Topic: 人體受外力牽拉後引發之姿勢反應_肌肉活化情形_finale
(Horak et al., 1992)
前言：
當人體在站立姿勢下接受一外在的平衡干擾時，下肢與軀幹肌肉的活化程度可以反映人體受到外在干擾後引發之姿勢反應。而當人體在站姿下受到外力干擾而產生前向的身體擺盪時，背側的肌肉主要負責活化以維持或回復平衡，避免跌倒發生。過去文獻指出，當站立於一塊會產生水平後向移動之力板上時，隨著干擾強度增加(力板移動速度加快或移動位移增加)，受試者肌肉活化程度亦會隨之增加。但，由腳底板所接受之干擾與從腰間承受之外在干擾對於受試者的姿勢干擾程度與形式是有所不同的，因此，當受試者由腰間接受干擾後，其肌肉活化情形是否亦會隨著干擾強度增加而增加則值得探討。
實驗目的：
探討人體在站姿下從腰部位置受到之前向牽拉干擾時背側肌肉之活化情形，且比較在不同牽拉強度干擾下，人體反應是否會隨之變化。
實驗流程：
在受試者肚臍位置穿戴一腰帶，將三牽拉軸之牽拉線固定於受試者腰帶上(如上圖)，由三牽拉軸產生之合力提供為干擾受試者站立平衡之牽拉力量。受試者在受到牽拉之後必須在不跨步的前提下盡可能地維持平衡不跌倒。牽拉強度共有4種 (固定牽拉速度為9cm/s，牽拉位移則依序增加：3,5,7,9cm)，每個牽拉強度情境各進行3次測試。以肌電圖所量測到的肌肉活化情形作為受試者受牽拉後的姿勢反應依據，受測肌肉包括：腓腸肌 (MG)、膕旁肌(HAM)、臀中肌(GM)，以及脊旁肌(PARA)。
| 牽拉位移 (cm) | 牽拉速度 (cm/s) | |
|---|---|---|
| V1 | 3 | 9 |
| V2 | 5 | 9 |
| V3 | 7 | 9 |
| V4 | 9 | 9 |
MATLAB程式目的：
由既訂之檔案途徑擷取資料：一為原始檔案(包括時間與肌肉活化大小(以voltage表示))，一為選取後之onset時間(牽拉開始以及各肌肉開始活化時間)。
功能一(mode 1)：
(1) 同時計算各個受試者在不同牽拉強度所引發之integral EMG大小，IEMG計算方式為"肌肉開始活化後150~500毫秒減去牽拉開始前150~500毫秒時間內之IEMG大小做標準化"(總共12次測試)。並計算各個牽拉強度下各受測肌肉IEMG之平均值與標準差(共分為四個強度)，以.xls檔案形式儲存於特定資料夾中。
(2) Missing data的處理
a. 當原始EMG資料中任一塊肌肉訊號有所遺漏時，其計算出來的IEMG數值會以NaN顯示，程式可判斷'如果同一次測試中，有任一塊肌肉IEMG數值缺乏時，則該次測試所有肌肉的IEMG數值以-99表示，且皆不列入最後的平均值與標準差之計算'。
b. 另外，程式亦可判斷'當某次測試的原始EMG資料檔案不存在時，則該次測試所有肌肉的IEMG數值以-99表示，且皆不列入最後的平均值與標準差之計算'。
(3) 計算所有受試者在各個牽拉強度下IEMG數值之平均值(group mean)，以繪出牽拉強度與肌肉反應之關係圖，並將圖形儲存於特定資料夾中。
功能二(mode 2)：畫出各個牽拉強度下EMG活化情形(各以其中一個具代表性的trial呈現)，並儲存於既定之資料夾內。
Flowchart
程式內容：
% IEMG7.m
% function: IEMG calculation (including mean and std of the value of each perturbation amplitude) for all trials of each subject
% onset data were retrived automatically
% if the file of raw data is not existed or the data of the measurments of a trial is not recorded, print all the IEMG data of that trial '-99'
% the IEMG results were output and saved as .xls
% print out figures of the selected trials and saved as .jpg
% raw_data: 1_Time(ms) 2_Fy 3_Mx 4_MGr 5_HAMr 6_PARAr 7_GMr 8_Pullonset
% range from (600ms prior to the pull onset) to (5000ms after the pull onset)
% baseline EMG:time interval between (500ms prior to the pull onset) to (150ms priot to the pull onset)
% IEMG:time interval between (150ms after the burst onset) to (500ms after the burst onset)
fclose('all');
clear all;
clc;
global EMGdata_path;
EMGdata_path = input('Please input the folder of the EMG data: [e.g.: D:\\Physical Therapy\\Graduate School\\Thesis\\Pilot study for Thesis\\Data\\Processed data for Matlab]','s');
if isempty(EMGdata_path)
