Assessment of Mental, Emotional and Physical Stress through Analysis of Physiological Signals Using Smartphones.
Assessment of Mental, Emotional and Physical Stress through Analysis of Physiological Signals Using Smartphones.
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DOI：
10.3390/s151025607
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发表时间：
2015-10-08
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影响因子：
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通讯作者：
Seoane F
中科院分区：
文献类型：
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作者：
Mohino-Herranz I;Gil-Pita R;Ferreira J;Rosa-Zurera M;Seoane F
实时确定受试者的压力水平可能对某些专业活动特别有意义，以便对士兵、飞行员、应急人员和其他负责人类生命的专业人员进行监测。评估当前执行手头任务的心理健康状况可能会避免不必要的风险。为了获得这些知识，本研究使用定制的非侵入式可穿戴仪器记录了两项生理测量结果，这些仪器可测量心电图 (ECG) 和胸部电生物阻抗 (TEB) 信号。通过评估一组精简的选定特征来提取每次测量的相关信息。这些特征主要是从原始时间测量的过滤和处理版本以及某些统计和描述参数的计算中获得的。使用遗传算法来选择减少的特征集，从而限制了实时实现的计算成本。人们已经研究了不同的分类方法，但本研究选择了神经网络，因为它们代表了解决方案的智能性和计算复杂性之间的良好权衡。考虑了三种不同的应用场景。在第一种情况下，所提出的系统能够区分不同类型的活动，概率误差为 21.2%，对于编码为中性、情感、精神和身体的活动。在第二种情况下，所提出的解决方案区分了中性、悲伤和厌恶三种不同的情绪状态，概率误差为 4.8%。在第三种情况下，系统能够以 32.3% 的概率误差区分低精神负荷和精神超负荷。计算了计算成本，并在市售的基于 Android 的智能手机中实施了该解决方案。结果表明，与当前智能手机的标称计算负载相比，此类监控解决方案的执行可以忽略不计。
Determining the stress level of a subject in real time could be of special interest in certain professional activities to allow the monitoring of soldiers, pilots, emergency personnel and other professionals responsible for human lives. Assessment of current mental fitness for executing a task at hand might avoid unnecessary risks. To obtain this knowledge, two physiological measurements were recorded in this work using customized non-invasive wearable instrumentation that measures electrocardiogram (ECG) and thoracic electrical bioimpedance (TEB) signals. The relevant information from each measurement is extracted via evaluation of a reduced set of selected features. These features are primarily obtained from filtered and processed versions of the raw time measurements with calculations of certain statistical and descriptive parameters. Selection of the reduced set of features was performed using genetic algorithms, thus constraining the computational cost of the real-time implementation. Different classification approaches have been studied, but neural networks were chosen for this investigation because they represent a good tradeoff between the intelligence of the solution and computational complexity. Three different application scenarios were considered. In the first scenario, the proposed system is capable of distinguishing among different types of activity with a 21.2% probability error, for activities coded as neutral, emotional, mental and physical. In the second scenario, the proposed solution distinguishes among the three different emotional states of neutral, sadness and disgust, with a probability error of 4.8%. In the third scenario, the system is able to distinguish between low mental load and mental overload with a probability error of 32.3%. The computational cost was calculated, and the solution was implemented in commercially available Android-based smartphones. The results indicate that execution of such a monitoring solution is negligible compared to the nominal computational load of current smartphones.