本文關(guān)鍵詞：基于Kinect下肢康復(fù)訓(xùn)練機(jī)器人人體步態(tài)分析　出處：《沈陽工業(yè)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 康復(fù)機(jī)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(jù)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(dá)到總?cè)丝诒壤?6.15%;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(yán)重的老齡化時期。而老年人的人體機(jī)能逐漸下降,會導(dǎo)致下肢不協(xié)調(diào)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(shù)量也不斷增加。由于人口年齡老化加劇和機(jī)械損傷也逐年上升,這些傷殘人的生活質(zhì)量下降,同時使其社會和國家經(jīng)濟(jì)方面加重負(fù)擔(dān),抑制了國家經(jīng)濟(jì)快速發(fā)展。為了提高下肢有障礙的人群的身體機(jī)能,需要對他們進(jìn)行合適的訓(xùn)練。在訓(xùn)練時,獲得下肢的步態(tài)信息進(jìn)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(fù)訓(xùn)練機(jī)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(biāo)記點(diǎn)的信息,這些信息數(shù)據(jù)主要是Kinect水平面到各個標(biāo)記點(diǎn)的水平距離和各個標(biāo)記點(diǎn)離地面的垂直高度,并通過這些數(shù)據(jù)計算出人體下肢膝關(guān)節(jié)角度。然后通過對膝關(guān)節(jié)角度和進(jìn)行時間序列的建模并結(jié)合卡爾曼濾波進(jìn)行預(yù)測和估計,同時,通過人體模擬各種步態(tài)的實(shí)驗(yàn)進(jìn)行了驗(yàn)證這種方法的合理性和有效性。接著根據(jù)得到各類步態(tài)實(shí)際值和預(yù)測值進(jìn)行差值,運(yùn)用滑動平均的方法來步態(tài)識別,并詳細(xì)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(shí)驗(yàn)進(jìn)行識別。識別時只需將計算得到的步態(tài)序列的預(yù)測值與估計值之差進(jìn)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(nèi)說明步態(tài)處于正常的,反之,就是異常步態(tài),當(dāng)異常步態(tài)出現(xiàn)時刻同時就會開啟報警信號,等待醫(yī)護(hù)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(xué)位授予單位】：沈陽工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP242

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