【摘要】：工作強度增加、工作時間延長、精神壓力增大等諸多因素都會導(dǎo)致出現(xiàn)疲勞感。在疲勞狀態(tài)下工作效率低,易引發(fā)安全事故,會給身體帶來多種疾病。對人體疲勞狀態(tài)進行實時檢測越來越受到學(xué)術(shù)界的關(guān)注,在體育訓(xùn)練、運動健身、醫(yī)療康復(fù)等領(lǐng)域的實際應(yīng)用價值更為突出。本課題通過對人的步態(tài)加速度信號進行采集和處理分析,從以下兩個方面對人處于疲勞狀態(tài)下行走時步態(tài)的改變展開了研究。 課題研究初期,通過觀察人體前后方向的步態(tài)加速度信號波形圖,發(fā)現(xiàn)疲勞前后存在明顯的不同,因此根據(jù)信號在時域上的變化,用相關(guān)系數(shù)法求出閾值進行疲勞判斷。對9個測試樣本進行實驗驗證,檢測結(jié)果的準確率為93.06%。該方法是針對同一個個體進行的疲勞檢測,檢測前需要先得到正常狀態(tài)下的步態(tài)加速度信號,然后和測得的信號進行比較判斷。 為了能夠從步態(tài)加速度信號中挖掘更多的步態(tài)特征參數(shù)進行疲勞檢測進行了更為深入的研究。通過對步態(tài)加速度信號進行了數(shù)學(xué)建模,設(shè)計相應(yīng)算法得到各類步態(tài)特征參數(shù),包括步態(tài)周期、步調(diào)、步態(tài)加速度均方根、自相關(guān)系數(shù)、峰峰值、FFT等。分別計算出疲勞和非疲勞狀態(tài)下各類步態(tài)參數(shù)的均值和標準差,然后進行配對T檢驗,進而分析人體疲勞前后步態(tài)在時域和頻域范圍內(nèi)的變化。研究結(jié)果表明,人體疲勞以后三個方向的步態(tài)加速度信號穩(wěn)定性都會變差,垂直方向上幅值、頻域范圍的變化非常明顯。今后可以使用這些步態(tài)特征參數(shù)從多角度、多層次進行疲勞檢測。 總之,以上研究均說明了步態(tài)加速度特征可以被用來進行疲勞檢測。前期研究是一種具體疲勞檢測方法,而后期研究則更加深入的發(fā)掘出更多的可被用來進行疲勞檢測步態(tài)特征參數(shù),對今后疲勞檢測的實際應(yīng)用具有一定的指導(dǎo)意義。
[Abstract]:Fatigue will occur due to increased work intensity, prolonged working hours, increased mental stress and so on. Under fatigue condition, work efficiency is low, easy to cause safety accident, will bring many kinds of diseases to the body. The real-time detection of human fatigue status has attracted more and more attention from academic circles, and the practical application value in sports training, sports fitness, medical rehabilitation and other fields is more prominent. By collecting and processing the gait acceleration signal, the change of gait is studied from the following two aspects. At the beginning of the study, by observing the waveform of gait acceleration signal in front and rear direction of human body, it is found that there are obvious differences before and after fatigue. Therefore, according to the change of signal in time domain, the threshold value is calculated by correlation coefficient method to judge fatigue. Nine test samples were tested and the accuracy of the test results was 93.06. This method is aimed at fatigue detection of the same individual. The gait acceleration signal in normal state should be obtained before detection, and then compared with the measured signal. In order to extract more gait characteristic parameters from gait acceleration signal for fatigue detection, more in-depth research has been done. Based on the mathematical modeling of gait acceleration signal, the corresponding algorithm is designed to obtain various gait characteristic parameters, including gait period, gait pace, gait acceleration root mean square, autocorrelation coefficient, peak and peak FFT, etc. The mean and standard deviation of gait parameters in fatigue and non-fatigue state were calculated, and then matched T test was carried out to analyze the changes of gait in time domain and frequency domain before and after fatigue. The results show that the stability of gait acceleration signals in the three directions after fatigue becomes worse and the amplitude and frequency range in the vertical direction are very obvious. These gait characteristic parameters can be used to detect fatigue from multi-angle and multi-level in the future. In a word, all the above studies show that gait acceleration characteristics can be used for fatigue detection. The previous research is a specific fatigue detection method, while the later research is more in-depth to find out more characteristic parameters can be used for fatigue detection of gait, which has a certain guiding significance for the practical application of fatigue detection in the future.
【學(xué)位授予單位】：山西大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：R318.0;TN911.7

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