本文選題：普適計算 + 智能手機�。� 參考：《南京大學(xué)》2017年碩士論文

【摘要】：睡眠是日常生活中重要的一環(huán),現(xiàn)代醫(yī)學(xué)研究成果表明睡眠中并不只是簡單的"睡著了",宏觀上睡眠可以劃分為N-REM(非快速眼動期)和REM(快速眼動期)兩階段,兩階段呈現(xiàn)相互交替的周期分布。不同的睡眠狀態(tài)具有截然不同的作用,并且睡眠狀態(tài)的分布并不穩(wěn)定,具有易被破壞的特點。睡眠周期不僅具有醫(yī)學(xué)意義,在不同的睡眠階段進行喚醒會對人產(chǎn)生顯著的影響�，F(xiàn)有的睡眠周期檢測手段具有成本高、專業(yè)要求高、侵入性強的特點。能夠進行輕量級、低成本、非侵入式的睡眠周期識別對改善用戶的生活和對睡眠的認(rèn)知都有顯著的作用。隨著智能手機搭載的傳感器越來越多樣化以及通信、計算能力的提升,越來越多的研究工作將目標(biāo)設(shè)定在使用智能手機感知人們生活的方方面面。而睡眠由于用戶意識下降的不可自知性和其重要的醫(yī)學(xué)意義成為諸多研究的焦點。睡眠周期分布由于難以觀察、識別線索弱、個體差異明顯等不利因素,使得睡眠周期識別成為睡眠相關(guān)工作中的難點。本文對使用智能手機識別人的睡眠狀態(tài)進行了探索。從醫(yī)學(xué)文獻中關(guān)于睡眠狀態(tài)和用戶夜間的外部表現(xiàn)研究成果及智能手機所搭載的傳感設(shè)備所搭載的傳感器的感知能力出發(fā),將目標(biāo)設(shè)定為日常生活場景中,對用戶清醒以及用戶睡眠期間的REM(快速眼動期)、N-REM(非快速眼動期)三種狀態(tài)的識別。建立了完整的睡眠周期識別系統(tǒng),從智能手機搭載的傳感器以及系統(tǒng)運行狀態(tài)獲得數(shù)據(jù),通過模式識別方法對用戶睡眠期間發(fā)生的事件進行識別,結(jié)合醫(yī)學(xué)背景中的知識提取這些事件的相應(yīng)特征,通過序列化模式識別算法對用戶的睡眠周期進行識別。并進行了大量實驗構(gòu)造了基于智能手機探測用戶睡眠周期識別的數(shù)據(jù)集。本文的貢獻在于:·實現(xiàn)了基于智能手機的睡眠周期識別系統(tǒng):該系統(tǒng)通過智能手機搭載的傳感系統(tǒng)對用戶睡眠事件進行識別,在事件識別的基礎(chǔ)上結(jié)合醫(yī)學(xué)知識,采用決策融合的方式對用戶的清醒、REM睡眠狀態(tài)、N-REM睡眠狀態(tài)進行識別�！みM行了長期的真實場景下的實驗:同時采用眼動儀監(jiān)測和智能手機采集的方式,構(gòu)建了使用智能手機識別睡眠周期的數(shù)據(jù)集。在實驗數(shù)據(jù)集上對系統(tǒng)的有效性進行了驗證,對實驗中表現(xiàn)出的個體差異、模型適配性、模型隨數(shù)據(jù)集規(guī)模的變化進行了分析�！せ谒咧芷谧R別系統(tǒng),實現(xiàn)了基于睡眠周期識別結(jié)果改善用戶生活的應(yīng)用程序:智能喚醒鬧鐘和睡眠統(tǒng)計報告。
[Abstract]:Sleep is an important part of daily life. Modern medical research shows that sleep is not simply "asleep", but can be divided into N-REM (non-rapid eye movement) and REM (rapid eye movement) stages. The two phases present alternating periodic distributions. Different sleep states play a different role, and the distribution of sleep states is unstable and easy to be destroyed. Sleep cycle is not only of medical significance, but also has a significant effect on people during different sleep stages. The existing sleep cycle detection methods are characterized by high cost, high professional requirements and strong invasion. The ability to perform lightweight, low-cost, non-invasive sleep cycle identification plays a significant role in improving the user's life and awareness of sleep. With the increasing diversity of sensors, communication and computing power, more and more research has focused on the use of smart phones to perceive all aspects of people's lives. However, sleep has become the focus of many researches because of the decrease of user consciousness and its important medical significance. Because the distribution of sleep cycle is difficult to observe, the identification clue is weak, the individual difference is obvious and so on, the sleep cycle recognition becomes the difficulty in the sleep related work. This paper explores the use of smart phones to identify sleep states. Based on the research results of sleep state and night external performance of users in medical literature and the sensing ability of sensor devices carried on smart phones, the target is set in the daily life scene. Recognition of three states of REM (rapid eye movement) (REM) and non-rapid eye movement (NREM) during the user's sleep. A complete sleep cycle recognition system is established. The data are obtained from the sensors and the system running state on the smart phone, and the events occurring during the user's sleep are identified by the pattern recognition method. Combined with the knowledge in medical background, the corresponding features of these events are extracted, and the sleep cycle of users is identified by serialization pattern recognition algorithm. A large number of experiments were carried out to construct the sleep cycle identification data set based on smart phone detection. The contribution of this paper lies in the realization of the sleep cycle recognition system based on smart phone. The system recognizes the sleep events of the user through the sensor system of the smart phone, and combines the medical knowledge with the event recognition. Using the method of decision fusion to identify the sleep state of the user's awake REM sleep and N-REM sleep. The experiments were carried out under the long-term real scene: eye movement monitor and smart phone acquisition were used at the same time. A data set using smart phones to identify sleep cycles is constructed. The validity of the system is verified on the experimental data set, and the individual differences, model adaptability and the variation of the model with the size of the data set are analyzed, based on the sleep cycle recognition system. The application program of improving user's life based on the result of sleep cycle recognition is implemented: intelligent wake-up alarm clock and sleep statistics report.
【學(xué)位授予單位】：南京大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP212.9

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