Our group has gotten past this while scripting in Matlab by using the diary function and saving to a text file (saved as the variable RRA.MassChanges in the code below). Then we read in the text file and search for the updated segment masses and save to a new model.

Hope this helps! Contact me at rickypim@live.unc.edu for any questions.

clc;
diary(RRAfile.MassChanges); % save all text printed into command window into text file
rraTool.run(); % run RRA
diary off;
clc;

fid = fopen(RRAfile.MassChanges, 'r');
TxtData1 = textscan(fid,'%q');
TxtDataString = TxtData1{1,1};
[StarWars,~] = find(contains(TxtDataString, '*****'));

TxtData = TxtDataString(StarWars(1):end);
CellDif = 8; % number of cells between segment name and new mass value
Segments = {'pelvis','femur_r','tibia_r','talus_r','calcn_r','toes_r', ...
'femur_l','tibia_l','talus_l','calcn_l','toes_l', 'torso'};

SegInOutput = find(contains(TxtData, Segments));
SegInOutput([1, 14, 15, 16]) = []; % remove mentions of pelvis that are not for segment masses

for i = 1:length(SegInOutput) % save mass changes into Subjects structure
Subjects(subj).Trials(trial).RRAmassChanges(i).segment = Segments{i};
Subjects(subj).Trials(trial).RRAmassChanges(i).value = ...
str2double(cell2mat(TxtData(SegInOutput(i)+CellDif)));
end

% Update model segment masses based on RRA results
newModel = Model(model); % create new model
for i = 1:length(SegInOutput)
Seg = Segments{i};
Segment = newModel.getBodySet().get(Seg);
Segment.setMass(Subjects(subj).Trials(trial).RRAmassChanges(i).value);
end

% save model as osim file
RRAUpdatedModel = strcat(RRAFolder2, '\', Subjects(subj).name, '_RRA_Post1.osim');
newModel.print(RRAUpdatedModel);