【摘要】：近些年來,隨著生物傳感器技術(shù)和可穿戴技術(shù)迅速的發(fā)展,越來越多用于監(jiān)測人的生命健康活動的可穿戴產(chǎn)品正不斷進(jìn)入人們的生活。在可穿戴設(shè)備上采集和記錄人體生理健康數(shù)據(jù),如脈搏、呼吸、體溫等,已經(jīng)變得現(xiàn)實(shí)可行。然而,在連續(xù)長時間范圍內(nèi)的人體的生理信號數(shù)據(jù)量過大,不利于用戶分析、觀察和提取有價值信息。因此,論文研究的目的就是實(shí)現(xiàn)一種從連續(xù)而大量的人體生理數(shù)據(jù)中提取出簡潔而有效的關(guān)于人體生理狀態(tài)變化信息的判別技術(shù)。利用此技術(shù)可以將人體的生理狀態(tài)分為兩類,一類是普通狀態(tài),即人體處于靜息下的狀態(tài)；另一類是事件狀態(tài),即人體經(jīng)歷活動、外力刺激或情緒變化等狀態(tài)。該方法利用脈搏、呼吸、體溫三種信號的各自判別機(jī)制對人體在相同時間內(nèi)的生理狀態(tài)進(jìn)行二分類判別,并將分類結(jié)果以可視化等級分布圖的形式回饋給用戶,用戶可以根據(jù)自身狀態(tài)等級的高低對相應(yīng)時間內(nèi)的生理數(shù)據(jù)進(jìn)行選擇性關(guān)注。論文對呼吸信號和體溫信號采用的是設(shè)置閾值的方式,通過判斷從呼吸波中提取出的呼吸頻率和體溫傳感器采集到的人體體溫的溫度是否超過正常的閾值范圍來將人體的生理狀態(tài)進(jìn)行二分類判別。而對于脈搏信號,采用基于離散小波變換的方法來去除掉信號中摻雜的高頻噪聲和基線漂移,從脈搏波的時域中提取周期和主波高度這兩個參數(shù)作為支持向量機(jī)(SVM)的輸入特征向量,通過有監(jiān)督學(xué)習(xí)的訓(xùn)練方法來構(gòu)建二分類模型,從脈搏的角度分類出人的生理狀態(tài)是處于普通狀態(tài)還是事件狀態(tài)。本文通過運(yùn)動、睡眠、喝酒三組實(shí)驗(yàn),對SVM的分類性能進(jìn)行了統(tǒng)計(jì)分析和評價,驗(yàn)證了SVM對人體生理狀態(tài)判別具有良好的效果。通過將三種信號的判別結(jié)果進(jìn)行融合顯示,利用可視化軟件將人體狀態(tài)等級隨時間的分布情況展示給用戶,這樣提供給用戶一個觀察自身生理狀態(tài)變化的整體而簡潔的視角。
[Abstract]:In recent years, with the rapid development of biosensor technology and wearable technology, more and more wearable products used to monitor human life and health activities are coming into people's lives. It has become feasible to collect and record physical and health data on wearable devices, such as pulse, respiration, body temperature and so on. However, the amount of physiological signals in a continuous long time range is too large, which is not conducive to user analysis, observation and extraction of valuable information. Therefore, the purpose of this paper is to realize a simple and effective discriminant technique for extracting simple and effective information about the changes of human physiological state from the continuous and large amount of human physiological data. By using this technique, the physiological state of human body can be divided into two categories, one is the normal state, that is, the human body is in the state of resting, the other is the state of events, that is, the state of human body experiencing activity, external force stimulation or emotional change. In this method, the physiological state of human body at the same time is judged by using the discriminant mechanism of pulse, respiration and body temperature, and the classification result is given back to the user in the form of visual grade distribution map. The user can selectively pay attention to the physiological data in the corresponding time according to the level of their own state. In this paper, the method of setting threshold is used for respiratory signal and body temperature signal. By judging whether the respiration frequency extracted from the respiration wave and the temperature of the body temperature collected by the temperature sensor exceed the normal threshold range, the physiological state of the human body can be classified into two categories. For pulse signal, the method based on discrete wavelet transform is used to remove the high frequency noise and baseline drift. Two parameters, period and main wave height, are extracted from the time domain of pulse wave as input eigenvector of support vector machine (SVM), and two classification models are constructed by supervised learning training method. The physiological state of a person is classified from the point of view of pulse whether he is in a normal state or an event state. In this paper, the classification performance of SVM was statistically analyzed and evaluated through three groups of experiments: exercise, sleep and drinking, and it was proved that SVM has a good effect on the identification of physiological state of human body. The results of the three signals are fused and displayed, and the distribution of the human state level with time is displayed to the user by using the visualization software, which provides the user with an overall and concise perspective to observe the changes of his own physiological state.
【學(xué)位授予單位】：東北大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP212;TN911.7

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