clc;               %deletes content of command window
clear all;         %deletes workspace
close all;         %closes figures 

cd('C:\Users\farid\Desktop\Kinesio\MAT');

for Proband = 1:5
    for Kinesio = 1:2   %Erst ohne dann mit Kinesio einlesen

    clear Start_End;    
        
[FileName,PathName] = uigetfile('*.mat','Select the MATLAB data file');
cd(PathName);
load(FileName);

Gonio1_abs=abs(Gonio1);

%Butterworth Filter
fc = 20;                                        % Cut-off frequency (Hz)
fs = 1000;                                     % Sampling rate (Hz)
order = 2;                                     % Filter order
[B,A] = butter(order,fc/(fs/2));               % Coefficients B and A are defined using the inputs

Gonio1_abs_filt = filtfilt(B,A,Gonio1_abs);          %EMG data is filtered

%figure(1);
%plot(Gonio1_abs_filt);

%% Start Ende bestimmen
Level=0.025;                                     %Level of force where step should be detected 
i=1;                                          %running variable that increases from 1 to the maximum number of frames of Fz
pos=1;                                        %running variable position for saving i values when contact happens
while (i < length(Gonio1_abs_filt))                           %as long i is smaller than length of Fz
    i=i+1;
    if Gonio1_abs_filt(i) > Level                          %if force of Fz higher than the detection level
        Start_End(pos,1)=i;                %a value for Start_End is saved
        pos=pos+1;                            %to avoid overwriting of Start_End position to save is increased
        while Gonio1_abs_filt(i) > Level && i < length(Gonio1_abs_filt) %after finding a value higher than Level the system continious searching for a value smaller than the level
                                              %in addition the system should stop if the increasing variable i reaches the value of the length of Fz (to avoid error)
            i=i+1;
        end;
        Start_End(pos,1)=i;                %if a smaller value is found the value of i (frame) is saved to Start_End
        pos=pos+1;                            %to avoid overwriting of Start_End position to save is increased
    end;
end;
if Proband == 3
Start_End(15:16)='';
end;

%% EMG Daten bearbeiten
%BICEPS
Biceps_abs=abs(Biceps);

%Butterworth Filter
fc = 3;                                        % Cut-off frequency (Hz)
fs = 1000;                                     % Sampling rate (Hz)
order = 2;                                     % Filter order
[B,A] = butter(order,fc/(fs/2));               % Coefficients B and A are defined using the inputs

Biceps_abs_filt = filtfilt(B,A,Biceps_abs);          %EMG data is filtered

% figure(2);
% plot(Biceps,'k');hold on;
% plot(Biceps_abs,'b','LineStyle','--');hold on;
% plot(Biceps_abs_filt,'r','LineWidth',3);
% legend('Measured','Absolute','Filtered Absolute');

%% Schritte ausschneiden
Number_of_Steps=length(Start_End)/2;                   %number of steps should be defined

for i=1:Number_of_Steps-1                              
    data=Biceps_abs_filt(Start_End(i*2-1):Start_End(i*2+1));   %data is used from one starting point to the next starting point - whole gait cycle
   
    Biceps_100(:,i) = interp1(1 : length(data)  , data   , linspace(1, length(data),100)); 
                 %interp1(original structure, values , new structure                )                                                                     
end

Mean_Biceps_100=mean(Biceps_100,2);
Max_Mean_Biceps_100=max(Mean_Biceps_100);

ALL_Biceps(Kinesio,Proband)=Max_Mean_Biceps_100;

if Kinesio == 1
ALL_oK_Mean_Biceps_100(:,Proband)=Mean_Biceps_100;
end;
if Kinesio == 2
ALL_mK_Mean_Biceps_100(:,Proband)=Mean_Biceps_100;
end;


%************************************************

%TRICEPS
%% EMG Daten bearbeiten
Triceps_abs=abs(Triceps);

%Butterworth Filter
fc = 3;                                        % Cut-off frequency (Hz)
fs = 1000;                                     % Sampling rate (Hz)
order = 2;                                     % Filter order
[B,A] = butter(order,fc/(fs/2));               % Coefficients B and A are defined using the inputs

