HADD: High-Accuracy Detection of Depressed Mood
HADD: High-Accuracy Detection of Depressed Mood
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DOI：
10.3390/technologies10060123
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发表时间：
2022-11
期刊：
影响因子：
3.6
通讯作者：
Yu Liu;Kyoung-Don Kang;Michael Doe
中科院分区：
文献类型：
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作者：
Yu Liu;Kyoung-Don Kang;Michael Doe
抑郁症是一种严重的情绪障碍，但未被充分认识和治疗。移动/可穿戴技术和机器学习的最新进展提供了在参与者同意的情况下检测他们日常生活中抑郁情绪的机会。为了支持高精度、普遍的抑郁情绪检测，我们提出了 HADD，它提供了新的功能。首先，HADD支持多模态数据分析，不仅分析客观传感器数据，还分析使用移动设备收集的主观EMA（生态瞬时评估）数据，以提高普遍存在的抑郁情绪检测的准确性。此外，HADD 通过有效的特征选择、数据增强和两阶段异常值检测，提高了最先进的抑郁情绪检测 ML 算法的准确性。在我们的评估中，HADD 显着提高了一套用于抑郁情绪检测的综合 ML 模型的准确性。
Depression is a serious mood disorder that is under-recognized and under-treated. Recent advances in mobile/wearable technology and ML (machine learning) have provided opportunities to detect the depressed moods of participants in their daily lives with their consent. To support high-accuracy, ubiquitous detection of depressed mood, we propose HADD, which provides new capabilities. First, HADD supports multimodal data analysis in order to enhance the accuracy of ubiquitous depressed mood detection by analyzing not only objective sensor data, but also subjective EMA (ecological momentary assessment) data collected by using mobile devices. In addition, HADD improves upon the accuracy of state-of-the-art ML algorithms for depressed mood detection via effective feature selection, data augmentation, and two-stage outlier detection. In our evaluation, HADD significantly enhanced the accuracy of a comprehensive set of ML models for depressed mood detection.