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Using

in a Prehension Study. Using. 12 May 2005. Ruud Meulenbroek David Rosenbaum, Mary Klein Breteler, Bert Steenbergen. Overview. Sneak Preview Report of experimental results to colleagues Use of Matlab Principle File I/O Data preprocessing Finding relevant time indices in behaviour

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  1. in a Prehension Study Using 12 May 2005 Ruud Meulenbroek David Rosenbaum, Mary Klein Breteler, Bert Steenbergen

  2. Overview • Sneak Preview • Report of experimental results to colleagues • Use of Matlab • Principle • File I/O • Data preprocessing • Finding relevant time indices in behaviour • Plotting • Calculating critical performance variables • Time-normalizing functions • Creating a data matrix for statistical analyses • Write output file for SPSSX

  3. Sneak preview- Report of experimental results to colleagues - Topic: “Hand shaping in grasping” • Background • Maximum aperture is linearly scaled to object size(Jeannerod 1981; Paulignan et al. 1991,1997; Smeets, 1999) • Research questions • Also when being blindfolded? • What about the rotations of the joints in the hand? Newell, K. M., Scully, D. M., McDonald, P. V., & Baillargeon, R.  (1989).  Task constraints and infant grip configurations.  Developmental Psychobiology, 22, 817-832.

  4. Sneak preview Theory • Motion planning • Entails goal-posture selection • Tuned to multiple task constraints Rosenbaum, D. A., Meulenbroek, R.G.J., Jansen, C., Vaughan, J. (2002). Posture-based motion planning: Applications to grasping. Psychological Review, 108, 708-734. Meulenbroek, R.G.J., Kappers, A.L., & Mutsaarts, M. (2001). Does haptic space play a role in the planning and execution of grasping movements? Poster presented at the 'Neural control of space coding and action production' meeting in Lyon (France), March 22-24, 2001

  5. Sneak preview Simulation The orientation of the‘opposition axis’reflects the final arm-hand posture Fast movement Slow movement

  6. Sneak preview Experiment • 10 Participants • 9 Objects (cylinders of appr. 20 cm height and a diameter of 0.3, 1.3, 2.3, 3.3, 4.3, 5.3, 6.3, 7.3, 8.3 cm) • 2 Modality Conditions: (Vision present versus absent; eyes open versus closed; ABBA counterbalancing) • 10 Replications (resulting in 180 trials per subject)

  7. Sneak preview Experimental Task • The participant sat comfortably at a table... • with the left and right hand on two pieces of sandpaper fixated on the table top, appr. 20 cm in front of left and right shoulder • At the start of each trial the experimenter placed a cylinder in the non-dominant hand of the participant • The participant held the bottom half of the cylinder with opposing thumb and fingers (of the non-dominant hand) • An acoustic go-signal was presented • The participant’s task was to grasp with the dominant hand the top half of the cylinder that he/she held in the non-dominant hand • In half the trials the participant was asked to close his/her eyes before the cylinder was placed in his/her non-dominant hand

  8. Optotrak 3020 (NDI, Canada) Upper arm Forearm “Rigid bodies” Infrared light emitting diodes Pen Trunk Hand Finger joints Infrared cameras Bouwhuisen, C.F., Meulenbroek, R.G.J., & Thomassen, A.J.W.M. (2002). A 3D motion-tracking method in graphonomic research: Possible applications in future handwriting recognition studies. International Journal of Pattern Recognition, 35(5), 1039-1047. Sneak preview Method Data acquisition

  9. Sneak preview Data acquisition + • Optotrak 3020; two time-locked camera systems • 8 IREDs on the joints of the hand • Sampling rate of 200 Hz • Recording interval of 3 s • Preprocessing: low-pass filtering (<8 Hz)

  10. 6 7 5 8 Aperture (‘opposition axis’ ) 1 4 2 3 Sneak preview IRED configuration and aperture index

  11. Use of Matlab Principle KISS : Keep It Simple Stupid (David Rosenbaum)

  12. Use of Matlab Constants, loop parameters, toggle switches Give your matlab programme a sensible name! Clear entire memory! % segmentation.m % clear all; % constants Fs=200; % Hz sec=1/Fs; filtfreq_low=8; filtfreq_high=0.0; % number of subjects and data files ibsubject=1; iesubject=12; ibfile=1; iefile=180; % toggle switches iplot=1; filter=1; outputwanted=1; Set constants Set loop parameters Exploit toggle switches (0=Off;1=On)