Triceps_abs_filt = filtfilt(B,A,Triceps_abs);          %EMG data is filtered

% figure(2);
% plot(Triceps,'k');hold on;
% plot(Triceps_abs,'b','LineStyle','--');hold on;
% plot(Triceps_abs_filt,'r','LineWidth',3);
% legend('Measured','Absolute','Filtered Absolute');

%% Schritte ausschneiden
Number_of_Steps=length(Start_End)/2;                   %number of steps should be defined

for i=1:Number_of_Steps-1                              
    data=Triceps_abs_filt(Start_End(i*2-1):Start_End(i*2+1));   %data is used from one starting point to the next starting point - whole gait cycle
   
    Triceps_100(:,i) = interp1(1 : length(data)  , data   , linspace(1, length(data),100)); 
                 %interp1(original structure, values , new structure                )                                                                     
end

Mean_Triceps_100=mean(Triceps_100,2);
Max_Mean_Triceps_100=max(Mean_Triceps_100);

ALL_Triceps(Kinesio,Proband)=Max_Mean_Triceps_100;

if Kinesio == 1
ALL_oK_Mean_Triceps_100(:,Proband)=Mean_Triceps_100;
end;
if Kinesio == 2
ALL_mK_Mean_Triceps_100(:,Proband)=Mean_Triceps_100;
end;
%******************************************

end;
end;


ALL_Faktor_Biceps=100./ALL_Biceps;
for i=1:5
ALL_oK_Mean_Biceps_100_norm(:,i)=ALL_oK_Mean_Biceps_100(:,i).*ALL_Faktor_Biceps(1,i);
end;
for i=1:5
ALL_mK_Mean_Biceps_100_norm(:,i)=ALL_mK_Mean_Biceps_100(:,i).*ALL_Faktor_Biceps(2,i);
end;
ALL_Mean_oK_Mean_Biceps_100_norm=mean(ALL_oK_Mean_Biceps_100_norm,2);
ALL_Mean_mK_Mean_Biceps_100_norm=mean(ALL_mK_Mean_Biceps_100_norm,2);

ALL_Faktor_Triceps=100./ALL_Triceps;
for i=1:5
ALL_oK_Mean_Triceps_100_norm(:,i)=ALL_oK_Mean_Triceps_100(:,i).*ALL_Faktor_Triceps(1,i);
end;
for i=1:5
ALL_mK_Mean_Triceps_100_norm(:,i)=ALL_mK_Mean_Triceps_100(:,i).*ALL_Faktor_Triceps(2,i);
end;
ALL_Mean_oK_Mean_Triceps_100_norm=mean(ALL_oK_Mean_Triceps_100_norm,2);
ALL_Mean_mK_Mean_Triceps_100_norm=mean(ALL_mK_Mean_Triceps_100_norm,2);

%Nachbearbeitung Messdaten
Prozent_Biceps=100-(100./ALL_Biceps(1,:).*ALL_Biceps(2,:));  

Mean_Prozent_Biceps=mean(Prozent_Biceps,2);
Std_Prozent_Biceps=std(Prozent_Biceps);
%Nachbearbeitung Messdaten
Prozent_Triceps=100-(100./ALL_Triceps(1,:).*ALL_Triceps(2,:));  
Mean_Prozent_Triceps=mean(Prozent_Triceps,2);
Std_Prozent_Triceps=std(Prozent_Triceps);


save('Save.mat');
% figure(1);
% plot(ALL_mK_Mean_Biceps_100(:,1),'r');hold on;
% plot(ALL_oK_Mean_Biceps_100(:,1),'k');hold on;
% 
% figure(2);
% plot(ALL_mK_Mean_Triceps_100(:,1),'r');hold on;
% plot(ALL_oK_Mean_Triceps_100(:,1),'k');hold on;

clc;               %deletes content of command window
clear all;         %deletes workspace
close all;         %closes figures 

cd('C:\Users\farid\Desktop\Kinesio\MAT');

for Proband = 1:5
   for Kinesio = 1:2   %Erst ohne dann mit Kinesio einlesen

   clear Start_End G*;
   clear B* M* N*;
   clear T* S* p* i*;
       
