用可穿戴设备和手机应用实时监测老人日常情绪，自动预测喜乐与活跃状态。
Monitoring and Prediction of Mood in Elderly People during Daily Life Activities
- 通过手环采集生理数据，结合手机EMA实现日常情绪追踪
- 仅用智能手环即可准确识别快乐与活跃情绪，效果媲美顶尖方法
- 适合老年心理健康监测、居家养老科技研发者参考
我们提出一种智能可穿戴系统，用于监测并预测老年人在日常生活中的情绪状态。系统由腕带设备采集多种生理活动数据，并配合移动端进行生态瞬时评估（EMA）。利用机器学习训练分类器，仅基于腕带数据即可自动预测不同情绪状态。实验表明该方法在情绪识别准确率方面表现良好，在幸福与活跃状态的检测上达到与当前最优方法相当的水平。
原文摘要 · Abstract (English)
We present an intelligent wearable system to monitor and predict mood states of elderly people during their daily life activities. Our system is composed of a wristband to record different physiological activities together with a mobile app for ecological momentary assessment (EMA). Machine learning is used to train a classifier to automatically predict different mood states based on the smart band only. Our approach shows promising results on mood accuracy and provides results comparable with the state of the art in the specific detection of happiness and activeness.
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