sEMG時頻特征線性回歸法與非線性神經(jīng)網(wǎng)絡(luò)法預(yù)測伸膝肌群極限功率保持能力測試中功率損失率的比較研究
發(fā)布時間：2019-08-02 15:02
【摘要】：目的:擬比較s EMG時頻特征線性回歸法與非線性神經(jīng)網(wǎng)絡(luò)法預(yù)測伸膝肌群極限功率保持能力測試中功率損失率的差異。方法:BTE Primus~(RS)系統(tǒng)與肌電儀同步,40名男大學(xué)生膝關(guān)節(jié)重復(fù)性屈伸運(yùn)動至疲勞,阻力設(shè)置50%等長峰值力矩,動作頻率60次/min。求取每次伸膝階段極限功率損失率(Power%),伸膝肌群sEMG時域(MAV%、RMS%)、頻域(MNF%、MDF%)與瞬時頻率(IMNF%、IMDF%)參數(shù)變化率,基于s EMG時頻特征參數(shù)(MAV、ZC、SSC、WL)建立多層感知人工神經(jīng)網(wǎng)絡(luò)模型,求取功率真實值與估計值。結(jié)果:IMDF%能單獨解釋股內(nèi)肌、股直肌與股外肌極限功率損失率的方差變異為6.33%、22.71%、12.31%,IMDF%聯(lián)合其他時頻參數(shù)一起能解釋的方差變異為6.95%、25.93%和16.05%,非線性神經(jīng)網(wǎng)絡(luò)法求取的功率估計值能解釋的方差變異為10.43%、34.23%和18.05%,且信噪比值逐步增大。線性與非線性技術(shù)功率真實值與估計值擬合所得兩直線的斜率與截距有顯著性差異(P0.05)。結(jié)論:s EMG時頻特征線性回歸法與非線性神經(jīng)網(wǎng)絡(luò)法,均能很好地追蹤人體神經(jīng)肌肉系統(tǒng)動態(tài)工作疲勞過程中輸出功率的損失,但后者的準(zhǔn)確性要優(yōu)于前者。
[Abstract]:Aim: to compare the difference of power loss rate between s EMG time-frequency characteristic linear regression method and nonlinear neural network method in predicting the ultimate power retention ability of knee extensor muscle group. Methods: BTE Primus~ (RS) system synchronized with EMG. 40 male college students had repetitive flexion and extension movements to fatigue. The resistance was set at 50% equal length peak torque, and the action frequency was 60 times / min.. The limit power loss rate (Power%), sEMG time domain (MAV%,RMS%), frequency domain (MNF%,MDF%) and instantaneous frequency (IMNF%,IMDF%) parameters of each knee extension stage were obtained. A multi-layer perceptual artificial neural network model was established based on sEMG time-frequency characteristic parameters (MAV,ZC,SSC,WL), and the real and estimated values of power were obtained. Results: the variance variation of limit power loss rate of medial muscle, rectus femoris and extrathigh muscle was 6.33%, 22.71% and 12.31%, respectively. The variance variation explained by IMDF% combined with other time-frequency parameters was 6.95%, 25.93% and 16.05%. The variance variation of power estimation obtained by nonlinear neural network method was 10.43%, 34.23% and 18.05%, respectively. the variance variation of IMDF% combined with other time-frequency parameters was 6.95%, 25.93% and 16.05%, and the variance variation of IMDF% combined with other time-frequency parameters was 6.95%, 25.93% and 16.05%. The signal-to-noise ratio (SNR) increases gradually. The slope and intercept of the two lines fitted with the real and estimated power of linear and nonlinear techniques are significantly different (P 0.05). Conclusion both: s EMG time-frequency characteristic linear regression method and nonlinear neural network method can track the loss of output power in the dynamic fatigue process of human neuromuscular system, but the accuracy of the latter is better than that of the former.
【作者單位】： 東北師范大學(xué);長春光華學(xué)院商學(xué)院;北京航空航天大學(xué)生物醫(yī)學(xué)工程學(xué)院;中國標(biāo)準(zhǔn)化研究院;
【基金】：科技基礎(chǔ)性工作專項(2013FY110200) 中央高�；究蒲袠I(yè)務(wù)費資助項目(14QNJJ032)
【分類號】：R318.01
本文編號：2522189
[Abstract]:Aim: to compare the difference of power loss rate between s EMG time-frequency characteristic linear regression method and nonlinear neural network method in predicting the ultimate power retention ability of knee extensor muscle group. Methods: BTE Primus~ (RS) system synchronized with EMG. 40 male college students had repetitive flexion and extension movements to fatigue. The resistance was set at 50% equal length peak torque, and the action frequency was 60 times / min.. The limit power loss rate (Power%), sEMG time domain (MAV%,RMS%), frequency domain (MNF%,MDF%) and instantaneous frequency (IMNF%,IMDF%) parameters of each knee extension stage were obtained. A multi-layer perceptual artificial neural network model was established based on sEMG time-frequency characteristic parameters (MAV,ZC,SSC,WL), and the real and estimated values of power were obtained. Results: the variance variation of limit power loss rate of medial muscle, rectus femoris and extrathigh muscle was 6.33%, 22.71% and 12.31%, respectively. The variance variation explained by IMDF% combined with other time-frequency parameters was 6.95%, 25.93% and 16.05%. The variance variation of power estimation obtained by nonlinear neural network method was 10.43%, 34.23% and 18.05%, respectively. the variance variation of IMDF% combined with other time-frequency parameters was 6.95%, 25.93% and 16.05%, and the variance variation of IMDF% combined with other time-frequency parameters was 6.95%, 25.93% and 16.05%. The signal-to-noise ratio (SNR) increases gradually. The slope and intercept of the two lines fitted with the real and estimated power of linear and nonlinear techniques are significantly different (P 0.05). Conclusion both: s EMG time-frequency characteristic linear regression method and nonlinear neural network method can track the loss of output power in the dynamic fatigue process of human neuromuscular system, but the accuracy of the latter is better than that of the former.
【作者單位】： 東北師范大學(xué);長春光華學(xué)院商學(xué)院;北京航空航天大學(xué)生物醫(yī)學(xué)工程學(xué)院;中國標(biāo)準(zhǔn)化研究院;
【基金】：科技基礎(chǔ)性工作專項(2013FY110200) 中央高�；究蒲袠I(yè)務(wù)費資助項目(14QNJJ032)
【分類號】：R318.01
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