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combiningEMGandActivity.m
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combiningEMGandActivity.m
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% written by Jon Stingel
% 20210329
% gather and plot all the muscle activities from simulations.
import org.opensim.modeling.*
repodir = 'G:\Shared drives\Exotendon\muscleModel\muscleEnergyModel';
resultsdir = strcat(repodir, '/../results');
cd(resultsdir)
exocolor = '#AB82FF'
natcolor = '#FF7F00'
% conditions
% walsconditions = ['walsslack','walslow','walsmed','walshigh','walsmax']
% jackconditions = ['jackpower1','jackpower2','jackpower3','jackpower4','jackpower5','jackpower6',
% 'jacktau1','jacktau2','jacktau3','jacktau4','jacktau5']
% dembconditions = ['dembnoloadfree','dembnoloadslow','dembloadedfree','dembloadedmatched']
% sildconditions = ['sildbw0','sildbw5','sildbw10','sild10w0','sild10w5','sild10w10',
% 'sild20w0','sild20w5','sild20w10','sild30w0','sild30w5','sild30w10',
% 'sildbwrun0','sild10wrun0','sild20wrun0','sild30wrun0']
%%%%% - remember to only put in the exo conditions that you are looking to see the reductions from
% dembconditions = {'dembnoloadfree', 'dembloadedfree'}; %
% dembsubjects = {'demb010','demb011','demb012','demb014', 'demb005','demb007','demb009'}; %
welkexoconditions = {'welkexo'};%,'welkexoexo'}; % ,'welknaturalslow','welknaturalnatural', ...
% 'welknaturalexo','welkexonatural','welkexoexo','welkexofast'};
welknaturalconditions = {'welknatural'};%,'welknaturalnatural'};
% welksubjects = {'welk002','welk003','welk005','welk007','welk008','welk009','welk010','welk013'};
welksubjects = {'welk002','welk003','welk005','welk008','welk009','welk010','welk013'}; % 'welk008
tag = 'muscletrack';
thingstoplot = {'excitation','activation'};
load 'G:\Shared drives\Exotendon\muscleModel\muscleEnergyModel\subjectgaitcycles.mat';
exomeans_excitation = struct();
naturalmeans_excitation = struct();
exomeans_activation = struct();
naturalmeans_activation = struct();
exopeaks_excitation = struct();
exopeaks_activation = struct();
naturalpeaks_excitation = struct();
naturalpeaks_activation = struct();
nat_activitygrouped = struct();
exo_activitygrouped = struct();
nat_maxiso = struct();
exo_maxiso = struct();
% loop through the subjects
for subj=1:length(welksubjects)
subject = char(welksubjects(subj));
subjdir = strcat(resultsdir, strcat('/',subject));
exomeans_excitation.(genvarname(subject)) = [];
naturalmeans_excitation.(genvarname(subject)) = [];
exomeans_activation.(genvarname(subject)) = [];
naturalmeans_activation.(genvarname(subject)) = [];
exopeaks_excitation.(genvarname(subject)) = [];
exopeaks_activation.(genvarname(subject)) = [];
naturalpeaks_excitation.(genvarname(subject)) = [];
naturalpeaks_activation.(genvarname(subject)) = [];
%idk
nat_maxiso.(genvarname(subject)) = [];
exo_maxiso.(genvarname(subject)) = [];
% loop through each of the things we want to plot
for thing=1:length(thingstoplot)
tempthing = char(thingstoplot(thing))
welknaturalstruct = struct();
welkexostruct = struct();
welknat_isostruct = struct();
welkexo_isostruct = struct();
% loop through conditions - exo first
for cond=1:length(welkexoconditions)
condition = char(welkexoconditions(cond));
conddir = strcat(subjdir, strcat('/',condition));
trials = fieldnames(subjectgaitcycles.(genvarname(subject)).(genvarname(condition)));
% loop the trials
for trial=1:length(trials)
% what do we actually want to do here
test = char(trials(trial));
trialdir = strcat(conddir, strcat('/',test));
cd(trialdir)
% disp(trialdir)
% now figure out how to get and plot the signal i want
% have all the muscle analysis files already
% do I want to do average or individual?
if tempthing == 'activation'
% do something for the activations
% disp('getting activations...')
tempfile = strcat(trialdir, strcat('/',tag,'_states_100con.sto'));
% disp(tempfile)
% if strcmp(subject,'welk002') || strcmp(subject,'welk003')
% tempfile = strcat(trialdir,'/muscleprescribe_states.sto');
% else
% tempfile = strcat(trialdir, strcat('/',tag,'_states.sto'));
% end
tempTimeSeriesTable = TimeSeriesTable(tempfile);
temptime = tempTimeSeriesTable.getIndependentColumn();
times = zeros(temptime.size(),1);
for i=0:temptime.size()-1
times(i+1) = temptime.get(i);
end
timespercent = (times - times(1)) / (times(end) - times(1)) *100;
timespercent101 = [0:1:100]';
welkexostruct.time = timespercent101;
% now for each of the things
numCols = tempTimeSeriesTable.getNumColumns(); % including time
labels = tempTimeSeriesTable.getColumnLabels();
% get the model so that we can grab all the max iso forces
model = Model(strcat(trialdir,'/simple_model_all_the_probes_adjusted.osim'));
modelmuscles = model.getMuscles();
% loop through everything and get the activations.
for i=0:labels.size()-1
templab = char(labels.get(i));
% check if it is an activation for the right leg
if string(templab(length(templab)-9:end)) == 'activation' && templab(length(templab)-11) == 'r'
% we want the activations - doing right leg for
% simplicity
tempcol = tempTimeSeriesTable.getDependentColumn(java.lang.String(templab)).getAsMat();
tempcolinterp = interp1(timespercent, tempcol, timespercent101);
% get max iso force as well for muscle averaging.
