Signal Processing of Multimodal Mobile Lifelogging Data Towards Detecting Stress in Real-World Driving
Signal Processing of Multimodal Mobile Lifelogging Data Towards Detecting Stress in Real-World Driving
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DOI：
10.1109/tmc.2018.2840153
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发表时间：
2019-03-01
影响因子：
7.9
通讯作者：
Fairclough, Stephen
中科院分区：
文献类型：
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作者：
Dobbins, Chelsea;Fairclough, Stephen
压力是一种负面情绪，是日常生活的一部分。然而，频繁发作或长时间的压力可能对长期健康有害。然而，培养自我意识是培养自我调节这些经历的有效方法的一个重要方面。移动的生活记录系统提供了一个理想的平台，通过不断记录心理生理和行为数据来提高对负面情绪状态的认识，从而支持压力的自我调节。然而，从大量原始数据中获取有意义的信息是一个重大挑战，因为在检测压力之前必须准确地量化和处理这些数据。这项工作描述了一组算法，旨在处理多个流的生活记录数据的压力检测的上下文中的真实的世界驾驶。已经进行了两次数据收集练习，其中多模态数据，包括原始心血管活动和驾驶信息，收集了21人在日常通勤旅程。我们的方法使我们能够1）预处理原始生理数据以计算心率变异性的有效测量，这是压力的重要标志，2）识别/校正原始生理数据中的伪影，以及3）提供用于检测压力的几个分类器之间的比较。结果是积极的，集成分类模型为现实世界中的压力二进制检测提供了86.9%的最高准确率。
Stress is a negative emotion that is part of everyday life. However, frequent episodes or prolonged periods of stress can be detrimental to long-term health. Nevertheless, developing self-awareness is an important aspect of fostering effective ways to self-regulate these experiences. Mobile lifelogging systems provide an ideal platform to support self-regulation of stress by raising awareness of negative emotional states via continuous recording of psychophysiological and behavioral data. However, obtaining meaningful information from large volumes of raw data represents a significant challenge because these data must be accurately quantified and processed before stress can be detected. This work describes a set of algorithms designed to process multiple streams of lifelogging data for stress detection in the context of real world driving. Two data collection exercises have been performed where multimodal data, including raw cardiovascular activity and driving information, were collected from 21 people during daily commuter journeys. Our approach enabled us to 1) pre-process raw physiological data to calculate valid measures of heart rate variability, a significant marker of stress, 2) identify/correct artefacts in the raw physiological data, and 3) provide a comparison between several classifiers for detecting stress. Results were positive and ensemble classification models provided a maximum accuracy of 86.9 percent for binary detection of stress in the real-world.