results_nooutliers is not a cell array of tables, but rather a cell array of cell arrays of tables.
load('results_nooutliers.mat')
results_nooutliers % 3x1 cell array
results_nooutliers{1} % 1x12 cell array
results_nooutliers{1}{1} % table
Since you've written the code to work on a cell array of tables, I'll make results_nooutliers into a 3x12 cell array of tables:
results_nooutliers = vertcat(results_nooutliers{:})
And then run the code as you've written it, except I'm going to vectorize the distance calculation (i.e., calculate the distances for all rows at once instead of looping over the rows) and change the name of the variable this_cell, which is a table, to T.
% Initialize a cell array to store the distances
results_distances = cell(size(results_nooutliers));
% Loop through each cell in the results_no_outliers array
for i = 1:numel(results_nooutliers)
% Get the current table for the participant
T = results_nooutliers{i};
% Check for empty tables
if isempty(T)
continue; % Skip empty tables
end
% Calculate distances for left hand
distances_A_B = sqrt( sum( ( T{:,[4 5 6]} - T{:,[1 2 3]} ).^2 , 2) );
% Calculate distances for right hand
distances_A_C = sqrt( sum( ( T{:,[7 8 9]} - T{:,[1 2 3]} ).^2 , 2) );
% Store distances for the current participant in a table
results_distances{i} = table(distances_A_B, distances_A_C, 'VariableNames', {'A_B_Distance', 'A_C_Distance'});
end
results_distances
