本文選題：智能下肢假肢 + 神經網絡�。� 參考：《長安大學》2015年碩士論文

【摘要】：智能下肢假肢的研究目的在于改善和提高殘疾人的生活質量,促進我國醫(yī)療福利事業(yè)的發(fā)展以及社會的和諧穩(wěn)定。目前,國內外都已經出現了智能化和仿生程度很高的智能下肢假肢產品,但是價格普遍偏高,難以普及到廣大的殘疾人群中去。因此,加大力度探索和研發(fā)高性能、低成本的智能下肢假肢產品,對于改善我國殘疾人的日常生活有著很大的意義。智能下肢假肢的研究必須以下肢運動模式識別以及下肢運動姿態(tài)的準確預測為前提。本文主要是在實現下肢運動信息采集的基礎上,探索和研究神經網絡算法在智能下肢假肢研究領域的可推廣性,實現基于神經網絡算法的人體下肢運動模式識別以及下肢運動姿態(tài)的準確預測,具體所做的研究工作有以下幾點:(1)本文為了更好地實現人體下肢運動模式識別以及運動姿態(tài)預測,詳細分析了人體下肢運動的狀態(tài)及特征參數,搭建了人體下肢運動的膝關節(jié)角度獲取系統(tǒng),并利用角度均值比的方法,對膝關節(jié)角度信號進行了簡單的歸一化處理,將膝關節(jié)角度信號轉化為膝關節(jié)角度特征值。(2)在多運動狀態(tài)的模式識別上,本文選用了比較成熟的BP神經網絡算法及其兩種改進算法還有自組織競爭神經網絡,一共四種網絡分別建立了多運動狀態(tài)的模式識別模型,并分別對模型進行訓練和仿真,最后比較識別結果,發(fā)現自組織競爭神經網絡建立的模式識別模型識別準確率更好,速度更快,模型訓練更加穩(wěn)定。(3)引入另外一種神經網絡算法RBF神經網絡,分別利用基于L_M反傳算法的BP神經網絡以及RBF神經網絡建立人體下肢運動姿態(tài)預測模型,實現對于人體下肢運動姿態(tài)的預測。比較兩種模型的仿真結果,發(fā)現RBF神經網絡建立的人體下肢運動姿態(tài)預測模型預測精確,與實際的運動趨勢幾乎吻合,適用于人體下肢的運動姿態(tài)預測。
[Abstract]:The purpose of intelligent limb prosthesis research is to improve and improve the quality of life of the disabled, promote the development of medical welfare and social harmony and stability in China. At present, intelligent limb prosthesis with intelligent and bionic degree has appeared at home and abroad, but the price is generally high, it is difficult to popularize to the majority of the disabled people. Therefore, strengthening the exploration and development of high performance and low cost intelligent limb prosthesis is of great significance for improving the daily life of the disabled in our country. The research of intelligent limb prosthesis must be based on the recognition of the following limb movement pattern and the accurate pretest of the lower limb movement posture. This article is mainly to realize the lower limb movement. On the basis of information collection, the paper explores and studies the generalization of neural network algorithm in the field of intelligent limb prosthesis research, and realizes the recognition of human body movement pattern recognition based on neural network algorithm and the accurate prediction of lower limb movement posture. The specific research work has the following points: (1) this paper is to better realize the lower limb movement of the human body. Dynamic pattern recognition and motion attitude prediction, the state and characteristic parameters of human body movement are analyzed in detail. A knee joint angle acquisition system of lower limb movement is built. The angle signal of knee joint is simplified and normalized by means of angle mean ratio, and the angle signal of knee joint is transformed into knee joint angle special. (2) in the pattern recognition of multi motion state, this paper selects a mature BP neural network algorithm and its two improved algorithms and self-organizing competitive neural network. A total of four kinds of network model recognition model of multi motion state are established respectively, and the model is trained and simulated respectively. Finally, the recognition results are compared and found from the model. The pattern recognition model established by organization competitive neural network has better recognition accuracy, faster speed and more stable model training. (3) introducing another neural network algorithm RBF neural network, using the BP neural network based on the L_M back propagation algorithm and the RBF neural network to establish the human body movement attitude prediction model of the human body, to realize the human body. The prediction of the motion posture of the lower extremities is compared with the simulation results of the two models. It is found that the prediction model of the motion posture of the lower extremities established by the RBF neural network is accurate and almost coincides with the actual movement trend, which is suitable for the motion posture prediction of the lower limbs of the human body.
【學位授予單位】：長安大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：R496;TP183

【參考文獻】

相關博士學位論文 前1條

1 耿艷利;下肢運動模式識別及動力型假肢膝關節(jié)控制方法研究[D];河北工業(yè)大學;2012年

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本文編號：2063350