基于可穿戴设备的日常压力状态评估研究

赵湛; 韩璐; 方震; 陈贤祥; 杜利东; 刘正奎

doi:10.11999/JEIT170120

基于可穿戴设备的日常压力状态评估研究

doi: 10.11999/JEIT170120

2.
(中国科学院电子学研究所北京 100190) ②(中国科学院大学北京 100049) ③(中国科学院心理研究所北京 100101)

基金项目:

国家自然科学基金(61302033)，北京市自然科学基金(Z160003)，国家重点研发计划(2016YFC1304302)

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出版历程
- 收稿日期: 2017-02-15
- 修回日期: 2017-04-19
- 刊出日期: 2017-11-19

Research on Daily Stress Detection Based on Wearable Device

2.
(Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China)

Funds:

The National Natural Science Foundation of China (61302033), The Key Project of Beijing Municipal Natural Science Foundation (Z160003), The National Key Research and Development Project (2016YFC1304302, 2016YFC0206502, 2016YFC1303900)

摘要

摘要: 现代生活普遍压力较大，容易引起消极痛苦的应激，导致不良情绪甚至滋生各类慢性病。心理专家需要了解个体的压力状态，从而开展对应性心理疏导和治疗。传统心理学自评法存在一定的主观性；基于生理多导仪的压力状态评估法，受设备体积所限无法用于日常压力状态评估。针对上述问题，该文采用可穿戴式传感设备实时采集个体生理信号，利用心理和生理的伴生关系，对个体的心理压力进行长期实时评估。同时通过蒙特利尔影像应激实验(MIST)诱发出被试平静、轻微及高度压力3种压力状态，此实验范式同时包含认知负荷精神压力因素与社会评价心理压力因素，与日常真实生活更为接近。该文共采集39名健康被试的实验数据，通过对数据的特征值提取等预处理，结合随机森林算法对最优特征子集进行选择，采用支持向量机(SVM)分类算法对3种压力状态进行分类预测。实验结果表明，通过随机森林特征选择优化后的SVM分类，与通用的单一SVM分类算法相比，具有更好的分类识别效果，对3种压力状态的分类准确率可从78%提高至84%。
- 压力识别 /
- 可穿戴设备 /
- 心电 /
- 呼吸 /
- 支持向量机
Abstract: In modern life, high stress causes negative emotions and even leads to various chronic diseases. Psychologists need to understand the stress state of the individual in order to facilitate the corresponding psychological treatment. The traditional method of self-evaluation in psychology contains some subjectivity, while the method based on physiological polygraph can not be used in daily stress assessment because of the volume of equipment. For these reasons, a wearable device is used to collect the physiological signals and an assessment of the individuals stress is made according to the associated relationship between the psychological and physiological. The Montreal Imaging Stress Task (MIST) is used to induce three states of no, moderate and high stress. The MIST includs both mental and psychosocial stress factors, which is more closing to a real-life condition. The experimental data are collected from 39 healthy subjects. Features are extracted from the data and the random forest is used to select the optimal stress-related feature combination, which is used to train and test the Support Vector Machine (SVM) classifier. Finally, the results show that the combination of random forest feature selection and SVM achieves a better performance. The accuracy is improved from 78% to 84% in the three states detection.
- Stress recognition /
- Wearable device /
- Electrocardiogram /
- Respiration /
- Support Vector Machine (SVM)

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