Continuous Stress Detection Using Wearable Sensors in Real Life: Algorithmic Programming Contest Case Study
Continuous Stress Detection Using Wearable Sensors in Real Life: Algorithmic Programming Contest Case Study
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DOI：
10.3390/s19081849
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发表时间：
2019-04-02
期刊：
影响因子：
3.9
通讯作者：
Ersoy, Cem
中科院分区：
文献类型：
--
作者：
Can, Yekta Said;Chalabianloo, Niaz;Ersoy, Cem
几十年来，精神压力对人类健康的负面影响已经为人所知。必须在早期阶段检测到高水平的压力，以防止这些负面影响。在可穿戴设备出现后，研究人员开始检测个人在日常生活中的极端压力。可穿戴设备可能会成为我们生活的一部分。最初的实验是在实验室环境中进行的，最近一些工作从实验室环境向现实生活迈进了一步。我们开发了一种自动压力检测系统，使用从不显眼的智能可穿戴设备获得的生理信号，可以在个人的日常生活中携带。该系统具有特定于通道的伪像去除和针对真实生活条件的特征提取方法。我们在真实环境中进一步测试了我们的系统，收集了为期9天的算法编程竞赛21名参赛者的生理数据。这次活动有讲座、比赛和自由时间。通过利用心脏活动、皮肤电导和加速度计信号，我们使用不同的机器学习方法成功地区分了比赛压力、相对较高的认知负荷(讲座)和放松的时间活动。
The negative effects of mental stress on human health has been known for decades. High-level stress must be detected at early stages to prevent these negative effects. After the emergence of wearable devices that could be part of our lives, researchers have started detecting extreme stress of individuals with them during daily routines. Initial experiments were performed in laboratory environments and recently a number of works took a step outside the laboratory environment to the real-life. We developed an automatic stress detection system using physiological signals obtained from unobtrusive smart wearable devices which can be carried during the daily life routines of individuals. This system has modality-specific artifact removal and feature extraction methods for real-life conditions. We further tested our system in a real-life setting with collected physiological data from 21 participants of an algorithmic programming contest for nine days. This event had lectures, contests as well as free time. By using heart activity, skin conductance and accelerometer signals, we successfully discriminated contest stress, relatively higher cognitive load (lecture) and relaxed time activities by using different machine learning methods.