% templab;
% get the forcepath name
musclename = templab(11:length(templab)-11);
tempmusc = modelmuscles.get(java.lang.String(musclename));
tempmaxiso = tempmusc.getMaxIsometricForce();
% add to the temp struct
if ~isfield(welkexostruct, templab(11:length(templab)-11))
% fix the naming
welkexostruct.(genvarname(templab(11:length(templab)-11))) = [];
welkexo_isostruct.(genvarname(templab(11:length(templab)-11))) = [];
end
welkexostruct.(genvarname(templab(11:length(templab)-11))) = [welkexostruct.(genvarname(templab(11:length(templab)-11))), tempcolinterp];
welkexo_isostruct.(genvarname(templab(11:length(templab)-11))) = [welkexo_isostruct.(genvarname(templab(11:length(templab)-11))), tempmaxiso];
end
end
end
% now for the case of excitations
if tempthing == 'excitation'
% do something for the excitations
% disp('getting excitations...')
tempfile = strcat(trialdir, strcat('/',tag,'_controls_100con.sto'));
% disp(tempfile)
% if strcmp(subject,'welk002') || strcmp(subject,'welk003')
% tempfile = strcat(trialdir,'/muscleprescribe_controls.sto');
% else
% tempfile = strcat(trialdir, strcat('/',tag,'_controls.sto'));
% end
tempTimeSeriesTable = TimeSeriesTable(tempfile);
temptime = tempTimeSeriesTable.getIndependentColumn();
times = zeros(temptime.size(),1);
for i=0:temptime.size()-1
times(i+1) = temptime.get(i);
end
timespercent = (times - times(1)) / (times(end) - times(1)) *100;
timespercent101 = [0:1:100]';
welkexostruct.time = timespercent101;
% now for each of the things
numCols = tempTimeSeriesTable.getNumColumns(); % including time
labels = tempTimeSeriesTable.getColumnLabels();
% loop through everything and get the activations.
for i=0:labels.size()-1
templab = char(labels.get(i));
% check if it is an activation for the right leg
if string(templab(length(templab)-1:end)) ~= '_l'
% we want the controls - doing right leg for
% simplicity
tempcol = tempTimeSeriesTable.getDependentColumn(java.lang.String(templab)).getAsMat();
tempcolinterp = interp1(timespercent, tempcol, timespercent101);
if ~isfield(welkexostruct, templab(11:end))
% fix the naming
welkexostruct.(genvarname(templab(11:end))) = [];
end
welkexostruct.(genvarname(templab(11:end))) = [welkexostruct.(genvarname(templab(11:end))), tempcolinterp];
end
end
end
end
end
% done with the exo conditions
% loop through conditions - now for the natural
for cond=1:length(welknaturalconditions)
condition = char(welknaturalconditions(cond));
conddir = strcat(subjdir, strcat('/',condition));
trials = fieldnames(subjectgaitcycles.(genvarname(subject)).(genvarname(condition)));
% loop the trials
for trial=1:length(trials)
% what do we actually want to do here
test = char(trials(trial));
trialdir = strcat(conddir, strcat('/',test));
cd(trialdir)
% disp(trialdir)
% now figure out how to get and plot the signal i want
% have all the muscle analysis files already
% do I want to do average or individual?
if tempthing == 'activation'
% do something for the activations
% disp('getting activations...')
tempfile = strcat(trialdir, strcat('/',tag,'_states_100con.sto'));
% if strcmp(subject,'welk002') || strcmp(subject,'welk003')
% tempfile = strcat(trialdir,'/muscleprescribe_states.sto');
% else
% tempfile = strcat(trialdir, strcat('/',tag,'_states.sto'));
% end
tempTimeSeriesTable = TimeSeriesTable(tempfile);
temptime = tempTimeSeriesTable.getIndependentColumn();
times = zeros(temptime.size(),1);
for i=0:temptime.size()-1
times(i+1) = temptime.get(i);
end
timespercent = (times - times(1)) / (times(end) - times(1)) *100;
timespercent101 = [0:1:100]';
welknaturalstruct.time = timespercent101;
% now for each of the things
numCols = tempTimeSeriesTable.getNumColumns(); % including time
labels = tempTimeSeriesTable.getColumnLabels();
% shouldn't need this but adding anyway
% get the model so that we can grab all the max iso forces
model = Model(strcat(trialdir,'/simple_model_all_the_probes_adjusted.osim'));
modelmuscles = model.getMuscles();
% loop through everything and get the activations.
for i=0:labels.size()-1
templab = char(labels.get(i));
% check if it is an activation for the right leg
if string(templab(length(templab)-9:end)) == 'activation' && templab(length(templab)-11) == 'r'
% we want the activations - doing right leg for
% simplicity
tempcol = tempTimeSeriesTable.getDependentColumn(java.lang.String(templab)).getAsMat();
tempcolinterp = interp1(timespercent, tempcol, timespercent101);
% get max iso force as well for muscle averaging.
