【摘要】：心肺復(fù)蘇已成為全球最為推崇且普及最為廣泛的急救技術(shù)，其技能培訓(xùn)對于急救護(hù)理學(xué)教學(xué)有著重要意義。隨著數(shù)字醫(yī)療技術(shù)、計(jì)算機(jī)科學(xué)技術(shù)的發(fā)展，涌現(xiàn)了大量用于心肺復(fù)蘇教學(xué)訓(xùn)練的設(shè)備，其中最具有代表性的是醫(yī)療培訓(xùn)模擬人。但是，目前的心肺復(fù)蘇模擬人教學(xué)系統(tǒng)，僅從模擬病人內(nèi)部體征的感知角度，采集了操作者的按壓深度、呼吸等數(shù)據(jù)，進(jìn)而對操作者的動(dòng)作做出判別，而忽略了操作者的按壓姿勢、手形、垂直用力的正確性，可見系統(tǒng)還不夠全面，需要進(jìn)一步的完善。同時(shí)，在心肺復(fù)蘇操作效果評價(jià)方面，心肺復(fù)蘇操作效果與操作者的按壓深度、按壓位置、按壓頻率以及呼吸量、呼吸頻率、垂直用力、正確的姿勢、手形等諸多因素有關(guān)，如何將這些影響因素通過一定的方法進(jìn)行分析、整合，形成對心肺復(fù)蘇操作的綜合評價(jià)，是目前心肺復(fù)蘇教學(xué)領(lǐng)域需要解決的問題之一。因此，本文針對這幾方面展開研究，具體工作如下： 心肺復(fù)蘇教學(xué)評價(jià)模型的總體設(shè)計(jì)，包括利用模擬人內(nèi)部傳感器采集按壓、呼吸數(shù)據(jù)，利用圖像傳感器識(shí)別按壓姿勢、垂直用力、手形，以及綜合各傳感器的數(shù)據(jù)得出綜合評價(jià)結(jié)果等三個(gè)部分。其中，心肺復(fù)蘇手形識(shí)別以及綜合評價(jià)模型的建立是本文的重點(diǎn)和難點(diǎn)。 在心肺復(fù)蘇手形識(shí)別方面，本文借鑒靜態(tài)手勢識(shí)別方法，設(shè)計(jì)了一種基于組合特征的心肺復(fù)蘇手形識(shí)別方法。首先，通過橢圓膚色模型以及對圖像進(jìn)行相應(yīng)預(yù)處理，，獲得按壓手形二值圖像；在特征提取中，本文提出了一種基于輪廓凸包和凹陷的結(jié)構(gòu)特征提取算法，利用手指個(gè)數(shù)、手指指尖夾角關(guān)系等手形結(jié)構(gòu)特征作為局部特征，并利用改進(jìn)的傅里葉描述子作為全局特征，形成心肺復(fù)蘇手形的組合特征；在手形識(shí)別中，根據(jù)局部特征和全局特征的各自特點(diǎn)，設(shè)計(jì)了一種逐步排除的快速識(shí)別方法，最后利用基于歐氏距離的模版匹配方法進(jìn)行識(shí)別。實(shí)驗(yàn)結(jié)果表明，本文的方法可以有效的區(qū)分正確、錯(cuò)誤按壓手形。 在心肺復(fù)蘇評價(jià)方面，為了綜合各傳感器采集的評價(jià)指標(biāo)數(shù)據(jù)，對學(xué)生的心肺復(fù)蘇操作做出客觀全面的綜合評價(jià)，本文引入數(shù)據(jù)融合的思想，提出基于支持向量回歸的決策級評價(jià)模型。本文選用混合核函數(shù)構(gòu)造支持向量回歸模型，并利用混沌差分進(jìn)化算法對混合核SVR的參數(shù)進(jìn)行優(yōu)化選擇，進(jìn)一步提高了模型的擬合精度和泛化能力，然后利用改進(jìn)的支持向量回歸模型對學(xué)生的心肺復(fù)蘇操作進(jìn)行綜合評價(jià)。 最后，通過實(shí)驗(yàn)驗(yàn)證本文所提方法的有效性。實(shí)驗(yàn)表明，本文的評價(jià)模型可以有效的融合傳感器采集的各項(xiàng)指標(biāo)因素，得出綜合評價(jià)結(jié)果，從而為心肺復(fù)蘇教學(xué)系統(tǒng)建立了科學(xué)的評價(jià)體系。
[Abstract]:Cardiopulmonary resuscitation (CPR) has become the most popular and widely used first aid technology in the world. With the development of digital medical technology and computer science and technology, a large number of equipment used in cardiopulmonary resuscitation teaching and training have emerged, among which the most representative is the medical training simulator. However, the current teaching system for simulating cardiopulmonary resuscitation (CPR) only collects the data of the operator's pressing depth, breathing and so on from the perspective of the perception of the internal physical signs of the simulated patient, and then makes a judgment on the action of the operator. The correctness of the operator's pressing posture, hand shape and vertical force is ignored, so the system is not comprehensive enough and needs further improvement. At the same time, in the evaluation of the operational effect of cardiopulmonary resuscitation, the operational effect of CPR is related to the operator's pressing depth, pressing position, pressing frequency, breathing quantity, respiratory frequency, vertical force, correct posture, hand shape and so on. It is one of the problems that need to be solved in the teaching field of cardiopulmonary resuscitation (CPR) how to analyze and integrate these influencing factors through certain methods to form a comprehensive evaluation of the operation of cardiopulmonary resuscitation (CPR). Therefore, this paper studies these aspects, the specific work is as follows: the overall design of teaching evaluation model of cardiopulmonary resuscitation, including the use of simulated human internal sensors to collect compression, respiratory data, The image sensor is used to identify the pressing position, the vertical force, the hand shape, and the synthetic evaluation result by synthesizing the data of each sensor. Among them, the recognition of hand shape and the establishment of comprehensive evaluation model are the key and difficult points in this paper. In the aspect of hand shape recognition of CPR, a hand recognition method based on combined features is designed by using static gesture recognition method. Firstly, by using the elliptical skin model and the corresponding preprocessing of the image, the binary image of the pressing hand is obtained. In the feature extraction, a structural feature extraction algorithm based on the contour convex hull and the depression is proposed, using the number of fingers, the number of fingers, The finger fingertip angle relation is used as the local feature, and the improved Fourier descriptor is used as the global feature to form the combined character of the hand shape of cardiopulmonary resuscitation. According to the respective characteristics of local and global features, a fast recognition method with gradual exclusion is designed. Finally, the template matching method based on Euclidean distance is used to identify. The experimental results show that the proposed method can effectively distinguish the correct and wrong pressing hand shape. In the evaluation of cardiopulmonary resuscitation (CPR), in order to synthesize the evaluation index data collected by various sensors and make an objective and comprehensive evaluation of the students' CPR operation, this paper introduces the idea of data fusion. A decision level evaluation model based on support vector regression is proposed. In this paper, the hybrid kernel function is used to construct the support vector regression model, and the chaotic differential evolution algorithm is used to optimize the parameters of the hybrid kernel SVR, which further improves the fitting accuracy and generalization ability of the model. Then the improved support vector regression model was used to evaluate the cardiopulmonary resuscitation (CPR). Finally, the effectiveness of the proposed method is verified by experiments. The experimental results show that the evaluation model of this paper can effectively fuse the various index factors collected by sensors and obtain the comprehensive evaluation results, thus establishing a scientific evaluation system for the teaching system of cardiopulmonary resuscitation.
【學(xué)位授予單位】：遼寧大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TP391.41;R459.7

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相關(guān)期刊論文 前1條

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