本文關(guān)鍵詞：基于多傳感器的人體運(yùn)動識別算法與應(yīng)用研究　出處：《重慶郵電大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 人體運(yùn)動識別 跌倒檢測 兩層決策樹 準(zhǔn)確率 系統(tǒng)能耗

【摘要】：隨著社會老齡人口數(shù)量的增加和家庭結(jié)構(gòu)的變化,中國“空巢”老人數(shù)量大幅度增加,老人的生活從以前依靠子女照顧到現(xiàn)在自動求助模式。因此老年人日常自動監(jiān)測會對老年人的日常生活以及社會的穩(wěn)定都會有很大的幫助。針對現(xiàn)有的基于多傳感器的人體運(yùn)動識別算法復(fù)雜度較高很難將其搬移到便攜的終端以及系統(tǒng)的能耗大需要頻繁更換系統(tǒng)電池或充電的問題,本文提出了基于兩層決策樹識別器的人體運(yùn)動識別算法,并以兩層決策樹算法為基礎(chǔ),設(shè)計(jì)了一套完整的跌倒檢測系統(tǒng),實(shí)現(xiàn)了對老年人的跌倒進(jìn)行識別。本文的主要工作如下:1.分析和總結(jié)了現(xiàn)有的基于傳感器的人體運(yùn)動識別的理論和研究方法,并對數(shù)據(jù)采集模塊、數(shù)據(jù)預(yù)處理的方法、特征提取和選擇的技術(shù)、分類識別的識別器以及無線短距離通信技術(shù)等五個(gè)方面進(jìn)行了詳細(xì)分析。2.根據(jù)日常生活中人體運(yùn)動的特點(diǎn)以及采集的數(shù)據(jù)特性,提出了一種基于兩層決策樹識別器的人體運(yùn)動識別算法,通過減少對加速度數(shù)據(jù)的提取和處理來降低系統(tǒng)的能耗和算法的復(fù)雜度。在本研究中還采用了卡爾曼濾波對數(shù)據(jù)進(jìn)行濾波處理以及長度為2s的半重疊的固定窗口對數(shù)據(jù)進(jìn)行加窗處理,來提高系統(tǒng)的識別率以及降低系統(tǒng)算法的復(fù)雜度。3.使用MPU6050傳感器模塊和藍(lán)牙通信模塊采集20位實(shí)驗(yàn)者在不同狀態(tài)下八種運(yùn)動的數(shù)據(jù)用于訓(xùn)練和驗(yàn)證識別器的準(zhǔn)確率。用WEKA軟件驗(yàn)證的結(jié)果顯示,基于兩層決策樹識別器的準(zhǔn)確率分別為98.44%和94.16%,并對比了其他分類識別器,其各方面的性能都是較優(yōu)的。4.設(shè)計(jì)并實(shí)現(xiàn)了一套完整的老年人跌倒檢測報(bào)警系統(tǒng),具體跌倒檢測算法是基于兩層決策樹識別器的。并通過實(shí)驗(yàn)數(shù)據(jù)表明,本文設(shè)計(jì)的跌倒檢測系統(tǒng)的準(zhǔn)確識別率達(dá)到了95%,是有一定市場利用價(jià)值的。
[Abstract]:With the increase in the number of elderly people in society and the changes in family structure, the number of "empty nests" in China has increased by a large margin. The life of the old people depends on their children to take care of them now. Therefore, automatic daily monitoring of the elderly will be of great help to the daily life of the elderly as well as the stability of the society. It is very difficult to move the sensor to portable terminals with high complexity and the problem that the energy consumption of the system needs to replace the battery or charge the system frequently. In this paper, a human motion recognition algorithm based on two-layer decision tree recognizer is proposed. Based on the two-layer decision tree algorithm, a complete fall detection system is designed. The main work of this paper is as follows: 1. The existing theory and research methods of human motion recognition based on sensor are analyzed and summarized, and the data acquisition module is given. Methods of data preprocessing, feature extraction and selection techniques. The classifier and wireless short distance communication technology are analyzed in detail. 2. According to the characteristics of human body movement and data collection in daily life. A human motion recognition algorithm based on two-layer decision tree recognizer is proposed. The energy consumption and algorithm complexity of the system are reduced by reducing the extraction and processing of acceleration data. In this study, Kalman filter is also used to filter the data and a semi-overlapping fixed window of 2 s in length is used in this study. The port carries on the window processing to the data. To improve the system recognition rate and reduce the complexity of the system algorithm .3. using the MPU6050 sensor module and Bluetooth communication module to collect 20 experimenters in different states of eight kinds of motion data for training. And verify the accuracy of the recognizer. The results of the verification with WEKA software show. The accuracy of the two-layer decision tree recognizer is 98.44% and 94.16, respectively, and the other classifiers are compared. The performance of each aspect is better. 4. A set of integrated fall detection and alarm system for the elderly is designed and implemented. The specific fall detection algorithm is based on a two-layer decision tree recognizer. The accurate recognition rate of the fall detection system is 95%, which has certain market value.
【學(xué)位授予單位】：重慶郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP212.9

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本文編號：1359131