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 lowdimensional feature space (32d). Moreover, we identify and discuss the indicators which have strong predictive power.
The Workshop on Computational Personality Recognition aims to define the state-of-the-art in the field and to provide tools for future standard evaluations in personality recognition tasks. In the WCPR14 we released two different datasets: one of Youtube Vlogs and one of Mobile Phone interactions. We structured the workshop in two tracks: an open shared task, where participants can do any kind of experiment, and a competition. We also distinguished two tasks: A) personality recognition from multimedia data, and B) personality recognition from text only. In this paper we discuss the results of the workshop.
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Abstract. The paper presents an empirical model of acceptance of mobile phones by elderly people. It is based on an extension of the widely used TAMTechnology Acceptance Model and aims specifically at investigating the relationship among intrinsic and extrinsic motivations to use. The data consists of 740 questionnaires from people over 65 years old. The validated model shows that intrinsic motivations play an important role albeit always mediated by utilitarian motives. Similarly, it emerges a strong influence of the reference social group (children and relatives) in increasing the utilitarian values of the use of mobile phones. These findings suggest that mobile phone usage by elderly might not be, after all, too much different, from a motivational point of view, from that of younger or adult people.
In this paper, we discuss a machine learning approach to automatically detect functional roles played by participants in a face to face interaction. We shortly introduce the coding scheme we used to classify the roles of the group members and the corpus we collected to assess the coding scheme reliability as well as to train statistical systems for automatic recognition of roles. We then discuss a machine learning approach based on multi-class SVM to automatically detect such roles by employing simple features of the visual and acoustical scene. The effectiveness of the classification is better than the chosen baselines and although the results are not yet good enough for a real application, they demonstrate the feasibility of the task of detecting group functional roles in face to face interactions.
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