【摘要】：人體步態(tài)識別技術(shù)是指利用運(yùn)動學(xué)、信號處理、模式識別等理論去分析處理傳感設(shè)備獲得人體運(yùn)動學(xué)、動力學(xué)和生理學(xué)等步態(tài)信號的技術(shù)。它在仿人機(jī)器人、人機(jī)耦合機(jī)器人(外骨骼和假肢等)、醫(yī)學(xué)診斷和康復(fù)治療、運(yùn)動分析、身份識別等領(lǐng)域得到了廣泛的應(yīng)用。步態(tài)信號具有準(zhǔn)周期性,步態(tài)階段之間的轉(zhuǎn)換可以看做一條馬爾科夫鏈。這種性質(zhì)使得隱馬爾科夫模型(Hidden Markov Model,HMM)在步態(tài)階段識別中得到了廣泛的應(yīng)用。但是,隱馬爾科夫模型應(yīng)用在步態(tài)階段識別中存在兩個不足。一是模型基于統(tǒng)計特征構(gòu)造駐留時間分布函數(shù),不能很好地描述步態(tài)階段的時間特性;二是模型參數(shù)固定,未針對具體使用場景進(jìn)行自適應(yīng)處理。這些不足限制了步態(tài)階段識別的效果。本文使用大腿上的加速度信號進(jìn)行步態(tài)階段識別。通過對傳統(tǒng)隱馬爾科夫模型的改進(jìn)和優(yōu)化,提高了模型對步態(tài)階段識別的準(zhǔn)確性和對步態(tài)數(shù)據(jù)的適應(yīng)性。具體工作如下:1,對采集的運(yùn)動步態(tài)數(shù)據(jù)進(jìn)行了預(yù)處理和特征提取。預(yù)處理主要包括去噪和平滑。特征提取主要包括步態(tài)窗口劃分和按窗口提取特征。2,討論了隱馬爾科夫模型原理。分析了其應(yīng)用于步態(tài)階段識別的不足,并指明了改進(jìn)方向。3,在隱馬爾科夫模型中引入時間參數(shù),用駐留時間分布函數(shù)來代替自轉(zhuǎn)移概率,使其能夠更好的描述運(yùn)動步態(tài)階段。4,針對隱馬爾科夫模型無法適應(yīng)不同的穿戴者、不同的運(yùn)動狀態(tài)、不同的運(yùn)動環(huán)境的缺陷進(jìn)行改進(jìn)。使用自適應(yīng)算法修正模型參數(shù),提高步態(tài)階段識別模型的魯棒性。5,針對自適應(yīng)過程中參考模型單一的問題進(jìn)行改進(jìn),提出將行為識別與步態(tài)階段自適應(yīng)識別結(jié)合的方法,實現(xiàn)自適應(yīng)中參考模型的多樣化和自主選擇。我們進(jìn)行了步態(tài)階段識別的對比實驗以驗證以上改進(jìn)的效果。結(jié)果表明針對傳統(tǒng)隱馬爾科夫模型的改進(jìn),能夠提升人體步態(tài)階段識別的效果,同時,其對不同人、不同運(yùn)動模式、不同運(yùn)動環(huán)境也具備一定的自適應(yīng)能力。
[Abstract]:Human gait recognition technology refers to the technology that gait signals such as kinematics, signal processing and pattern recognition are obtained by means of kinematics, signal processing, pattern recognition and so on. It has been widely used in the fields of humanoid robot, human-computer coupling robot (exoskeleton and prosthesis), medical diagnosis and rehabilitation, motion analysis, identity recognition and so on. Gait signals are quasi-periodic, and the transition between gait stages can be regarded as a Markov chain. This property makes Hidden Markov Model (Hidden Markov Model,HMM) widely used in gait recognition. However, there are two shortcomings in the application of Hidden Markov Model in gait recognition. One is that the model constructs resident time distribution function based on statistical features, which can not well describe the time characteristics of gait phase; the other is that the model parameters are fixed and the adaptive processing is not carried out for specific use scenes. These deficiencies limit the effect of gait recognition. In this paper, the acceleration signal on the thigh is used to identify the gait stage. By improving and optimizing the traditional hidden Markov model, the accuracy of the model for gait recognition and the adaptability to gait data are improved. The main work is as follows: 1. Preprocessing and feature extraction of gait data are carried out. Pretreatment mainly includes denoising and smoothing. Feature extraction mainly includes gait window partition and feature extraction by window. 2. The principle of hidden Markov model is discussed. The deficiency of its application in gait recognition is analyzed, and the improvement direction is pointed out. 3. Time parameter is introduced into Hidden Markov Model, and the resident time distribution function is used to replace the self-transfer probability. 4, the hidden Markov model can not adapt to different wearer, different motion state, different motion environment defect. 4. The hidden Markov model can not adapt to the defects of different wearer, different motion state and different motion environment. 4. The hidden Markov model can not adapt to different wearer, different motion state and different motion environment. The adaptive algorithm is used to modify the model parameters to improve the robustness of the gait stage recognition model. 5. Aiming at the single problem of reference model in the adaptive process, a new method is proposed to combine the behavior recognition with the gait stage adaptive recognition. The diversity and independent selection of reference models in adaptive system are realized. A comparative experiment of gait stage recognition was carried out to verify the improved results. The results show that the improvement of traditional hidden Markov model can improve the recognition effect of human gait, at the same time, it also has certain adaptive ability to different people, different motion modes and different moving environments.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.4;O211.62

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