發(fā)布時間：2017-12-28 02:24

  本文關鍵詞：基于Kinect下肢康復訓練機器人人體步態(tài)分析　出處：《沈陽工業(yè)大學》2017年碩士論文　論文類型：學位論文

  更多相關文章： 康復機器人 步態(tài)序列 時間序列模型 卡爾曼濾波 步態(tài)識別

【摘要】：根據統(tǒng)計來看,在2015年就有2.22億的人口是60歲乃至于60歲以上,達到總人口比例的16.15%;估計到了2020年,將有2.48億是老年的人口,人口老齡化的水平也將到17.17%,這里有3067萬人是年齡較大的80歲以上人口;直到2025年,將突破3億人是60歲以上的,這將是國家面臨著非常嚴重的老齡化時期。而老年人的人體機能逐漸下降,會導致下肢不協(xié)調而跌倒。除外,因疾病、交通、工傷事故等原因造成的下肢病殘人員數量也不斷增加。由于人口年齡老化加劇和機械損傷也逐年上升,這些傷殘人的生活質量下降,同時使其社會和國家經濟方面加重負擔,抑制了國家經濟快速發(fā)展。為了提高下肢有障礙的人群的身體機能,需要對他們進行合適的訓練。在訓練時,獲得下肢的步態(tài)信息進行分析,這有助于減小意外發(fā)生的概率。本課題就基于Kinect傳感器搭建一個康復訓練機器人,通過Kinect來檢測獲取人體下肢的步態(tài)信息,這里主要是獲取人體下肢所貼有八個白色標記點的信息,這些信息數據主要是Kinect水平面到各個標記點的水平距離和各個標記點離地面的垂直高度,并通過這些數據計算出人體下肢膝關節(jié)角度。然后通過對膝關節(jié)角度和進行時間序列的建模并結合卡爾曼濾波進行預測和估計,同時,通過人體模擬各種步態(tài)的實驗進行了驗證這種方法的合理性和有效性。接著根據得到各類步態(tài)實際值和預測值進行差值,運用滑動平均的方法來步態(tài)識別,并詳細而深入的研究滑動平均法識別步態(tài)影響情況,最后用滑動平均法對模擬的具有對稱性和非對稱性的正常步態(tài)的實驗進行識別。識別時只需將計算得到的步態(tài)序列的預測值與估計值之差進行滑動平均看得到偏差均值的波動范圍來判別,在波動允許的范圍內說明步態(tài)處于正常的,反之,就是異常步態(tài),當異常步態(tài)出現時刻同時就會開啟報警信號,等待醫(yī)護人員救援。
[Abstract]:According to statistics, in 2015 222 million of the population is 60 years old and over the age of 60, the total population reached 16.15%; estimated that by 2020, there will be 248 million elderly population, the aging of the population level will be 17.17%, there are 30 million 670 thousand people who are older people over the age of 80; until in 2025, 300 million people over the age of 60 will be a breakthrough, this will be the country facing the aging period is very serious. In the elderly, the function of the human body gradually decreases, which leads to the fall of the lower extremities. In addition, the number of disabled persons in the lower extremities caused by diseases, traffic, industrial injuries and other causes is also increasing. Due to the aging of population and the increase of mechanical damage, the quality of life of these disabled people has been reduced, and their social and national economic burden has been aggravated, which has suppressed the rapid development of the national economy. In order to improve the body function of the lower extremities, it is necessary to train them properly. In training, the gait information of the lower extremities is analyzed, which helps to reduce the probability of accident. This topic on the Kinect sensor to build a rehabilitation training robot based on the detection of lower limb gait information acquisition by Kinect, here is the main access to the lower limb of the human body with eight white marker information, these data are mainly Kinect level to each marked point horizontal distance and vertical height of each marker from the ground, and through these data to calculate the human knee joint angle. Then we predict and estimate the knee angle and time series and combine with Calman filter, and verify the rationality and effectiveness of this method by simulating various gait experiments of human body. Then according to the various gait actual value and predictive value of the difference, using moving average method to gait recognition, gait recognition research of moving average method and effect of detailed and in-depth, finally the simulation of normal gait symmetry and non symmetry by moving average method of experimental identification. Only when the prediction of gait recognition sequence calculated the value and estimated value of the difference of sliding average deviation of the mean fluctuation range to determine, in a normal gait, and that in the range permitted, is abnormal gait, when abnormal gait time at the same time opens the alarm signal, waiting for rescue medical personnel.
【學位授予單位】：沈陽工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP242

【參考文獻】

相關期刊論文 前10條

1 李軍強;王娟;趙海文;劉今越;;下肢康復訓練機器人關鍵技術分析[J];機械設計與制造;2013年09期

2 楊俊友;李宇慶;白殿春;;下肢康復機器人機械結構設計及動力學仿真[J];沈陽工業(yè)大學學報;2010年05期

3 張金明;高秋菊;高宇辰;;肢體殘疾人康復需求調查[J];中國康復;2010年02期

4 劉蓉;黃璐;李少偉;劉毅;;基于步態(tài)加速度的步態(tài)分析研究[J];傳感技術學報;2009年06期

5 張立勛;王令軍;王鳳良;王克寬;;一種人體步態(tài)軌跡測量方法[J];測控技術;2009年02期

6 侯向鋒;劉蓉;周兆豐;;加速度傳感器MMA7260在步態(tài)特征提取中的應用[J];傳感技術學報;2007年03期

7 薛召軍;李佳;明東;萬柏坤;;基于支持向量機的步態(tài)識別新方法[J];天津大學學報;2007年01期

8 田光見,趙榮椿;步態(tài)識別綜述[J];計算機應用研究;2005年05期

9 王亮,胡衛(wèi)明,譚鐵牛;基于步態(tài)的身份識別[J];計算機學報;2003年03期

10 楊衍明,林方,袁波,李政;超聲定位人體下肢步態(tài)分析儀[J];中國生物醫(yī)學工程學報;1997年04期

相關博士學位論文 前1條

1 柴艷妹;基于步態(tài)特征的身份識別技術研究[D];西北工業(yè)大學;2007年

，

本文編號：1344206