[FileName,PathName] = uigetfile('*.mat','Select the MATLAB data file');
cd(PathName);
load(FileName);

Gonio1_abs=abs(Gonio1);

%Butterworth Filter
fc = 20;                                        % Cut-off frequency (Hz)
fs = 1000;                                     % Sampling rate (Hz)
order = 2;                                     % Filter order
[B,A] = butter(order,fc/(fs/2));               % Coefficients B and A are defined using the inputs

Gonio1_abs_filt = filtfilt(B,A,Gonio1_abs);          %EMG data is filtered

%figure(1);
%plot(Gonio1_abs_filt);

%% Start Ende bestimmen
Level=0.025;                                     %Level of force where step should be detected 
i=1;                                          %running variable that increases from 1 to the maximum number of frames of Fz
pos=1;                                        %running variable position for saving i values when contact happens
while (i < length(Gonio1_abs_filt))                           %as long i is smaller than length of Fz
   i=i+1;
   if Gonio1_abs_filt(i) > Level                          %if force of Fz higher than the detection level
       Start_End(pos,1)=i;                %a value for Start_End is saved
       pos=pos+1;                            %to avoid overwriting of Start_End position to save is increased
       while Gonio1_abs_filt(i) > Level && i < length(Gonio1_abs_filt) %after finding a value higher than Level the system continious searching for a value smaller than the level
                                             %in addition the system should stop if the increasing variable i reaches the value of the length of Fz (to avoid error)
           i=i+1;
       end;
       Start_End(pos,1)=i;                %if a smaller value is found the value of i (frame) is saved to Start_End
       pos=pos+1;                            %to avoid overwriting of Start_End position to save is increased
   end;
end;
if Proband == 3
Start_End(15:16)='';
end;
% if Proband == 5
% Start_End(15:16)='';
% end;
%% EMG Daten bearbeiten
%BICEPS
Biceps_abs=abs(Biceps);

%Butterworth Filter
fc = 3;                                        % Cut-off frequency (Hz)
fs = 1000;                                     % Sampling rate (Hz)
order = 2;                                     % Filter order
[B,A] = butter(order,fc/(fs/2));               % Coefficients B and A are defined using the inputs

Biceps_abs_filt = filtfilt(B,A,Biceps_abs);          %EMG data is filtered

% figure(2);
% plot(Biceps,'k');hold on;
% plot(Biceps_abs,'b','LineStyle','--');hold on;
% plot(Biceps_abs_filt,'r','LineWidth',3);
% legend('Measured','Absolute','Filtered Absolute');

%% Schritte ausschneiden
Number_of_Steps=7;%length(Start_End)/2;                   %number of steps should be defined

for i=1:Number_of_Steps-1                              
   data=Biceps_abs_filt(Start_End(i*2-1):Start_End(i*2+1));   %data is used from one starting point to the next starting point - whole gait cycle
  
   Biceps_100(:,i) = interp1(1 : length(data)  , data   , linspace(1, length(data),100)); 
                %interp1(original structure, values , new structure                )                                                                     
end

Mean_Biceps_100=mean(Biceps_100,2);
Max_Mean_Biceps_100=max(Mean_Biceps_100);

ALL_Biceps(Kinesio,Proband)=Max_Mean_Biceps_100;

if Kinesio == 1
ALL_oK_Mean_Biceps_100(:,Proband)=Mean_Biceps_100;
end;
if Kinesio == 2
ALL_mK_Mean_Biceps_100(:,Proband)=Mean_Biceps_100;
end;


%************************************************

%TRICEPS
%% EMG Daten bearbeiten
Triceps_abs=abs(Triceps);

%Butterworth Filter
fc = 3;                                        % Cut-off frequency (Hz)
fs = 1000;                                     % Sampling rate (Hz)
order = 2;                                     % Filter order
[B,A] = butter(order,fc/(fs/2));               % Coefficients B and A are defined using the inputs