% templab;
% get the forcepath name
musclename = templab(11:length(templab)-11);
tempmusc = modelmuscles.get(java.lang.String(musclename));
tempmaxiso = tempmusc.getMaxIsometricForce();
if ~isfield(welknaturalstruct, templab(11:length(templab)-11))
% fix the naming
welknaturalstruct.(genvarname(templab(11:length(templab)-11))) = [];
welknat_isostruct.(genvarname(templab(11:length(templab)-11))) = [];
end
welknaturalstruct.(genvarname(templab(11:length(templab)-11))) = [welknaturalstruct.(genvarname(templab(11:length(templab)-11))), tempcolinterp];
welknat_isostruct.(genvarname(templab(11:length(templab)-11))) = [welknat_isostruct.(genvarname(templab(11:length(templab)-11))), tempmaxiso];
end
end
end
% now for the case of excitations
if tempthing == 'excitation'
% do something for the excitations
% disp('getting excitations...')
tempfile = strcat(trialdir, strcat('/',tag,'_controls_100con.sto'));
% if strcmp(subject,'welk002') || strcmp(subject,'welk003')
% tempfile = strcat(trialdir,'/muscleprescribe_controls.sto');
% else
% tempfile = strcat(trialdir, strcat('/',tag,'_controls.sto'));
% end
tempTimeSeriesTable = TimeSeriesTable(tempfile);
temptime = tempTimeSeriesTable.getIndependentColumn();
times = zeros(temptime.size(),1);
for i=0:temptime.size()-1
times(i+1) = temptime.get(i);
end
timespercent = (times - times(1)) / (times(end) - times(1)) *100;
timespercent101 = [0:1:100]';
welknaturalstruct.time = timespercent101;
% now for each of the things
numCols = tempTimeSeriesTable.getNumColumns(); % including time
labels = tempTimeSeriesTable.getColumnLabels();
% loop through everything and get the activations.
for i=0:labels.size()-1
templab = char(labels.get(i));
% check if it is an activation for the right leg
if string(templab(length(templab)-1:end)) ~= '_l'
% we want the controls - doing right leg for
% simplicity
tempcol = tempTimeSeriesTable.getDependentColumn(java.lang.String(templab)).getAsMat();
tempcolinterp = interp1(timespercent, tempcol, timespercent101);
if ~isfield(welknaturalstruct, templab(11:end))
% fix the naming
welknaturalstruct.(genvarname(templab(11:end))) = [];
end
welknaturalstruct.(genvarname(templab(11:end))) = [welknaturalstruct.(genvarname(templab(11:end))), tempcolinterp];
end
end
end
end
end
% now need to loop through both natural and exo to find the 3 glutes
disp('can do weighted avgs here')
if tempthing == 'activation'
% create maps of all the muscles that we want to grab out
quadriceps = {'recfem_r','vasint_r','vaslat_r','vasmed_r'};
hipflexors = {'grac_r','iliacus_r','psoas_r','sart_r','tfl_r'};
hipabductors = {'glmed1_r','glmed2_r','glmed3_r','piri_r','glmin1_r','glmin2_r','glmin3_r'};
hamstrings = {'bflh_r','bfsh_r','semiten_r','semimem_r'};
hipadductors = {'addbrev_r','addlong_r','addmagDist_r','addmagIsch_r','addmagMid_r','addmagProx_r'};
hipextensors = {'glmax1_r','glmax2_r','glmax3_r'};
plantarflexors = {'fdl_r','fhl_r','gaslat_r','gasmed_r','soleus_r','perlong_r','perbrev_r','tibpost_r'};
dorsiflexors = {'tibant_r','edl_r','ehl_r'};
% create empty data
quadriceps_nat_data = [];
hipflexors_nat_data = [];
hipabductors_nat_data = [];
hamstrings_nat_data = [];
hipadductors_nat_data = [];
hipextensors_nat_data = [];
plantarflexors_nat_data = [];
dorsiflexors_nat_data = [];
quadriceps_exo_data = [];
hipflexors_exo_data = [];
hipabductors_exo_data = [];
hamstrings_exo_data = [];
hipadductors_exo_data = [];
hipextensors_exo_data = [];
plantarflexors_exo_data = [];
dorsiflexors_exo_data = [];
labels_iso = fields(welknat_isostruct);
quadcount = 0;
quadholder_nat = zeros(101,4);
quadholder_exo = zeros(101,4);
hipflexcount = 0;
hipflexholder_nat = zeros(101,4);
hipflexholder_exo = zeros(101,4);
hipabdcount = 0;
hipabdholder_nat = zeros(101,4);
hipabdholder_exo = zeros(101,4);
hamstringcount = 0;
hamstringholder_nat = zeros(101,4);
hamstringholder_exo = zeros(101,4);
hipaddcount = 0;
hipaddholder_nat = zeros(101,4);
hipaddholder_exo = zeros(101,4);
hipextcount = 0;
hipextholder_nat = zeros(101,4);
hipextholder_exo = zeros(101,4);
pfcount = 0;
pfholder_nat = zeros(101,4);
pfholder_exo = zeros(101,4);
dfcount = 0;
dfholder_nat = zeros(101,4);
dfholder_exo = zeros(101,4);
% can just multiply each muscle activation by the max iso for that subject,
for o=1:length(labels_iso)
templabel_iso = string(labels_iso(o));
if any(strcmp(quadriceps, templabel_iso))
tempquadact_nat = welknaturalstruct.(genvarname(templabel_iso));
tempquadact_exo = welkexostruct.(genvarname(templabel_iso));
getisoforce = welknat_isostruct.(genvarname(templabel_iso))(1);