  13. Use of Matlab Windows, name of file with design codes % windows for plotting purposes if iplot==1 fig1=figure('position',[5 300 400 220]); fig2=figure('position',[400 300 400 220]); end; % subject loop for subject=ibsubject:iesubject, pp=subject % without semi-colon to provide feedback in command window! ppstr=int2str(pp); % read the data from the design file design.txt prefix=['C:\Ruud\Projects\Objectsize\data\pp' ppstr '\']; fname=['design']; ext=['.txt']; filename=[prefix fname ext]; eval(['load ' filename]); design=eval(fname);

  14. Use of Matlab Design information, file loop, filename datafile % put design information in separate arrays trialnr=design(:,1); vision=design(:,2); cylinder_size=design(:,3); ntrials=length(trialnr); % data file loop for jfile=1:ntrials, % feedback on filenumber in command window file=trialnr(jfile) % again without semicolon! fstr=int2str(file); % pick up IRED displacement data ext=['.otd']; if jfile<10 fname=['C#000' fstr]; end; if jfile>9 & jfile<100 fname=['C#00' fstr]; end; if jfile>99 fname=['C#0' fstr]; end; filename=[prefix fname ext];

  15. Use of MatlabRead data file fpread.m is separate m-file % read IRED displacement data from file [hdr,cdata] = fpfread(filename); X1 Y1 Z1 X2 Y2 Z2 Units: mm Time (samples)

  16. % Detect missing data value (-36973140302885665500000000000.0000) % and interpolate linearly function [x]=interpolate_missing_data_points(s); mdv=-36973140302885665500000000000.0000; ns=length(s); if s(1)==mdv i=1; while s(i)==mdv i=i+1; end; s(1:i)=s(i+1); end; for i=2:ns-1, if s(i)==mdv ib=i-1; j=i; while ((s(j)==mdv) & (j<ns)) j=j+1; end; ie=j; if ie==ns if ib>1 for k=ib:ie, s(k)=s(ib-1); end; end end; ds=(s(ie)-s(ib))./(ie-ib+1); for k=ib:ie, s(k)=s(ib)+((k-ib+1).*ds); end; end; end; x=s; Use of MatlabPreprocessing IRED displacement data % interpolate missing data points ndim=size(cdata); ns_ireds=ndim(1); for idim=1:ndim(2), h=cdata(:,idim); hi=interpolate_missing_data_points(h); cdata(:,idim)=hi; end; % convert to cm for idim=1:ndim(2), cdata(:,idim)=cdata(:,idim)./10; end;

  17. Use of MatlabPreprocessing IRED displacement data: A. filtering • Filter = 3rd-order Butterworth filter • Applied forward and backward to prevent phase shift % filter data for idim=1:ndim(2), h=cdata(:,idim); hf=filteren(h,Fs,filtfreq_low); cdata(:,idim)=hf; end; function[smooth]=filteren(data,Fs,Fc) % FILTEREN.M % function file voor filteren met verwijdering van de inslinger-effecten % Fs is de sample frequentie % Fc is de afsnijfrequentie [m,n]=size(data); Fs=Fs./2; [B,A]=butter(3,Fc/Fs); % 3e orde filter. % 6e orde low-pass filter, zero phase lag. for j=1:n smooth(:,j) = filtfilt(B,A,data(:,j)); end

  18. Use of MatlabPreprocessing IRED displacement data: B. filtering • Filter = 3rd-order Butterworth filter • Applied forward and backward to prevent phase shift % filter data for idim=1:ndim(2), h=cdata(:,idim); hf=filteren(h,Fs,filtfreq_low); cdata(:,idim)=hf; end; Red = raw signal Blue = smoothed signal

  19. Use of MatlabAdditional arrays to prepare plotting % number of samples, time axis, y=0 axis ns=ndim(1); t=[1:ns].*sec; t=t'; y0=zeros(1,ns); y0=y0';

