Outliers in Smartphone Sensor Data Reveal Outliers in Daily Happiness
Outliers in Smartphone Sensor Data Reveal Outliers in Daily Happiness
复制标题
DOI：
10.1145/3448095
复制
发表时间：
2021-03-01
期刊：
影响因子：
--
通讯作者：
Matic, Aleksandar
中科院分区：
文献类型：
--
作者：
Buda, Teodora Sandra;Khwaja, Mohammed;Matic, Aleksandar
使智能手机能够了解我们的情绪健康，为创建个性化应用程序和高度响应的界面提供了可能。然而，这绝不是一项微不足道的任务--报告情绪的主观性会影响地面真相信息的可靠性，而智能手机与专用可穿戴设备不同，传感能力有限。在本文中，我们提出了一种新的方法，通过提取基于离群值的特征和减轻捕获地面实况信息的主观性来推进情绪状态预测。我们在一个独特而具有挑战性的用例中使用了这种方法-幸福检测-与传统建模方法相比，我们证明了AUC的预测性能提高了13%，F分数提高了27%。结果表明，传感器读数的极值（即离群值）反映了报告的幸福水平的极值。此外，我们还证明了这种方法在全新的实验环境中复制预测模型时更加稳健。
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.