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Leave One Out Cross Validation Hyperparameter Tuning

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I have a dataset of motion data from different subjects which is used to do activity recognition.
To make u understand something like this
1 32133 322332 212121 WALKING
1 54322 424242 342757 LYING
2 32133 322332 212121 WALKING
2 52322 424652 321217 RUNNING
Now my prediction is the activity column and i know there are specific function that can separate my dataset in training test and tuning hyperparameters ....
However while i'm building the classifier model i want to do a specific cross-validation in which every fold is made by all data from all subjects except one for testing. So i was thinking to use the built-in function of MATLAB and bayesopt to tune the hyperparameter of the model.
I have a data set of around 16600 observation and if i use the 'LeaveOut' option in cvpartion it gave me back the same number of folds.
Does anyone can give some advice on this ?
Every help or documentation is really appreciate.
Observations from all subjects are not always the same number

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