Enabling smartphones to understand our emotional well-being provides the potential to create personalised applications and highly responsive interfaces. However, this is by no means a trivial task - subjectivity in reporting emotions impacts the reliability of ground-truth information whereas smartphones, unlike specialised wearables, have limited sensing capabilities. In this paper, we propose a new approach that advances emotional state prediction by extracting outlier-based features and by mitigating the subjectivity in capturing ground-truth information. We utilised this approach in a distinctive and challenging use case - happiness detection - and we demonstrated prediction performance improvements of up to 13% in AUC and 27% in F-score compared to the traditional modelling approaches. The results indicate that extreme values (i.e. outliers) of sensor readings mirror extreme values in the reported happiness levels. Furthermore, we showed that this approach is more robust in replicating the prediction model in completely new experimental settings.
Sensor data collected from smartphones provides the possibility to passively infer a user's personality traits. Such models can be used to enable technology personalization, while contributing to our substantive understanding of how human behavior manifests in daily life. A significant challenge in personality modeling involves improving the accuracy of personality inferences, however, research has yet to assess and consider the cultural impact of users' country of residence on model replicability. We collected mobile sensing data and self-reported Big Five traits from 166 participants (54 women and 112 men) recruited in five different countries (UK, Spain, Colombia, Peru, and Chile) for 3 weeks. We developed machine learning based personality models using culturally diverse datasets -representing different countries -and we show that such models can achieve state-of-the-art accuracy when tested in new countries, ranging from 63% (Agreeableness) to 71% (Extraversion) of classification accuracy. Our results indicate that using country-specific datasets can improve the classification accuracy between 3% and 7% for Extraversion, Agreeableness, and Conscientiousness. We show that these findings hold regardless of gender and age balance in the dataset. Interestingly, using gender-or age-balanced datasets as well as gender-separated datasets improve trait prediction by up to 17%. We unpack differences in personality models across the five countries, highlight the most predictive data categories (location, noise, unlocks, accelerometer), and provide takeaways to technologists and social scientists interested in passive personality assessment.
The recent growth of digital interventions for mental well-being prompts a call-to-arms to explore the delivery of personalised recommendations from a user's perspective. In a randomised placebo study with a two-way factorial design, we analysed the difference between an autonomous user experience as opposed to personalised guidance, with respect to both users' preference and their actual usage of a mental well-being app. Furthermore, we explored users' preference in sharing their data for receiving personalised recommendations, by juxtaposing questionnaires and mobile sensor data. Interestingly, self-reported results indicate the preference for personalised guidance, whereas behavioural data suggests that a blend of autonomous choice and recommended activities results in higher engagement. Additionally, although users reported a strong preference of filling out questionnaires instead of sharing their mobile data, the data source did not have any impact on the actual app use. We discuss the implications of our findings and provide takeaways for designers of mental well-being applications. CCS CONCEPTS• Human-centered computing → Human computer interaction (HCI); Empirical studies in ubiquitous and mobile computing.
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