EMGdata_path = 'D:\Physical Therapy\Graduate School\Thesis\Pilot study for Thesis\Data\Processed data for Matlab';
end
cd(EMGdata_path);
Subject_information = textread('Subject_information.txt','%s');
Subject_amount = size(Subject_information,1);
EMGfig_information = textread('EMGfig_information.txt','%s');
EMGfig_amount = size(EMGfig_information,1);
EMGoutput_path = ['D:\Physical Therapy\Graduate School\Thesis\Pilot study for Thesis\Results\Data output from Matlab'];
if ~exist(EMGoutput_path,'dir')
mkdir('D:\Physical Therapy\Graduate School\Thesis\Pilot study for Thesis\Results\Data output from Matlab');
end
mod_choice = input('Please Choose the Mode: [1] Integrated EMG Analysis and Correlation Figure Print-out. [2] EMG Figure Print-out. [e.g. 1 or 2]');
switch mod_choice
case 1
for i = 1:Subject_amount
subjectoutput_folder = ['D:\Physical Therapy\Graduate School\Thesis\Pilot study for Thesis\Results\Data output from Matlab\Results for IEMG calculation'];
if ~exist(subjectoutput_folder,'dir')
mkdir('D:\Physical Therapy\Graduate School\Thesis\Pilot study for Thesis\Results\Data output from Matlab\Results for IEMG calculation');
end
Onsetdata_xls = xlsread([EMGdata_path,'\',Subject_information{i}(1:4),'\','Time events for EMG and pullonset signials.xls'],'B3:F14'); % [pullonset,MGronset,HAMronset,PARAronset,GMronset]
data_name=0;
Onsetdata=0;
IEMG=0;
fprintf('\n %s\t\t rMG (V*ms)\t rHAM (V*ms)\t rPARA (V*ms)\t rGM (V*ms)\n',Subject_information{i}(1:4))
IEMG_fid = fopen([subjectoutput_folder,'\',Subject_information{i},'Integrated EMG','.xls'],'w');
fprintf(IEMG_fid,' \t rMG (V*ms)\t rHAM (V*ms)\t rPARA (V*ms)\t rGM (V*ms)\n');
for j = 1:4 % 4 perturbation amplitudes
for k = 1:3 % 3 trials
data_name = [Subject_information{i}(1:4),'C',num2str(1),'V',num2str(j),'R',num2str(k)];
Onsetdata = Onsetdata_xls((j-1)*3+k,:);
rawdata_file = [EMGdata_path,'\',Subject_information{i}(1:4),'\',data_name,'.xls'];
if exist(rawdata_file)
raw_data = xlsread([EMGdata_path,'\',Subject_information{i}(1:4),'\',data_name,'.xls'],'C19:J5618');
baseline = mean(raw_data(101:451,4:7));
rawIEMG(1) = (1/2*(raw_data((Onsetdata(2)-Onsetdata(1)+749),4)+raw_data((Onsetdata(2)-Onsetdata(1)+1099),4))+sum(raw_data((Onsetdata(2)-Onsetdata(1)+750):(Onsetdata(2)-Onsetdata(1)+1098),4))); % unit:V*ms
rawIEMG(2) = (1/2*(raw_data((Onsetdata(3)-Onsetdata(1)+749),5)+raw_data((Onsetdata(3)-Onsetdata(1)+1099),5))+sum(raw_data((Onsetdata(3)-Onsetdata(1)+750):(Onsetdata(3)-Onsetdata(1)+1098),5))); % unit:V*ms