% have the curve and the iso - want to multiply and keep a running sum of the multiplier
newquad_nat = tempquadact_nat.*getisoforce;
quadholder_nat = quadholder_nat + newquad_nat;
newquad_exo = tempquadact_exo.*getisoforce;
quadholder_exo = quadholder_exo + newquad_exo;
quadcount = quadcount + getisoforce;
end
if any(strcmp(hipflexors, templabel_iso))
temphipflexact_nat = welknaturalstruct.(genvarname(templabel_iso));
temphipflexact_exo = welkexostruct.(genvarname(templabel_iso));
getisoforce = welknat_isostruct.(genvarname(templabel_iso))(1);
% have the curve and the iso - want to multiply and keep a running sum of the multiplier
newhipflex_nat = temphipflexact_nat.*getisoforce;
hipflexholder_nat = hipflexholder_nat + newhipflex_nat;
newhipflex_exo = temphipflexact_exo.*getisoforce;
hipflexholder_exo = hipflexholder_exo + newhipflex_exo;
hipflexcount = hipflexcount + getisoforce;
end
if any(strcmp(hipabductors, templabel_iso))
temphipabdact_nat = welknaturalstruct.(genvarname(templabel_iso));
temphipabdact_exo = welkexostruct.(genvarname(templabel_iso));
getisoforce = welknat_isostruct.(genvarname(templabel_iso))(1);
% have the curve and the iso - want to multiply and keep a running sum of the multiplier
newhipabd_nat = temphipabdact_nat.*getisoforce;
hipabdholder_nat = hipabdholder_nat + newhipabd_nat;
newhipabd_exo = temphipabdact_exo.*getisoforce;
hipabdholder_exo = hipabdholder_exo + newhipabd_exo;
hipabdcount = hipabdcount + getisoforce;
end
if any(strcmp(hamstrings, templabel_iso))
temphamstringact_nat = welknaturalstruct.(genvarname(templabel_iso));
temphamstringact_exo = welkexostruct.(genvarname(templabel_iso));
getisoforce = welknat_isostruct.(genvarname(templabel_iso))(1);
% have the curve and the iso - want to multiply and keep a running sum of the multiplier
newhamstring_nat = temphamstringact_nat.*getisoforce;
hamstringholder_nat = hamstringholder_nat + newhamstring_nat;
newhamstring_exo = temphamstringact_exo.*getisoforce;
hamstringholder_exo = hamstringholder_exo + newhamstring_exo;
hamstringcount = hamstringcount + getisoforce;
end
if any(strcmp(hipadductors, templabel_iso))
temphipaddact_nat = welknaturalstruct.(genvarname(templabel_iso));
temphipaddact_exo = welkexostruct.(genvarname(templabel_iso));
getisoforce = welknat_isostruct.(genvarname(templabel_iso))(1);
% have the curve and the iso - want to multiply and keep a running sum of the multiplier
newhipadd_nat = temphipaddact_nat.*getisoforce;
hipaddholder_nat = hipaddholder_nat + newhipadd_nat;
newhipadd_exo = temphipaddact_exo.*getisoforce;
hipaddholder_exo = hipaddholder_exo + newhipadd_exo;
hipaddcount = hipaddcount + getisoforce;
end
if any(strcmp(hipextensors, templabel_iso))
temphipextact_nat = welknaturalstruct.(genvarname(templabel_iso));
temphipextact_exo = welkexostruct.(genvarname(templabel_iso));
getisoforce = welknat_isostruct.(genvarname(templabel_iso))(1);
% have the curve and the iso - want to multiply and keep a running sum of the multiplier
newhipext_nat = temphipextact_nat.*getisoforce;
hipextholder_nat = hipextholder_nat + newhipext_nat;
newhipext_exo = temphipextact_exo.*getisoforce;
hipextholder_exo = hipextholder_exo + newhipext_exo;
hipextcount = hipextcount + getisoforce;
end
if any(strcmp(plantarflexors, templabel_iso))
temppfact_nat = welknaturalstruct.(genvarname(templabel_iso));
temppfact_exo = welkexostruct.(genvarname(templabel_iso));
getisoforce = welknat_isostruct.(genvarname(templabel_iso))(1);
% have the curve and the iso - want to multiply and keep a running sum of the multiplier
newpf_nat = temppfact_nat.*getisoforce;
pfholder_nat = pfholder_nat + newpf_nat;
newpf_exo = temppfact_exo.*getisoforce;
pfholder_exo = pfholder_exo + newpf_exo;
pfcount = pfcount + getisoforce;
end
if any(strcmp(dorsiflexors, templabel_iso))
tempdfact_nat = welknaturalstruct.(genvarname(templabel_iso));
tempdfact_exo = welkexostruct.(genvarname(templabel_iso));
getisoforce = welknat_isostruct.(genvarname(templabel_iso))(1);
% have the curve and the iso - want to multiply and keep a running sum of the multiplier
newdf_nat = tempdfact_nat.*getisoforce;
dfholder_nat = dfholder_nat + newdf_nat;
newdf_exo = tempdfact_exo.*getisoforce;
dfholder_exo = dfholder_exo + newdf_exo;
dfcount = dfcount + getisoforce;
end
end
% need to divide by count for weighted average
quadriceps_exo_data = mean(quadholder_exo./quadcount, 2);
quadriceps_nat_data = mean(quadholder_nat./quadcount, 2);
hipflexors_nat_data = mean(hipflexholder_nat./hipflexcount, 2);
