一种基于神经网络的情感识别方法
Cluster-based Approach to Improve Affect Recognition from Passively Sensed Data
摘要：
负面情绪是成年人心理健康的代表。通过能够不引人注目地预测参与者的负面情绪状态，研究人员和临床医生将能够更好地通过移动应用程序提供有针对性的、及时的心理健康干预。这项工作试图通过基于组的建模从移动性，通信和活动模式捕获的用户行为模式，个性化的消极影响状态的被动识别。结果表明，在基于两周用户日常生活的数据集中，组模型的性能优于广义模型。
英文摘要：
Negative affect is a proxy for mental health in adults. By being able to predict participants' negative affect states unobtrusively, researchers and clinicians will be better positioned to deliver targeted, just-in-time mental health interventions via mobile applications. This work attempts to personalize the passive recognition of negative affect states via group-based modeling of user behavior patterns captured from mobility, communication, and activity patterns. Results show that group models outperform generalized models in a dataset based on two weeks of users' daily lives.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
