KINEMATICS_TOOL_data_filteration.m
85ff112848b3master

KINEMATICS_TOOL_data_filteration.m
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			function [SSDATA] = KINEMATICS_TOOL_data_filteration(task, varargin)
			%{
			Function for filtering and transforming the uploaded raw measured data.
			--------------------------------------------------------------------------
			Syntax
			[SSDATA] = KINEMATICS_TOOL_data_filteration(task, varargin)
			--------------------------------------------------------------------------
			File Description :
			This function performs the following tasks depending on what task argument
			is defined:
			case 1: residual analysis
			case 2: harmonic analysis
			case 3: filter and transform the data
			The residual and harmonic analysis are provided to provide the user with a
			sense of the measured data in order to help him/her decide what cutoff
			frequency should be chosen for the low-pass filtering of the data. At the
			end, when the user input the data for the cutoff frequency and filter order
			the next step is to filter the data and transform them into the Matlab
			(thorax) coordinate system.
			Note that if the imported raw data has been already transformed and
			filtered user should skip thi spart of the GUI.
			--------------------------------------------------------------------------
			%}

			% define the landmarks list necessary for all the cases
			landmark_list = {'IJ' 'PX' 'C7' 'T8' 'SC' 'Ac' 'AA' 'EL' 'EM' 'RS' 'US'};

			% sampling frequency of VICON
			fs = 100;

			% -------------------------------------------------------------------------
			% CASE 1: Residual Analysis
			% -------------------------------------------------------------------------
			if isequal(task, 'Residual Analysis')

			% take the input data
			SSDATAin = varargin{1,1};
			raw_landmarks = SSDATAin.Measured_Kinematics;

			% define the range of cutoff frequency required for the residual
			% analysis
			cutoff_frequency_range = 1:0.1:4;

			% filter the all the data over the given range of cutoff frequency
			for CF_Id = 1:length(cutoff_frequency_range)

			% design the filter for the given cutoff frequency
			[B_filt A_filt] = butter(4, cutoff_frequency_range(CF_Id)*2/fs);

			% filter all the data over for each CF_Id
			for LM_Id = 1:11 % for landmark id

			% take the raw data of the landmark LM_Id and put it in X
			X = (raw_landmarks.(landmark_list{LM_Id})(:,:))';

			% filter the raw data in X
			X_filtered = filtfilt(B_filt, A_filt, X);

			% define the residual of the landmark LM_Id for the cutoff
			% frequency CF_Id and save it in the structure named
			% residual
			residual.(landmark_list{LM_Id})(:, CF_Id) = diag((X-X_filtered)'*(X-X_filtered))/length(X);
			end
			end

			% show the results of the residual analysis
			figure('units', 'normalized',...
			'position', [0.2, 0.3, 0.9, 0.9],...
			'name', 'residual analysis');

			for LM_Id = 1 :11 % for landmarks id
			subplot(3,4,LM_Id)
			resdiue = residual.(landmark_list{LM_Id});
			p = plot(cutoff_frequency_range, resdiue, 'o', 'MarkerSize', 8);
			p(1).Color = 'r'; p(2).Color = 'g'; p(3).Color = 'b';
			title([landmark_list{LM_Id}])
			xlabel('frequency [Hz]');
			ylabel('residual');
			legend('x','y','z');
			set(gca,'FontSize',16);
			grid on
			end

			% -------------------------------------------------------------------------
			% CASE 1: Harmonic Analysis
			% -------------------------------------------------------------------------
			elseif isequal(task, 'Harmonic Analysis')

			% take the input data
			SSDATAin = varargin{1,1};
			raw_landmarks = SSDATAin.Measured_Kinematics;

			% show the results of harmonic analysis for all the landmarks (I do not
			% need to save them)
			figure('units', 'normalized',...
			'position', [0.2, 0.3, 0.9, 0.9],...
			'name', 'Harmonic Analysis');

			for LM_Id = 1 :11 % for landmarks id
			subplot(3,4,LM_Id)

			% take the raw data of the landmark LM_Id
			X = (raw_landmarks.(landmark_list{LM_Id}))';

			% define the PSD of the landmark
			[Pxx, f] = pwelch(X, floor(fs/2), [], [], fs);

			p = plot(f, Pxx, 'LineWidth', 2);
			p(1).Color = 'r'; p(2).Color = 'g'; p(3).Color = 'b';
			title([landmark_list{LM_Id}]);
			xlabel('frequency');
			ylabel('signal power');
			legend('x','y','z');
			set(gca,'FontSize',16);
			grid on
			end
			% -------------------------------------------------------------------------
			% CASE 3: Filter and Transform Data
			% -------------------------------------------------------------------------
			elseif isequal(task, 'Filter Data')

			% take the input data
			SSDATAin = varargin{1,1};
			raw_landmarks = SSDATAin.Measured_Kinematics;

			% take the cutoff frequency from the user inputted data in the GUI
			KRGUIHandlein = varargin{1,2};
			CF = str2num(KRGUIHandlein.Cutoff_Frequency.Edit.String);

			% take the filter order from the data provided by the user
			filter_order = str2num(KRGUIHandlein.Filter_Order.Edit.String);

			% define the filter transfer function. We use 4th order butterworth filter
			% that will transfrom into 8th order zero-phase low-pass filter as we use
			% filtfilt in order to eliminate the phase lag of the forward filtering.
			[B_denom A_nom] = butter(filter_order, CF*2/fs);

			% 1) ------------------------------------------------------------------
			% low-pass filter the raw landmarks and plot them together with the raw
			% data
			figure('units', 'normalized',...
			'position', [0.2, 0.3, 0.9, 0.9],...
			'name', ['filtered data yet in VICON frame']);

			for LM_Id = 1 :11 % for landmarks id
			subplot(3,4,LM_Id)