hipflexors_exo_data = mean(hipflexholder_exo./hipflexcount, 2);
hipabductors_nat_data = mean(hipabdholder_nat./hipabdcount, 2);
hipabductors_exo_data = mean(hipabdholder_exo./hipabdcount, 2);
hamstrings_nat_data = mean(hamstringholder_nat./hamstringcount, 2);
hamstrings_exo_data = mean(hamstringholder_exo./hamstringcount, 2);
hipadductors_nat_data = mean(hipaddholder_nat./hipaddcount, 2);
hipadductors_exo_data = mean(hipaddholder_exo./hipaddcount, 2);
hipextensors_nat_data = mean(hipextholder_nat./hipextcount, 2);
hipextensors_exo_data = mean(hipextholder_exo./hipextcount, 2);
plantarflexors_nat_data = mean(pfholder_nat./pfcount, 2);
plantarflexors_exo_data = mean(pfholder_exo./pfcount, 2);
dorsiflexors_nat_data = mean(dfholder_nat./dfcount, 2);
dorsiflexors_exo_data = mean(dfholder_exo./dfcount, 2);
% now add them to the struct so that they are with the rest of
% the fields
welknaturalstruct.quadriceps = quadriceps_nat_data;
welknaturalstruct.hipflexors = hipflexors_nat_data;
welknaturalstruct.hipabductors = hipabductors_nat_data;
welknaturalstruct.hamstrings = hamstrings_nat_data;
welknaturalstruct.hipadductors = hipadductors_nat_data;
welknaturalstruct.hipextensors = hipextensors_nat_data;
welknaturalstruct.plantarflexors = plantarflexors_nat_data;
welknaturalstruct.dorsiflexors = dorsiflexors_nat_data;
welkexostruct.quadriceps = quadriceps_exo_data;
welkexostruct.hipflexors = hipflexors_exo_data;
welkexostruct.hipabductors = hipabductors_exo_data;
welkexostruct.hamstrings = hamstrings_exo_data;
welkexostruct.hipadductors = hipadductors_exo_data;
welkexostruct.hipextensors = hipextensors_exo_data;
welkexostruct.plantarflexors = plantarflexors_exo_data;
welkexostruct.dorsiflexors = dorsiflexors_exo_data;
% and update the fields variable so that they are included.
testlabels_nat = fields(welknaturalstruct);
testlabels_exo = fields(welkexostruct);
end
labels_nat = fields(welknaturalstruct);
glutemax = {'glmax1_r','glmax2_r','glmax3_r'};
glutemed = {'glmed1_r','glmed2_r','glmed3_r'};
glutemin = {'glmin1_r','glmin2_r','glmin3_r'};
glutemax_data_nat = [];
glutemed_data_nat = [];
glutemin_data_nat = [];
glutemax_data_exo = [];
glutemed_data_exo= [];
glutemin_data_exo = [];
% loop the naturals first
for i=1:length(labels_nat)
templabel_nat = string(labels_nat(i));
if any(strcmp(glutemax, templabel_nat))
tempglute = welknaturalstruct.(genvarname(templabel_nat));
glutemax_data_nat = [glutemax_data_nat, tempglute];
end
if any(strcmp(glutemed, templabel_nat))
tempglute = welknaturalstruct.(genvarname(templabel_nat));
glutemed_data_nat = [glutemed_data_nat, tempglute];
end
if any(strcmp(glutemin, templabel_nat))
tempglute = welknaturalstruct.(genvarname(templabel_nat));
glutemin_data_nat = [glutemin_data_nat, tempglute];
end
end
glutemax_data_nat = mean(glutemax_data_nat, 2);
glutemed_data_nat = mean(glutemed_data_nat, 2);
glutemin_data_nat = mean(glutemin_data_nat, 2);
labels_exo = fields(welkexostruct);
% loop the exos now
for i=1:length(labels_exo)
templabel_exo = string(labels_exo(i));
if any(strcmp(glutemax, templabel_exo))
tempglute = welkexostruct.(genvarname(templabel_exo));
glutemax_data_exo = [glutemax_data_exo, tempglute];
end
if any(strcmp(glutemed, templabel_exo))
tempglute = welkexostruct.(genvarname(templabel_exo));
glutemed_data_exo = [glutemed_data_exo, tempglute];
end
if any(strcmp(glutemin, templabel_exo))
tempglute = welkexostruct.(genvarname(templabel_exo));
glutemin_data_exo = [glutemin_data_exo, tempglute];
end
end
glutemax_data_exo = mean(glutemax_data_exo, 2);
glutemed_data_exo = mean(glutemed_data_exo, 2);
glutemin_data_exo = mean(glutemin_data_exo, 2);
% make sure the new averaged will get into figure
welknaturalstruct.glmax_avg_r = glutemax_data_nat;
welknaturalstruct.glmed_avg_r = glutemed_data_nat;
welknaturalstruct.glmin_avg_r = glutemin_data_nat;
welkexostruct.glmax_avg_r = glutemax_data_exo;
welkexostruct.glmed_avg_r = glutemed_data_exo;
welkexostruct.glmin_avg_r = glutemin_data_exo;
% need to get new total labels
testlabels_nat = fields(welknaturalstruct);
testlabels_exo = fields(welkexostruct);
% now create a figure
% tempfig = figure('Position',[1,1,1920,1080]);
% for i=2:length(testlabels_nat)
% subplot(7,9,i-1);
% templabel = char(testlabels_nat(i));
% muscleplot1 = welknaturalstruct.(genvarname(templabel));
% plot(welknaturalstruct.time, muscleplot1, ':')
% hold on;
% plot(welknaturalstruct.time, mean(muscleplot1,2), 'k-', 'LineWidth', 2)
% title(templabel)
% xlabel('% gait cycle')
% ylabel(tempthing)
% grid on;
% end
% print(tempfig, ...