  20. Use of MatlabPrepare plotting of top view of hand IRED positions % put IRED data in separate 3D matrix for i=1:ndim(1), for j=1:8, j1=j; k=(j1-1).*3+1; handx(i,j)=cdata(i,k); handy(i,j)=cdata(i,k+1); handz(i,j)=cdata(i,k+2); end; end; cdata 600x24 115200 double array handx 600x8 38400 double array handy 600x8 38400 double array handz 600x8 38400 double array

  21. Start End Use of MatlabPlot top view of hand % Plot XY plot if iplot==1, figure(fig1); set(gcf,'Color','white'); title('Top View'); hold on; axis equal; view(2); for i=1:4:600, plot3(handx(i,:),handy(i,:),handz(i,:),'k'); end; xlabel('X (cm)'); ylabel('Y (cm)'); end; % of if iplot==1

  22. 6 7 5 8 1 4 2 3 function [afgeleide]=afgeleid(data,sf) % AFGELEID.M % dit programma differentieert een willekeurige data set data die staat in een matrix % sf = samplefrequentie in Hz; Ron Jacobs, juli 1989 [m,n]=size(data); afgeleide=zeros(m,n); for i=3:m-2, afgeleide(i,:)=(-data(i-2,:)*1.5-data(i-1,:)*4+data(i+1,:)*4+data(i+2,:)*1.5)*(sf/14); end afgeleide(1,:) = afgeleide(3,:); afgeleide(2,:) = afgeleide(3,:); afgeleide(m,:) = afgeleide(m-2,:); afgeleide(m-1,:) = afgeleide(m-2,:); Use of MatlabDetermine wrist speed function % Determine wrist speed xw=handx(:,4); yw=handy(:,4); zw=handz(:,4); dxw=afgeleid(xw,Fs); dyw=afgeleid(yw,Fs); dzw=afgeleid(zw,Fs); vw=sqrt(dxw.^2+dyw.^2+dzw.^2);

  23. Use of MatlabCheck wrist speed function % Check wrist speed function in command window plot(vw); xlabel(‘samples’); ylabel(‘wrist speed (cm/s);

  24. Use of MatlabFind critical time indices in wrist speed function % Find start and end of movement and time index of maximum speed [vmax,tvmax]=max(vw); vthreshold=.05.*vmax; ib=tvmax; while vw(ib)>=vthreshold & ib>2 ib=ib-1; end; ie=tvmax; while vw(ie)>=vthreshold & ie<ns-1 ie=ie+1; end; ib ie tvmax threshold

  25. 6 7 5 8 1 4 2 3 Use of MatlabDetermine aperture-time function % Determine aperture-time function for i=1:ndim(1); dx=handx(i,1)-handx(i,8); dy=handy(i,1)-handy(i,8); dz=handy(i,1)-handy(i,8); aperture(i)=sqrt(dx.^2+dy.^2+dz.^2); end;

  26. Use of MatlabCheck aperture-time function % Check aperture function in command window plot(aperture); xlabel(‘samples’); ylabel(‘aperture (cm);

  27. Use of MatlabFind critical time indices in aperture-time function % Find maximum aperture and moment of maximum aperture [maxapt,tmaxapt]=max(aperture); mommaxapt=100.*((tmaxapt-ib)./(ie-ib)); maxapt tvmaxapt

  28. Define reference lines of critical time indices for plotting % Data for reference line in plotting tref(1)=tmaxapt;tref(2)=tmaxapt; aptref(1)=0;aptref(2)=maxapt; % Data for reference line in plotting trefv(1)=tvmax;trefv(2)=tvmax; vwref(1)=0;vwref(2)=max(vws); Use of MatlabCalculate amplitude-scaled wrist speed function to plot simultaneously with aperture-time function % Calculate amplitude scaled wrist speed function scalefactor=max(aperture)./max(vw); vws=vw.*scalefactor;

  29. Use of MatlabPlot aperture-time and normalized speed functions if iplot==1, figure(fig2); set(gcf,'Color','white'); title('Normalized Speed & Aperture')'; hold on; axis([t(ib) t(ie) 0 max(aperture)]); plot(t(ib:ie),aperture(ib:ie),'r'); plot(t(tref),aptref,'k'); plot(t(ib:ie),vws(ib:ie),'k-'); plot(t(trefv),vwref,'k'); xlabel('Time (s)'); ylabel('Aperture (cm)'); end; % of if iplot==1