rawIEMG(3) = (1/2*(raw_data((Onsetdata(4)-Onsetdata(1)+749),6)+raw_data((Onsetdata(4)-Onsetdata(1)+1099),6))+sum(raw_data((Onsetdata(4)-Onsetdata(1)+750):(Onsetdata(4)-Onsetdata(1)+1098),6))); % unit:V*ms
rawIEMG(4) = (1/2*(raw_data((Onsetdata(5)-Onsetdata(1)+749),7)+raw_data((Onsetdata(5)-Onsetdata(1)+1099),7))+sum(raw_data((Onsetdata(5)-Onsetdata(1)+750):(Onsetdata(5)-Onsetdata(1)+1098),7))); % unit:V*ms
IEMG = rawIEMG-baseline; % [rMG rHAM rPARA rGM]
if ~isempty(find(isnan(IEMG)))
IEMG(1,1:4) = -99;
end
else
IEMG(1,1:4) = -99;
end
IEMG_command = ['IEMG_cell(',num2str(k),',:) ={IEMG};'];
eval(IEMG_command)
fprintf(' %s\t %f\t %f\t %f\t %f\n',data_name,IEMG)
fprintf(IEMG_fid,' %s\t %.3f\t %.3f\t %.3f\t %.3f\n',data_name,IEMG);
end % for k loop
IEMG_array = reshape([IEMG_cell{:}],4,3)';
for l = 1:4
if ~isempty((find(IEMG_array(:,l) ~= -99)))
mean_IEMG(1,l) = mean(IEMG_array(find(IEMG_array(:,l) ~= -99),l));
std_IEMG(1,l) = std(IEMG_array(find(IEMG_array(:,l) ~= -99),l));
else
mean_IEMG(1,1:4) = -99;
std_IEMG(1,1:4) = -99;
end
end
fprintf(IEMG_fid,' \n');
fprintf(IEMG_fid,' %s\t %.3f\t %.3f\t %.3f\t %.3f\n','Mean',mean_IEMG);
fprintf(IEMG_fid,' %s\t %.3f\t %.3f\t %.3f\t %.3f\n','Std',std_IEMG);
fprintf(IEMG_fid,' \n');
IEMGmean_command = ['IEMGmean_cell(',num2str(j),',:) ={mean_IEMG};'];
eval(IEMGmean_command)
end % for j loop
IEMGmean_array(:,:,i) = reshape([IEMGmean_cell{:}],4,4)';
for q = 1:4 % 4 perturbation amplitudes
sumEMG(q) = sum(IEMGmean_array(q,1,1:i));
GroupMean_cell{1} = sumEMG./Subject_amount;
end
for q = 1:4 % 4 perturbation amplitudes
sumEMG(q) = sum(IEMGmean_array(q,2,1:i));
GroupMean_cell{2} = sumEMG./Subject_amount;
end
for q = 1:4 % 4 perturbation amplitudes
sumEMG(q) = sum(IEMGmean_array(q,3,1:i));
GroupMean_cell{3} = sumEMG./Subject_amount;
end
for q = 1:4 % 4 perturbation amplitudes
sumEMG(q) = sum(IEMGmean_array(q,4,1:i));
GroupMean_cell{4} = sumEMG./Subject_amount;
end
fclose(IEMG_fid);
end % for i loop
GroupMean = reshape([GroupMean_cell{:}],4,4);
x = 1:4;
amp = ['V1';'V2';'V3';'V4'];
cor_IEMG = plot(x,GroupMean,'linewidth',2);xlabel('Perturbation cnodition');ylabel('Mean IEMG (V*ms)');title('Correaltion between muscle responses and perturbation amplitude');legend('MG','HAM','PARA','GM');box off;
set(gca,'xtick',1:4,'xticklabel',amp);
