本文選題：跌倒預(yù)測　切入點：支持向量機　出處：《蘇州大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

【摘要】：當(dāng)今世界面臨著嚴(yán)重的人口老齡化問題,老人由于身體機能下降、平衡協(xié)調(diào)能力減弱和視力變差等生理原因,容易發(fā)生跌倒。跌倒預(yù)測系統(tǒng)可以及時檢測跌倒并進(jìn)行報警,可以減少老人跌倒后等待救助的時間,降低跌倒對人體造成的傷害,減少因跌倒產(chǎn)生的醫(yī)療開支,增強老年人獨立生活的信心。本文在對當(dāng)前跌倒檢測方法進(jìn)行總結(jié)分析的基礎(chǔ)上,采用基于穿戴式設(shè)備的方法進(jìn)行跌倒預(yù)測研究。課題的創(chuàng)新點在于結(jié)合加速度特征和姿態(tài)角特征,同時采用機器學(xué)習(xí)算法中的支持向量機算法作為分類算法研究跌倒預(yù)測,即在人體跌倒碰撞地面前進(jìn)行跌倒的預(yù)判。本文將跌倒預(yù)測問題作為一個二分類問題來處理,建立跌倒預(yù)測分類模型,實現(xiàn)了跌倒預(yù)測和跌倒遠(yuǎn)程報警,并對跌倒保護裝置進(jìn)行了探討。本文首先綜述了不同的跌倒預(yù)測算法,確定支持向量機算法作為分類算法,闡述了支持向量機的理論原理,說明了實現(xiàn)支持向量機算法的過程及參數(shù)尋優(yōu)方法。然后將傳感器設(shè)備穿戴在人體腰部,建立人體三維坐標(biāo)系,采集3軸加速度、3軸角速度和3軸磁場數(shù)據(jù)。定義了4種跌倒行為和10種日常活動行為以及加速度和角速度統(tǒng)計量,分析不同行為過程的加速度特征、角速度特征和姿態(tài)角特征,發(fā)現(xiàn)采用這些特征進(jìn)行跌倒預(yù)測具有很強的可行性。在特征分析的基礎(chǔ)上,進(jìn)行特征提取,根據(jù)加速度和角速度統(tǒng)計量的變化規(guī)律,在時域中提取4個加速度特征和4個角速度特征,在捷聯(lián)慣導(dǎo)系統(tǒng)中解算姿態(tài)角,提取姿態(tài)角特征,將提取的9個特征組成特征向量。接著對提取的9個特征進(jìn)行特征選擇,采集跌倒行為樣本和日�；顒有袨闃颖窘M成訓(xùn)練樣本集和測試樣本集,對樣本采集過程中的變量進(jìn)行討論,分別采用序列前向選擇方法和序列后向選擇方法進(jìn)行特征選擇,綜合兩種方法得到最優(yōu)特征組合,最優(yōu)特征組合包含3個特征,最終得到最優(yōu)跌倒預(yù)測分類模型,最優(yōu)跌倒預(yù)測分類模型對應(yīng)的跌倒行為檢測率為100%,日�；顒有袨闄z測率為100%,平均前置時間為291ms,證明了本文提出的跌倒預(yù)測算法的有效性和可行性。最后本文對跌倒遠(yuǎn)程報警及跌倒保護裝置進(jìn)行了研究,采用SIM900A模塊實現(xiàn)跌倒遠(yuǎn)程短信報警,將一款手動充氣氣囊裝置改造成自動充氣氣囊裝置,設(shè)計了2種不同的連桿機構(gòu)連接舵機和氣囊裝置,并通過實驗選擇實際耗時較少的連桿機構(gòu),發(fā)現(xiàn)從檢測到跌倒到打開壓縮氣瓶最少耗時約為133ms,在目前的最優(yōu)跌倒預(yù)測分類模型下,留給氣囊的充氣時間只有約158ms,而在現(xiàn)有的壓縮氣瓶條件下,氣體完全釋放所需的時間約為452ms,所以計算了在滿足158ms充氣時間條件下的氣瓶規(guī)格,然后在現(xiàn)有氣瓶條件下,通過實驗對跌倒保護裝置進(jìn)行了整體測試,實驗結(jié)果表明跌倒保護裝置可以在跌倒碰撞地面前啟動,具有一定的可行性。
[Abstract]:The world is facing the serious problem of population aging, the elderly due to the decline in physical function, balance ability weakened and poor eyesight physiological reasons, prone to fall. Fall prediction system can detect falls and alarm, can reduce falls among the elderly wait for the rescue time, falls the harm to human body, reduce the by the fall of the medical expenses, enhance the elderly independent living confidence. Based on the analysis on the current fall detection methods, the prediction method based on the fall of wearable devices. The innovation point is to combine the characteristics of acceleration and attitude angle characteristics, at the same time using machine learning algorithm of support vector machine algorithm the prediction falls as the classification algorithm research, which falls in the body hit the ground before the fall of the pre judgment. This paper will fall as the forecasting problem A two classification problem and establish classification model can fall, fall fall prediction and remote alarm, and the fall protection device are discussed. This paper reviews the different prediction algorithms fall, determine the support vector machine algorithm as the classification algorithm, and expounds the principle of support vector machine, description of the process and the parameters of support vector machine algorithm optimization method. Then the sensor device worn on the waist, the establishment of human 3D coordinates, collecting 3 axis acceleration, 3 axis and 3 axis angular velocity field data. It defines 4 kinds of fall behavior and 10 kinds of daily activities and the acceleration and angular velocity acceleration statistics analysis the characteristics of different behavior process, angular velocity and attitude angle characteristic features, these features are found by the fall prediction is feasible. Based on the analysis, the feature extraction Take, according to the variation of acceleration and angular velocity statistics, 4 acceleration characteristics and 4 angular velocity feature extraction in time domain, in the strapdown inertial navigation system for the calculation of attitude angle, attitude angle feature extraction, 9 feature extraction feature vector. Then the extracted 9 features for feature selection fall, acquisition behavior sample and daily activities behavior of samples consisting of training samples and the test samples, the sample collection process variables are discussed, using the sequential forward selection method and the sequential backward selection method for feature selection respectively, two methods to obtain the optimal combination of features, the best combination of features contains 3 characteristics, finally the optimal fall prediction classification model, optimal prediction fall fall behavior classification models corresponding to the detection rate was 100%, the daily activity detection rate was 100%, the average lead time is 291ms, that the The effectiveness of the proposed fall prediction algorithm and feasibility. Finally we study the fall and fall remote alarm protection device, using SIM900A module to realize the remote alarm message will fall, a manual air bag device into automatic air bag device, designed 2 different linkage mechanism connected with the steering engine and the airbag device, and the actual linkage is selected through experiments with less time-consuming, found from detection to fall to open the compressed gas cylinders least time is about 133ms, at present the optimal classification model to predict falls, balloon inflation time is only about 158ms, while in the condition of compressed gas cylinders, gas release time required is about 452ms. So the calculation to meet the specifications of the cylinders 158ms inflation time conditions, and then in the existing cylinder condition, through the whole test of the fall protection device The experimental results show that the fall protection device can start before the fall and collide on the ground, and it is feasible.

【學(xué)位授予單位】：蘇州大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TH789

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本文編號：1569794