% strcat(repodir,'\..\analysis\',subject,'\', tempthing, '_natural', '.png'),...
% '-dpng', '-r500')
% disp('print 1')
% tempfig2 = figure('Position',[1,1,1920,1080]);
% for i=2:length(testlabels_exo)
% subplot(7,9,i-1);
% templabel = char(testlabels_exo(i));
% muscleplot2 = welkexostruct.(genvarname(templabel));
% plot(welkexostruct.time, muscleplot2, ':')
% hold on;
% plot(welkexostruct.time, mean(muscleplot2,2), 'k-', 'LineWidth', 2)
% title(templabel)
% xlabel('% gait cycle')
% ylabel(tempthing)
% grid on;
% end
% print(tempfig2, ...
% strcat(repodir,'\..\analysis\',subject,'\', tempthing, '_exo', '.png'),...
% '-dpng', '-r500')
% disp('print 2')
% combined exo and natural fig
% combineexonaturalfig = figure('Position',[1,1,1920,1080]);
% title('red=natural, blue=exo');
labels = fields(welkexostruct);
for i=2:length(labels)
% subplot(7,9,i-1);
templabel = char(labels(i));
muscleplot2 = welkexostruct.(genvarname(templabel));
muscleplot1 = welknaturalstruct.(genvarname(templabel));
% % plot(welkexostruct.time, muscleplot1, ':')
% hold on;
% plot(welkexostruct.time, mean(muscleplot2,2), 'b-', 'LineWidth', 2)
% plot(welknaturalstruct.time, mean(muscleplot1,2), 'r-', 'LineWidth', 2)
% title(templabel)
% xlabel('% gait cycle')
% ylabel(tempthing)
% grid on;
% % legend('exo','natural')
if tempthing == 'excitation'
exomeans_excitation.(genvarname(subject)) = [exomeans_excitation.(genvarname(subject)), mean(muscleplot2, 2)];
naturalmeans_excitation.(genvarname(subject)) = [naturalmeans_excitation.(genvarname(subject)), mean(muscleplot1, 2)];
excitelabels = fields(welkexostruct);
% exomeans_excitation. = [exomeans_excitation, mean(muscleplot2,2)];
% naturalmeans_excitation = [naturalmeans_excitation, mean(muscleplot1, 2)];
exopeaks_excitation.(genvarname(subject)) = [exopeaks_excitation.(genvarname(subject)), max(mean(muscleplot2, 2))];
naturalpeaks_excitation.(genvarname(subject)) = [naturalpeaks_excitation.(genvarname(subject)), max(mean(muscleplot1, 2))];
end
if tempthing == 'activation'
exomeans_activation.(genvarname(subject)) = [exomeans_activation.(genvarname(subject)), mean(muscleplot2, 2)];
naturalmeans_activation.(genvarname(subject)) = [naturalmeans_activation.(genvarname(subject)), mean(muscleplot1, 2)];
activelabels = fields(welkexostruct);
% exomeans_activation = [exomeans_activation, mean(muscleplot2, 2)];
% naturalmeans_activation = [naturalmeans_activation, mean(muscleplot1, 2)];
exopeaks_activation.(genvarname(subject)) = [exopeaks_activation.(genvarname(subject)), max(mean(muscleplot2, 2))];
naturalpeaks_activation.(genvarname(subject)) = [naturalpeaks_activation.(genvarname(subject)), max(mean(muscleplot1, 2))];
end
end
% print(combineexonaturalfig, ...
% strcat(repodir,'\..\analysis\',subject,'\', tempthing, '_combined', '.png'),...
% '-dpng', '-r500')
disp('print combined')
end
nat_maxiso.(genvarname(subject)) = [nat_maxiso.(genvarname(subject)), welknat_isostruct];
exo_maxiso.(genvarname(subject)) = [exo_maxiso.(genvarname(subject)), welkexo_isostruct];
end
% combined exo and natural fig - averages for subjects and mean
subjectcombineexonaturalfig1 = figure('Position',[1,1,1200,1920]);
title('red=natural, blue=exo');
% do more stuff
% averaging and whatnot
for i=1:length(excitelabels)-1
% make a subplot
subplot(16,4,i);
templabel = char(excitelabels(i+1));
hold on;
tempsubjavgs1 = [];
tempsubjavgs2 = [];
% for each muscle go through each subject
for subj=1:length(welksubjects)
subject = char(welksubjects(subj));
plot(welkexostruct.time, exomeans_excitation.(genvarname(subject))(:,i), 'b:', 'LineWidth', 0.4)
plot(welknaturalstruct.time, naturalmeans_excitation.(genvarname(subject))(:,i), 'r:', 'LineWidth', 0.4)
% add each signal to the temp vector to get means before moving to
% the next muscle
tempsubjavgs1 = [tempsubjavgs1, naturalmeans_excitation.(genvarname(subject))(:,i)];
tempsubjavgs2 = [tempsubjavgs2, exomeans_excitation.(genvarname(subject))(:,i)];
end
% now plot the means from the tempsubjavgs1/2
plot(welkexostruct.time, mean(tempsubjavgs2, 2), 'b-', 'LineWidth', 2)
plot(welknaturalstruct.time, mean(tempsubjavgs1, 2), 'r-', 'LineWidth', 2)
title(templabel)
xlabel('% gait cycle')
ylabel(tempthing)
% grid on;
% legend('exo','natural')
% legend(strcat('exo peak: ', num2str(max(mean(tempsubjavgs2, 2)))),strcat('nat peak: ',num2str(max(mean(tempsubjavgs1, 2)))))
end
% print(subjectcombineexonaturalfig1, ...
