personalisation

Personalisation in smart phones requires adaptability to dynamic context based on user mobility, application usage and sensor inputs. Current personalisation approaches, which rely on static logic that is developed a priori, do not provide sufficient adaptability to dynamic and unexpected context. This paper proposes genetic programming (GP), which can evolve program logic in realtime, as an online learning method to deal with the highly dynamic context in smart phone personalisation. We introduce the concept of collaborative smart phone personalisation through the GP Island Model, in order to exploit shared context among co-located phone users and reduce convergence time. We implement these concepts on real smartphones to demonstrate the capability of personalisation through GP and to explore the benefits of the Island Model. Our empirical evaluations on two example applications confirm that the Island Model can reduce convergence time by up to two-thirds over standalone GP personalisation.Comment: 43 pages, 11 figure

Exploring Smartphone Application Usage Logs with Declared Sociological Information

International audienceIn this paper we present an exploratory smartphone usage study with logs collected from users in the wild, combined with the sociodemographic, technological and cultural information provided by them. We observe a high diversity among users' most used applications, but by classifying applications into services we find significant correlations between service usage and socio-demographic profile. We discuss that sociological information has rich potential in characterizing smartphone usage and can be applied to interesting incentive strategies and use cases based on users' sociological context

Mobile News Consumption and Its Relation to Young Adults' Knowledge About and Participation in Referendums

The news media are among the most important sources of information about political events, such as referendums. For young adults, the smartphone has become the main device for accessing news. However, we know little about the factors influencing mobile news consumption and how this consumption is related to political knowledge and political participation. This study investigates the antecedents of young individuals’ smartphone news consumption and how it is correlated with their knowledge about and participation in two referendums in Switzerland. We record the mobile internet usage of 309 young adults and link their digital trace data to survey data. We show that trust in news media and the use of broadcast media are positively correlated with the duration of mobile news consumption. The use of social media leads to more news source diversity. However, we find that the duration of mobile news consumption and news source diversity are not correlated with political knowledge about or participation in the referendum. As interest in politics is also positively correlated with the diversity of news sources used by individual participants, our study supports the idea that attentive audiences use a broader range of news sources to inform themselves about referendums

A Model for Remote Access and Protection of Smartphones using Short Message Service

The smartphone usage among people is increasing rapidly. With the phenomenal growth of smartphone use, smartphone theft is also increasing. This paper proposes a model to secure smartphones from theft as well as provides options to access a smartphone through other smartphone or a normal mobile via Short Message Service. This model provides option to track and secure the mobile by locking it. It also provides facilities to receive the incoming call and sms information to the remotely connected device and enables the remote user to control the mobile through SMS. The proposed model is validated by the prototype implementation in Android platform. Various tests are conducted in the implementation and the results are discussed.Comment: 10 Pages, 11 Figure