			% get the landmarks' coordinates
			X = (raw_landmarks.(landmark_list{LM_Id}))';

			% filter the landmark data
			X_filtered.(landmark_list{LM_Id}) = filtfilt(B_denom, A_nom, X)';

			% construct the time for each landmark, in fact it's the same for all the landmarks of an activity-trial
			time = 0:1/fs:length(X)/fs-1/fs;

			% plot both the raw and filter data
			p = plot(time, X_filtered.(landmark_list{LM_Id}),...
			time, X, '--', 'LineWidth', 2);
			p(1).Color = 'r'; p(2).Color = 'g'; p(3).Color = 'b';
			p(4).Color = 'r'; p(5).Color = 'g'; p(6).Color = 'b';
			title([landmark_list{LM_Id}]);
			xlabel('time [s]');
			ylabel('position in VICON frame [mm]');
			legend('filtered x','filtered y','filtered z', 'raw x', 'raw y', 'raw z');
			set(gca,'FontSize',16);
			grid on
			end

			% 2) ------------------------------------------------------------------
			% transform the data from VICON coordinate to the Matlab (thorax)
			% coordinate system

			% go over all the data frames
			for frameId = 1: length(X_filtered.(landmark_list{1}))

			% get the landmarks' coordinates for the current frame
			for LM_Id = 1 : 11 % for landmarks id

			X_current.(landmark_list{LM_Id}) = X_filtered.(landmark_list{LM_Id})(:, frameId);

			end

			if frameId == 1
			% VICON -> MATLAB Rotation Matrix for the current frame
			Zt = (X_current.C7 + X_current.IJ)/2 - (X_current.T8 + X_current.PX)/2;
			Xt = cross(X_current.C7 - X_current.IJ, (X_current.T8 + X_current.PX)/2-X_current.IJ);
			Yt = cross(Zt, Xt);
			R_am = [Xt/norm(Xt), Yt/norm(Yt), Zt/norm(Zt)]'; %R_am=R_matlab', which means
			%R_matlab map the points in matlab to AMIRA. For instance if you think
			%about R_matlab*[1 0 0]' it returns the associated vector (point) related
			%to the x axis of matlab in amira. Obviously it will be the first column of
			%the R_matlab.


			% Homogeneous transformation Matrix which maps a point in AMIRA to matlab
			% coordinate system
			H_am = [[R_am, -R_am*X_current.IJ]; [0, 0, 0, 1]];

			end

			% Transform all points to MATLAB reference frame and then get rid of the
			% homegeneous part
			for LM_Id = 1 : 11 % for landmarks id

			X_current.(landmark_list{LM_Id}) = H_am*[X_current.(landmark_list{LM_Id}); 1];

			end

			% IJ = H_am*[IJ; 1]; measurement_pure2_t1.IJ(:,FrameId) = IJ(1:3,1);
			% PX = H_am*[PX; 1]; measurement_pure2_t1.PX(:,FrameId) = PX(1:3,1);
			% T8 = H_am*[T8; 1]; measurement_pure2_t1.T8(:,FrameId) = T8(1:3,1);
			% C7 = H_am*[C7; 1]; measurement_pure2_t1.C7(:,FrameId) = C7(1:3,1);
			% SC = H_am*[SC; 1]; measurement_pure2_t1.SC(:,FrameId) = SC(1:3,1);
			% Ac = H_am*[Ac; 1]; measurement_pure2_t1.Ac(:,FrameId) = Ac(1:3,1);
			% AA = H_am*[AA; 1]; measurement_pure2_t1.AA(:,FrameId) = AA(1:3,1);
			% EL = H_am*[EL; 1]; measurement_pure2_t1.EL(:,FrameId) = EL(1:3,1);
			% EM = H_am*[EM; 1]; measurement_pure2_t1.EM(:,FrameId) = EM(1:3,1);
			% US = H_am*[US; 1]; measurement_pure2_t1.US(:,FrameId) = US(1:3,1);
			% RS = H_am*[RS; 1]; measurement_pure2_t1.RS(:,FrameId) = RS(1:3,1);

			% save the filtered and transformed data per frame
			for LM_Id = 1 : 11 % for landmarks id

			Measured_Kinematics.(landmark_list{LM_Id})(:, frameId) = X_current.(landmark_list{LM_Id})(1:3,1);

			end
			end

			% replace the unfiltered and untrasnformed data by the recent filtered and
			% transformed data
			SSDATA.Measured_Kinematics = Measured_Kinematics;

			% display a message for the user
			msgbox(['the imported raw data was filtered, transformed, and saved in',...
			'SSDATA.Measured_Kinematics data structure'], 'Success');
			% -------------------------------------------------------------------------
			% CASE 4: if not a valid task
			% -------------------------------------------------------------------------
			else
			% Throw an error. No valid task was given
			ErrorMsg = [...
			'The user supplied task is not valid.',...
			'KINEMATICS_TOOL_data_filteration requires a valid task as input.\n',...
			'Try the Following : \n',...
			'1) Residual Analysis,\n',...
			'2) Harmonic Analysis,\n',...
			'3) Filter Data,\n',...
			'4) :),\n',...
			'9) .'];
			error('ScptGen:ErrorMsg', ErrorMsg);

			end


			return

msm/ShoulderModel/TOOLBOX_Kinematics/KINEMATICS_TOOL_data_filteration.m85ff112848b3master

KINEMATICS_TOOL_data_filteration.mView Options

msm/ShoulderModel/TOOLBOX_Kinematics/KINEMATICS_TOOL_data_filteration.m
85ff112848b3master

KINEMATICS_TOOL_data_filteration.m
View Options