Outliers in Smartphone Sensor Data Reveal Outliers in Daily Happiness
| Autor: | Aleksandar Matic, Teodora Sandra Buda, Mohammed Khwaja |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Subjectivity
020205 medical informatics Computer Networks and Communications Computer science media_common.quotation_subject Wearable computer 0102 computer and information sciences 02 engineering and technology Machine learning computer.software_genre 01 natural sciences Task (project management) 0202 electrical engineering electronic engineering information engineering Extreme value theory Reliability (statistics) media_common business.industry Human-Computer Interaction 010201 computation theory & mathematics Hardware and Architecture Outlier Happiness State prediction Artificial intelligence business computer |
| Zdroj: | Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 5:1-19 |
| ISSN: | 2474-9567 |
| DOI: | 10.1145/3448095 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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