The possibility of recognizing diverse aspects of human behavior and environmental context from passively captured data motivates its use for mental health assessment. In this paper, we analyze the contribution of different passively collected sensor data types (WiFi, GPS, Social interaction, Phone Log, Physical Activity, Audio, and Academic features) to predict daily selfreport stress and PHQ-9 depression score. First, we compute 125 mid-level features from the original raw data. These 125 features include groups of features from the different sensor data types. Then, we evaluate the contribution of each feature type by comparing the performance of Neural Network models trained with all features against Neural Network models trained with specific feature groups. Our results show that WiFi features (which encode mobility patterns) and Phone Log features (which encode information correlated with sleep patterns), provide significative information for stress and depression prediction.

翻译：摘要：从被动捕获的数据中识别人类行为和环境背景的多种可能性，推动了其在心理健康评估中的应用。本文分析了不同被动采集传感器数据类型（WiFi、GPS、社交互动、通话日志、身体活动、音频和学术特征）对预测每日自我报告压力和PHQ-9抑郁分数的贡献。首先，我们从原始数据中计算了125个中层特征。这125个特征包括来自不同传感器数据类型的分组特征。然后，我们通过比较使用所有特征训练的神经网络模型与使用特定特征组训练的神经网络模型的性能，评估了每种特征类型的贡献。结果表明，WiFi特征（编码活动模式）和通话日志特征（编码与睡眠模式相关的信息）为压力和抑郁预测提供了显著的信息。