Triceps_abs_filt = filtfilt(B,A,Triceps_abs);          %EMG data is filtered

% figure(2);
% plot(Triceps,'k');hold on;
% plot(Triceps_abs,'b','LineStyle','--');hold on;
% plot(Triceps_abs_filt,'r','LineWidth',3);
% legend('Measured','Absolute','Filtered Absolute');

%% Schritte ausschneiden
Number_of_Steps=7;%length(Start_End)/2;                   %number of steps should be defined

for i=1:Number_of_Steps-1                              
   data=Triceps_abs_filt(Start_End(i*2-1):Start_End(i*2+1));   %data is used from one starting point to the next starting point - whole gait cycle
  
   Triceps_100(:,i) = interp1(1 : length(data)  , data   , linspace(1, length(data),100)); 
                %interp1(original structure, values , new structure                )                                                                     
end

%SPEZIELLE DATENKORREKTUR
if Proband == 1 && Kinesio == 1
   Triceps_100(63:68,2)=Triceps_100(63,2);
end;

Mean_Triceps_100=mean(Triceps_100,2);
Max_Mean_Triceps_100=max(Mean_Triceps_100);

ALL_Triceps(Kinesio,Proband)=Max_Mean_Triceps_100;

if Kinesio == 1
ALL_oK_Mean_Triceps_100(:,Proband)=Mean_Triceps_100;
end;
if Kinesio == 2
ALL_mK_Mean_Triceps_100(:,Proband)=Mean_Triceps_100;
end;
%******************************************

end;
end;


ALL_Faktor_Biceps=100./ALL_Biceps;
for i=1:5
ALL_oK_Mean_Biceps_100_norm(:,i)=ALL_oK_Mean_Biceps_100(:,i).*ALL_Faktor_Biceps(1,i);
end;
for i=1:5
ALL_mK_Mean_Biceps_100_norm(:,i)=ALL_mK_Mean_Biceps_100(:,i).*ALL_Faktor_Biceps(2,i);
end;
ALL_Mean_oK_Mean_Biceps_100_norm=mean(ALL_oK_Mean_Biceps_100_norm,2);
ALL_Mean_mK_Mean_Biceps_100_norm=mean(ALL_mK_Mean_Biceps_100_norm,2);

ALL_Faktor_Triceps=100./ALL_Triceps;
for i=1:5
ALL_oK_Mean_Triceps_100_norm(:,i)=ALL_oK_Mean_Triceps_100(:,i).*ALL_Faktor_Triceps(1,i);
end;
for i=1:5
ALL_mK_Mean_Triceps_100_norm(:,i)=ALL_mK_Mean_Triceps_100(:,i).*ALL_Faktor_Triceps(2,i);
end;
ALL_Mean_oK_Mean_Triceps_100_norm=mean(ALL_oK_Mean_Triceps_100_norm,2);
ALL_Mean_mK_Mean_Triceps_100_norm=mean(ALL_mK_Mean_Triceps_100_norm,2);

%Nachbearbeitung Messdaten
Prozent_Biceps=100-(100./ALL_Biceps(1,:).*ALL_Biceps(2,:));  

Mean_Prozent_Biceps=mean(Prozent_Biceps,2);
Std_Prozent_Biceps=std(Prozent_Biceps);
%Nachbearbeitung Messdaten
Prozent_Triceps=100-(100./ALL_Triceps(1,:).*ALL_Triceps(2,:));  
Mean_Prozent_Triceps=mean(Prozent_Triceps,2);
Std_Prozent_Triceps=std(Prozent_Triceps);


save('Save.mat');
% figure(1);
% plot(ALL_mK_Mean_Biceps_100(:,1),'r');hold on;
% plot(ALL_oK_Mean_Biceps_100(:,1),'k');hold on;
% 
% figure(2);
% plot(ALL_mK_Mean_Triceps_100(:,1),'r');hold on;
% plot(ALL_oK_Mean_Triceps_100(:,1),'k');hold on;