% strcat(repodir, '\..\analysis\', 'excitation_all_subjects_combined_nolegend.png'),...
% '-dpng', '-r500')
disp('print combined')
% now for the activations
% combined exo and natural fig - averages for subjects and mean
subjectcombineexonaturalfig2 = figure(100); %'Number',100,'Position',[1,1,1920,1080]);
set(gcf,'WindowStyle','Docked','Position',[1,1,3508,1280,])
% set(gcf,'Position',[1,1,700,4508])
title('red=natural, blue=exo');
% do more stuff
% averaging and whatnot
for i=1:length(activelabels)-1
% make a subplot
subplot(5,11,i);
% axis('square')
% axis('tight')
templabel = char(activelabels(i+1));
hold on;
tempsubjavgs1 = [];
tempsubjavgs2 = [];
% for each muscle go through each subject
for subj=1:length(welksubjects)
subject = char(welksubjects(subj));
% plot(welkexostruct.time, exomeans_activation.(genvarname(subject))(:,i), 'b:', 'LineWidth', 0.4)
% plot(welknaturalstruct.time, naturalmeans_activation.(genvarname(subject))(:,i), 'r:', 'LineWidth', 0.4)
% add each signal to the temp vector to get means before moving to
% the next muscle
tempsubjavgs1 = [tempsubjavgs1, naturalmeans_activation.(genvarname(subject))(:,i)];
tempsubjavgs2 = [tempsubjavgs2, exomeans_activation.(genvarname(subject))(:,i)];
end
disp(templabel)
% spm tests for simulated activities
addpath(genpath('G:\Shared drives\Exotendon\Matlab\common\')) ;
spm = spm1d.stats.nonparam.ttest_paired(squeeze(tempsubjavgs1)', squeeze(tempsubjavgs2)');
spmi = spm.inference(0.05, 'two_tailed',true, 'interp',true);
disp(spmi)
% tempfig = figure;
% spmi.plot();
% spmi.plot_threshold_label();
% spmi.plot_p_values();
% title(templabel)
% print(tempfig, strcat(repodir, '\..\analysis\activitySPM\',templabel,'.png'), ...
% '-dpng', '-r500')
% now plot the means from the tempsubjavgs1/2
figure(100);
plot(welkexostruct.time, mean(tempsubjavgs2, 2), 'b-', 'LineWidth', 2)
plot(welknaturalstruct.time, mean(tempsubjavgs1, 2), 'r-', 'LineWidth', 2)
title(templabel)
xlabel('% gait cycle')
ylabel(tempthing)
% grid on;
% legend('exo','natural')
% legend(strcat('exo peak: ', num2str(max(mean(tempsubjavgs2, 2)))),strcat('nat peak: ',num2str(max(mean(tempsubjavgs1, 2)))))
end
% print(subjectcombineexonaturalfig2, ...
% strcat(repodir, '\..\analysis\', 'testgroups_activation_all_subjects_combined_nolegend.png'),...
% '-dpng', '-r500')
disp('print combined')
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% now need to get the EMG stuff in here and grouped as well to add to the same figure
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%
% EMG_analysis.m
% This program loads processed EMG signals and determines portions of the
% gait cycle when they are significantly different
% Written by: Cara Welker, updated Jon Stingel
% 3/9/2023
% close all; clc; clear
% set(0,'DefaultFigureWindowStyle','docked')
if ismac
basedir = '/Volumes/GoogleDrive/Shared drives/Exotendon/DATA';
addpath(genpath('/Volumes/GoogleDrive/Shared drives/Exotendon/Matlab/common'));
addpath('G:\Shared drives\Exotendon\Matlab\plotting');
else
basedir = 'G:\Shared drives\Exotendon\DATA\' ;
addpath(genpath('G:\Shared drives\Exotendon\Matlab\common\')) ;
addpath('G:\Shared drives\Exotendon\Matlab\plotting');
end
% load('EMG_muscles_removed_withS4.mat');
% load('EMG_muscles_removed.mat');
load('EMG_all_updated.mat')
[nSubs, nTrials] = size(EMG.data);
nMuscl = length(EMG.muscleNames);
timesteps = 101;
%% idk what this is
EMG.avg_avg = zeros(timesteps, nMuscl);
EMG.avg = EMG.data;
% Loop through the subjects
for sub = 1:nSubs
%for statistic testing, make the number of gait cycles tested
%the same for exo and non-exo cases
%ToDO; rewrite so not hard-coded in!
EMG_exo = EMG.data{sub}; %exo
EMG_nat = EMG.data{sub+nSubs}; %natural
nTrials = min(length(squeeze(EMG_exo(1,1,:))),...
length(squeeze(EMG_nat(1,1,:))));
EMG_exo = EMG_exo(:,:,length(squeeze(EMG_exo(1,1,:)))-nTrials+1:end);
EMG_nat = EMG_nat(:,:,length(squeeze(EMG_nat(1,1,:)))-nTrials+1:end);
EMG.data{sub} = EMG_exo;
EMG.data{sub+nSubs} = EMG_nat;
% take averages and add to struct
EMG.avg{sub} = mean(EMG_exo,3);
EMG.avg{sub+nSubs} = mean(EMG_nat,3);
end % sub
%% statistical analysis and plotting for all subjects
%TODO: change so generalizable for different trials!
