Designing Effective Movement Digital Biomarkers for Unobtrusive Emotional State Mobile Monitoring
Designing Effective Movement Digital Biomarkers for Unobtrusive Emotional State Mobile Monitoring
复制标题
设计有效的运动数字生物标记，用于不引人注目的情绪状态移动监测
DOI：
10.1145/3089341.3089342
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Mehrotra A
中科院分区：
文献类型：
--
作者：
Mehrotra A
移动传感技术和机器学习技术已被成功地用于建立有效的心理健康监测和干预系统。最近提出了各种方法来有效地利用上下文信息，例如移动性、通信和移动使用模式来量化用户的情绪状态和幸福感。特别是，已经表明，通过智能手机收集的位置信息可以成功地用于监测和预测抑郁程度，如通过PHQ-8等标准分数进行测量。在本文中，我们研究了基于用户移动模式的细粒度特征的新型数字生物标记器的设计，并考虑了用户移动的时间维度(例如，他们访问的地点的顺序)。我们表明，所提出的生物标记物与情绪状态具有统计意义上的显著关联。我们还证明，与一周中的所有日子相比，情绪状态与工作日的流动性模式有更强的关系。最后，我们讨论了在数字健康的“情绪感知”系统的实施中使用这些生物标记物的挑战。
Mobile sensing technologies and machine learning techniques have been successfully exploited to build effective systems for mental health monitoring and intervention. Various approaches have recently been proposed to effectively exploit contextual information such as mobility, communication and mobile usage patterns for quantifying users' emotional states and wellbeing. In particular, it has been shown that location information collected by means of smartphones can be successfully used to monitor and predict depression levels, as measured by means of standard scores such as PHQ-8. In this paper, we investigate the design of novel digital biomarkers based on the fine-grained characterization of the mobility patterns of a user, also considering the temporal dimension of their movements (e.g., sequence of places visited by them). We show that the proposed biomarkers have a statistically significant association with emotional states. We also demonstrate that emotional states have a stronger relationship with mobility patterns of weekdays compared to all days of a week. Finally, we discuss the challenges in using these biomarkers in the implementation of "emotion-aware" systems for digital health.
幸福可以教吗？
影响因子：
1.7
作者：
M. Seligman
通讯作者：
M. Seligman
体验激活：精力充沛的唤醒和紧张的唤醒不是效价和激活的混合物。
影响因子：
4.2
作者：
U. Schimmack;R. Reisenzein
通讯作者：
R. Reisenzein