認(rèn)知行為信號處理與模式識別

傅山　楊政　黃丹

摘要：本研究報(bào)告以駕駛艙設(shè)計(jì)為背景和出發(fā)點(diǎn)，通過對駕駛艙設(shè)計(jì)理念和布局規(guī)則的深入理解，結(jié)合人因?qū)W理論以及特定的飛行操控，將飛行員的肌電信號特征作為研究對象，通過信號的分解、相關(guān)性分析、選擇、組合四個(gè)過程，選擇合適的信號處理和模式識別的方法，從信息學(xué)角度揭示飛行操控中肌電信號的狀態(tài)特征，并結(jié)合生理學(xué)、人因?qū)W、飛行器設(shè)計(jì)等理論得出飛行員的操作績效，使飛機(jī)駕駛艙內(nèi)的儀器儀表、操縱駕駛桿等合理有效地放入駕駛艙且滿足飛行員的要求，形成一系列行之有效的信號處理體系，最終為駕駛艙的設(shè)計(jì)提供指導(dǎo)或參考，以及為駕駛艙適航符合性驗(yàn)證提供幫助。 研究過程通過搜集前人的肌電信號分析方法，在傳統(tǒng)的傅里葉變換，時(shí)域指標(biāo)和頻域指標(biāo)，小波變換等等方法的運(yùn)用，發(fā)現(xiàn)這些方法在靜態(tài)疲勞檢測方面有很好的結(jié)果，但是運(yùn)用在動(dòng)態(tài)疲勞檢測中效果不佳。隨著希爾伯特黃變換的提出，EMD在生物信號處理、結(jié)構(gòu)檢測等非穩(wěn)態(tài)、非線性信號上有很好的運(yùn)用。本研究比較了EMD與EEMD在肌電信號分解中的性能， 提出基于EEMD 和Hilbert 變換的動(dòng)態(tài)疲勞評價(jià)方法。實(shí)驗(yàn)證明基于平均瞬時(shí)頻率的疲勞指標(biāo)很好的表征動(dòng)態(tài)肌電信號的疲勞趨勢。 實(shí)驗(yàn)結(jié)果顯示，我們提出的動(dòng)態(tài)肌電信號疲勞特征指標(biāo)（瞬時(shí)平均頻率），可以監(jiān)測飛行員生理疲勞參數(shù)的實(shí)時(shí)狀態(tài)。并且基于現(xiàn)有的信號處理體系信號分解——相關(guān)性分析——分量篩選——分量重構(gòu)，在揭示肌電信號物理意義并將其運(yùn)用在理論研究和工程實(shí)踐中都十分適合，為解決高維、多類、大量的數(shù)據(jù)（包括生物信號、飛行數(shù)據(jù)等）的采集，并進(jìn)行信號分解后，結(jié)合相關(guān)性分析，提取出其中有意義、需要重點(diǎn)研究的分量，進(jìn)行信號重組突出研究。

關(guān)鍵詞：肌電信號；經(jīng)驗(yàn)?zāi)B(tài)分解；希爾伯特黃變換

Abstract：We take pilots EMG signal as the research object to study cockpit design and layout rules combining theory and specific flight control. This signal goes through the decomposition， correlation analysis， selection， combination， selecting the appropriate signal processing methods and pattern recognition methods to reveal the status of EMG features in the flight controls. From the perspective of information science， combined with physiology， human factors， aircraft design， the performance of the pilots operating can be got to make the aircraft cockpit reasonable and effective. In addition， we will establish a standard signal processing system， and ultimately provide guidance for the design of the cockpit. According to the collected resource， the traditional methods of EMG analysis are the fast Fourier transform， the time domain and frequency domain indexes， wavelet transform， etc. We find that these methods are good at static fatigue， but do well in dynamic fatigue. As the Hilbert Huang transform was proposed in 1998， it has been widely used in the unsteady and nonlinear signal processing， such as biological signal and structure damage detection. We compare the advantages and disadvantages of EMD and EEMD in the decomposition of EMG and propose a dynamic fatigue evaluation approach on the basis of EEMD and Hilbert transform. Experiments show that the mean instantaneous frequency as the fatigue index has a good performance in the EMG dynamic fatigue. Our research proposes the index of dynamic EMG fatigue-mean instantaneous frequency. It can monitor the pilots muscle fatigue during the flight. And a signal processing system， including decomposition， correlation analysis， selection and reconstruction， has proved to be good at the EMG signal. The signal processing system based on the existing signals can provide help for the whole team， in order to reveal the importance and physical meaning of high-dimensional， multi-class， large amounts of data （including biological signals， flight data， etc.）. After acquisition and signal decomposition， combined with correlation analysis， the system can point out which signals are meaningful， needed to focus on.

Keywords：EMG；Empirical Mode Decompostion； Hilbert-Huang Transform