  30. Sneak preview Example: Prehension kinematics Normalized speed (arbitrary units) Aperture (cm) Time (s)

  31. Sneak preview IRED configuration and joint angles 6 7 5 8 j5 j6 j4 1 j1 j3 j2 4 2 3

  32. Use of MatlabDetermine joint angles in hand function[alfa]=angle2d_signed(pos1,pos2,pos3); % calculates the 2D joint angle % Mary Klein Breteler, March 2002 % orientation of first segment th=atan2(pos2(1)-pos1(1),pos2(2)-pos1(2)); % orientation of second segment relative to first segment Rz = [cos(th) -sin(th) 0; sin(th) cos(th) 0; 0 0 1]; vec = pos2(1:2) - pos3(1:2); vec = [vec/norm(vec) 0]; vecrot = (Rz*vec')'; % angle alfa=atan2(vecrot(1),vecrot(2)); beta=unwrap(alfa); alfa=beta.*180/pi; % only positive values if alfa < 0, alfa = alfa + 360; end % Constants degtorad=pi./180; radtodeg=1./degtorad; % Determine enclosed angle of hand joints (n=6) for ired=1:6, for i=1:ns, pos1(1)=handx(i,ired); pos1(2)=handy(i,ired); pos1(3)=handz(i,ired); pos2(1)=handx(i,ired+1); pos2(2)=handy(i,ired+1); pos2(3)=handz(i,ired+1); pos3(1)=handx(i,ired+2); pos3(2)=handy(i,ired+2); pos3(3)=handz(i,ired+2); joint(i,ired)=angle2d_signed(pos1,pos2,pos3); end; end;

  33. j5 j6 j4 j1 j2 j3 Sneak preview Example: Joint rotations Note that joint angles may exceed 180 degs, partially due to their placement,i.e. imperfect alignment, and partially due to overextension.

  34. Use of MatlabCalculating critical performance variables, e.g. % Find start and end of movement ib=fileib(jfile); ie=fileie(jfile); [vmax,tvmax]=max(vw(ib:ie)); tvmax=tvmax+ib; momvmax=100.*((tvmax-ib)./(ie-ib)); % Determine maximum and moment of maximum joint angles for j=1:6, [jointmax,tjointmax]=max(joint(ib:ie,j)); maxjntang(j)=jointmax; tmaxjntang(j)=100.*((tjointmax)./(ie-ib)); end;

  35. Use of MatlabTime normalizing functions • Resample function to 50 data points • Write resampled function to output file • Process resampled functions in Excel/SPSSX % store time-normalized kinematic functions n1=ie-ib+1; n2=50; vwtn=time_normalize(vw(ib:ie),n1,n2); % function [tx]=time_normalize(x,n1,n2); % % x = 1-dimensionaal input array (check op rij of kolom array) % n1 = lengte van input array x % n2 = gewenste lengte van outputarray tx % function [tx]=time_normalize(x,n1,n2); s=1:n1; step=(n1-1)./(n2-1); si=1:step:n1; tx=interp1(s,x,si,'spline');

  36. Use of MatlabCreating a data matrix for statistical analysis % clear output arrays output=zeros(15,1); if outputwanted==1 output(1)=pp; output(2)=file; output(3)=vision(file); % vision output(4)=objectsize(file); % size output(5)=(ie-ib).*sec; % MT output(6)=vmax; % peak speed output(7)=momvmax; % moment peak speed (%MT) output(8)=maxapt; % peak aperture (cm) …Etc.

  37. Use of Matlab - Write outputfile for SPSSX % Write outputfile of, say 8 variables (independent + dependent) per observation if outputwanted==1 ext_out=['.dat']; fnameout=['output_new']; outname=[prefix fnameout ext_out]; fid=fopen(outname,'a'); f='%8.3f '; f_last='%8.3f\n'; % Take care number of f's equals number of output variables minus 1 outputformat=([f f f f f f f f_last]); fprintf(fid,outputformat,output); fclose(fid); end; % of outputwanted==1

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