saveas(gcf,'D:\Physical Therapy\Graduate School\Thesis\Pilot study for Thesis\Results\Data output from Matlab\Results for IEMG calculation\IEMGScalingFig','jpg')
case 2
for m = 1:EMGfig_amount
Onsetdata_xls = xlsread([EMGdata_path,'\',EMGfig_information{m}(1:4),'\','Time events for EMG and pullonset signials.xls'],'B3:F14'); % [pullonset,MGronset,HAMronset,PARAronset,GMronset]
data_name = [EMGfig_information{m}(1:10)];
Onsetdata = Onsetdata_xls((str2num(EMGfig_information{m}(8))-1)*3+str2num(EMGfig_information{m}(10)),:);
raw_data = xlsread([EMGdata_path,'\',EMGfig_information{m}(1:4),'\',data_name,'.xls'],'C19:J5618');
muscle_name = {'MG (V)' 'HAM (V)' 'PARA (V)' 'GM (V)'};
linecolor = ['b' 'r' 'g' 'm'];
for n=1:4
subplot(4,1,n),fig_EMG = plot((raw_data(:,1)),(raw_data(:,n+3)),linecolor(n));xlabel('Time (ms)');box off;hold on;onsetline=plot(Onsetdata(1)*ones(100,1),0:max(raw_data(:,n+3))/99:max(raw_data(:,n+3)),'k');text(Onsetdata(1),max(raw_data(:,n+3)),'pull onset');
ylabel_command = ['ylabel(''',muscle_name{n},''')'];
eval(ylabel_command)
end % for n loop
subjectoutput_folder = ['D:\Physical Therapy\Graduate School\Thesis\Pilot study for Thesis\Results\Data output from Matlab\EMG figures'];
if ~exist(subjectoutput_folder,'dir')
mkdir('D:\Physical Therapy\Graduate School\Thesis\Pilot study for Thesis\Results\Data output from Matlab\EMG figures');
end
IEMG_fig_command = ['saveas(gcf,''D:\Physical Therapy\Graduate School\Thesis\Pilot study for Thesis\Results\Data output from Matlab\EMG figures\',data_name,'_fig'',','''jpg'')'];
eval(IEMG_fig_command)
clf;
fprintf('figure print-out: %s\n',data_name)
end % for m loop
end
raw_path = 'C:\MATLAB701\work';
cd(raw_path);
執行結果：
IEMG數值 (以He01為例)
rMG (V*ms) rHAM (V*ms) rPARA (V*ms) rGM (V*ms)
He01C1V1R1 2.356 2.288 1.054 5.551
He01C1V1R2 1.641 2.368 0.907 5.878
He01C1V1R3 2.089 2.423 0.927 5.477
Mean 2.029 2.36 0.963 5.636
Std 0.361 0.068 0.08 0.213
He01C1V2R1 2.072 1.916 1.222 5.586
He01C1V2R2 1.198 1.862 1.34 5.578
He01C1V2R3 2.608 1.998 1.614 5.718
Mean 1.959 1.926 1.392 5.627
Std 0.712 0.068 0.201 0.078
He01C1V3R1 2.816 2.233 0.766 5.599
He01C1V3R2 1.951 1.883 3.109 5.227
He01C1V3R3 2.678 2.139 1.792 5.382
Mean 2.482 2.085 1.889 5.403
Std 0.465 0.181 1.175 0.187
He01C1V4R1 2.252 2.067 2.343 5.017
He01C1V4R2 1.52 2.334 0.745 2.722
He01C1V4R3 1.588 1.777 0.967 4.878
Mean 1.787 2.059 1.352 4.206
Std 0.404 0.279 0.866 1.287
牽拉強度與肌肉反應之關係圖
EMG圖形
