Statistical recognition of breathing by MS Kinect depth sensor
Statistical recognition of breathing by MS Kinect depth sensor
复制标题
MS Kinect深度传感器对呼吸的统计识别
DOI：
10.1109/iwcim.2015.7347062
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
A. Procházka
中科院分区：
文献类型：
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作者：
M. Schätz;Fabio Centonze;J. Kuchynka;O. Tupa;O. Vysata;O. Geman;A. Procházka
使用呼吸带等接触方法测量呼吸对患者来说非常不舒服，并且在进行复杂的睡眠分析时，来自不同传感器的电缆可能会极大地影响睡眠质量。本文介绍了使用MS Kinect深度传感器的非接触式呼吸测量，并将所得结果与流量计观察到的呼吸记录进行了比较。方法的一部分，该文件是专门的光谱分析所获得的数据，特征提取，其贝叶斯分类。所提出的分类器是能够区分睡眠和唤醒类的准确性为100%（交叉验证：0）给定的数据。在给定的情况下，分类为3类（睡眠，跌倒和清醒）的准确率为97%（交叉验证：0.0248）。
Measuring of breathing with contact methods, like respiratory belts, is very uncomfortable for patients and in case of complex sleep analysis, cables from different sensors can substantially affect the quality of the sleep. This paper presents the contactless measuring of breathing using the MS Kinect depth sensor, and it compares the results obtained with records of breathing observed by the flowmetry. The methodological part of the paper is devoted to spectral analysis of data acquired, feature extraction, and their Bayesian classification. The proposed classifier is able to distinguish the Sleep and Wake classes with the accuracy of 100% (cross-validation: 0) for given data. The achieved accuracy of classification into 3 classes (Sleep, Falling Asleep and Wake) is 97% (cross-validation: 0.0248) in the given case.