In this paper, we investigate the relationship between automatically extracted behavioral characteristics derived from rich smartphone data and selfreported Big-Five personality traits (Extraversion, Agreeableness, Conscientiousness, Emotional Stability and Openness to Experience). Our data stems from smartphones of 117 Nokia N95 smartphone users, collected over a continuous period of 17 months in Switzerland. From the analysis, we show that several aggregated features obtained from smartphone usage data can be indicators of the Big-Five traits. Next, we describe a machine learning method to detect the personality trait of a user based on smartphone usage. Finally, we study the benefits of using gender-specific models for this task. Apart from a psychological viewpoint, this study facilitates further research on the automated classification and usage of personality traits for personalizing services on smartphones.
In this paper, we investigate the relationship between behavioral characteristics derived from rich smartphone data and self-reported personality traits. Our data stems from smartphones of a set of 83 individuals collected over a continuous period of 8 months. From the analysis, we show that aggregated features obtained from smartphone usage data can be indicators of the Big-Five personality traits. Additionally, we develop an automatic method to infer the personality type of a user based on cellphone usage using supervised learning. We show that our method performs significantly above chance and up to 75.9% accuracy. To our knowledge, this constitutes the first study on the analysis and classification of personality traits using smartphone data.
Stress can have long term adverse effects on individuals' physical and mental well-being. Changes in the speech production process is one of many physiological changes that happen during stress. Microphones, embedded in mobile phones and carried ubiquitously by people, provide the opportunity to continuously and non-invasively monitor stress in real-life situations. We propose StressSense for unobtrusively recognizing stress from human voice using smartphones. We investigate methods for adapting a one-size-fitsall stress model to individual speakers and scenarios. We demonstrate that the StressSense classifier can robustly identify stress across multiple individuals in diverse acoustic environments: using model adaptation StressSense achieves 81% and 76% accuracy for indoor and outdoor environments, respectively. We show that StressSense can be implemented on commodity Android phones and run in real-time. To the best of our knowledge, StressSense represents the first system to consider voice based stress detection and model adaptation in diverse real-life conversational situations using smartphones.
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