Time Series Data Mining for Multimodal Bio-Signal Data
Time Series Data Mining for Multimodal Bio-Signal Data
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发表时间：
2006
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通讯作者：
Masaki Aono;Y. Sekiguchi;Y. Yasuda;Naoya Suzuki;Yohei Seki
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作者：
Masaki Aono;Y. Sekiguchi;Y. Yasuda;Naoya Suzuki;Yohei Seki
人们认为，人体的生理活动和心理活动之间存在着密切的联系。这些身体活动可以使用各种多模态生物信号来测量，这些多模态生物信号采用侵入式或非侵入式传感器。相比之下，尽管心理活动已经从各种角度进行了建模，但它们大多与大脑活动有关。我们专注于将物理多模态生物信号映射为具有诸如嗜睡、疲劳和集中等精神状态的时间序列数据。我们将讨论两种数学数据挖掘工具：（1）区间互相关系数;（2）区间互协方差函数系数。给定一个多模态的时间序列的生物信号数据采集在一个给定的频率使用非侵入性传感器连接到一个人类志愿者，我们的方法旨在预测人类受试者的精神状态。主要目的是研究我们的方法在预测学生的心理状态在大学课堂上的课的可行性。先前从生物信号预测精神状态的尝试大多基于脑电图（EEG）、心电图（ECG）或眼电图（EOG），但尚未尝试将它们联合收割机组合。然而，它往往是很难获得稳定的EEG信号，从学生在大学教室，因为产生的文物从身体和眼睛的运动。在此基础上，考虑了多模态生物信号及其组合，引入了区间互相关系数和区间互协方差函数系数作为数据挖掘工具，用于映射人体的生理和心理状态。我们使用配备了多个传感器的受试者进行了实验，并将结果与我们的数据挖掘方法的输出进行了比较。初步实验表明，我们的方法产生合理的结果，并允许我们控制实验参数，以科普个别的变化。我们的方法也适用于监测个人医疗保健，车辆驾驶员和商业团体会议中的个人。
Summary It is believed that there is a close correlation between the physical and mental activities of human body. These physical activities can be measured using various multimodal bio-signals employing either invasive or non-invasive sensors. In contrast, although mental activities have been modeled from various standpoints, they have been associated mostly with brain activities. We have focused on mapping physical multimodal bio-signals as time series data with mental states such as somnolence, fatigue, and concentration. We will discuss two mathematical data mining tools: (1) the interval cross-correlation coefficient; and (2) the interval cross- covariance function coefficient. Given a multimodal time series bio-signal data acquired at a given frequency using non-invasive sensors attached to a human volunteer, our methods aimed to predict the mental state of a human subject. The primary objective was to examine the feasibility of our methods in predicting the mental state of students during lessons in a university classroom. Previous attempts to predict mental states from bio-signals have mostly been based on electroencephalogram (EEG), electrocardiogram (ECG), or electrooculogram (EOG), but have not tried to combine them. However, it is often difficult to obtain stable EEG signals from students in a university classroom because of artifacts arising from body and eye movements. Based on this observation, we considered simultaneous multimodal bio-signals with their combination, and introduced interval cross- correlation coefficient and interval cross-covariance function coefficient as data mining tools for mapping the physical and mental states of the human body. We conducted experiments using subjects equipped with multiple sensors, and compared the results with the outputs of our data mining methods. Preliminary experiments show that our method produces reasonable results and allows us to control the experimental parameters to cope with individual variations. Our method is also applicable to monitoring personal health care, vehicle drivers, and individuals in business group meetings.