EMG_plotting = zeros(timesteps, nMuscl, nSubs, 2);
%EMG_plotting_avg = zeros(timesteps, nMuscl, 2);
Trials = [1,2]; % 1,2 bc natural used to be the 5th trial
for trial = 1:length(Trials)
nTrial = Trials(trial);
temp = zeros(timesteps,nSubs,nMuscl);
for sub = 1:nSubs
for muscl = 1:nMuscl
EMG_plotting(:,muscl,sub,trial) = EMG.avg{sub,nTrial}(:,muscl);
%temp(:,sub,muscl) = EMG.avg{sub,nTrial}(:,muscl);
end
end
% EMG_plotting_avg(:,:,trial) = squeeze(nanmean(temp,2));
end
params = [];
params.fh = 1;
data = [];
data.time = 0:100;
data.colheaders = EMG.muscleNames;
% EMG_plotting is 101, 14, 7, 2
quadEMG = {'VL','VM','RF'};
hipflexEMG = {'PS'};
hipabdEMG = {'GMED'};
hamEMG = {'BF','ST'};
hipaddEMG = {'ADD'};
hipextEMG = {'GMAX'};
pfEMG = {'SOL','LG','MG','PER'};
dfEMG = {'TA'};
secondtime = false;
for i = 1:2
if i==2
secondtime = true;
end
data.data = squeeze(EMG_plotting(:,:,:,i));
if i==2
%comparison data for statistical test
comp_data = squeeze(EMG_plotting(:,:,:,1));
params.color = [0.5, 0, 0];
else
comp_data = squeeze(EMG_plotting(:,:,:,2));
params.color = [0, 0, 0.5];
end
% need a way to get the iso forces to multiply
for subj=1:length(welksubjects)
subject = char(welksubjects(subj));
% get data
tempPS = data.data(:,1,subj);
tempADD = data.data(:,2,subj);
tempRF = data.data(:,3,subj);
tempVL = data.data(:,4,subj);
tempVM = data.data(:,5,subj);
tempGMAX = data.data(:,6,subj);
tempGMED = data.data(:,7,subj);
tempTA = data.data(:,8,subj);
tempBF = data.data(:,9,subj);
tempST = data.data(:,10,subj);
tempLG = data.data(:,11,subj);
tempMG = data.data(:,12,subj);
tempSOL = data.data(:,13,subj);
tempPER = data.data(:,14,subj);
% get the max iso for each muscle for each subject
subjisos = nat_maxiso.(genvarname(subject));
PSiso = subjisos.psoas_r(1);
ADDiso = subjisos.addlong_r(1);
RFiso = subjisos.recfem_r(1);
VLiso = subjisos.vaslat_r(1);
VMiso = subjisos.vasmed_r(1);
GMAXiso = mean([subjisos.glmax1_r(1), subjisos.glmax2_r(1), subjisos.glmax3_r(1)]);
GMEDiso = mean([subjisos.glmed1_r(1), subjisos.glmed2_r(1), subjisos.glmed3_r(1)]);
TAiso = subjisos.tibant_r(1);
BFiso = subjisos.bflh_r(1);
STiso = subjisos.semiten_r(1);
LGiso = subjisos.gaslat_r(1);
MGiso = subjisos.gasmed_r(1);
SOLiso = subjisos.soleus_r(1);
PERiso = subjisos.perlong_r(1);
% get averages by hand easily
quadsEMGavg = ((tempVL.*VLiso) + (tempVM.*VMiso) + (tempRF.*RFiso))/(VLiso + VMiso + RFiso);
hipflexEMGavg = tempPS;
hipabdEMGavg = tempGMED;
hamEMGavg = ((tempBF.*BFiso) + (tempST.*STiso))/(BFiso + STiso);
hipaddEMGavg = tempADD;
hipextEMGavg = tempGMAX;
pfEMGavg = ((tempSOL.*SOLiso) + (tempPER.*PERiso) + (tempLG.*LGiso) + (tempMG.*MGiso))/(SOLiso + PERiso + LGiso + MGiso);
dfEMGavg = tempTA;
% add to struct
if i==1
% we have the exo stuff so add to the exo struct
exomeans_activation.(genvarname(subject)) = [exomeans_activation.(genvarname(subject)), ...
quadsEMGavg, hipflexEMGavg, hipabdEMGavg, hamEMGavg, hipaddEMGavg, hipextEMGavg, pfEMGavg, dfEMGavg];
else
naturalmeans_activation.(genvarname(subject)) = [naturalmeans_activation.(genvarname(subject)), ...
quadsEMGavg, hipflexEMGavg, hipabdEMGavg, hamEMGavg, hipaddEMGavg, hipextEMGavg, pfEMGavg, dfEMGavg];
end
end
% plot_results(data, comp_data, params, secondtime);
end
activelabels = [activelabels; {'quadEMG'};{'hipflexEMG'};{'hipabdEMG'};{'hamEMG'};{'hipaddEMG'};{'hipextEMG'};{'pfEMG'};{'dfEMG'}];
% print(strcat('G:\Shared drives\Exotendon\muscleModel\analysis\', 'emg_updated_all_w_spm.png'),...
% '-dpng', '-r500')
keyboard
%% finally plot them both together.
% now for the activations
% combined exo and natural fig - averages for subjects and mean
subjectcombineexonaturalfig3 = figure(200); %'Number',100,'Position',[1,1,1920,1080]);
set(gcf,'WindowStyle','Docked','Position',[1,1,1500,1280,])