How to train an LSTM on multiple independent time-series of sensor-data

Question

I have sensor measurements for 10 different people performing the same experiment in which they need to complete a specific task. For each timestep in the measurements I have the corresponding label and my goal is to train a sequential classifier which predicts the action a person is performing given the sensor observations. So, basically, for each person I have a separate dataset containing timesteps, several sensor measurements and the corresponding action (activity) for each timestep. I want to perform a leave-one-out cross validation, which would mean that I will take the sequence of measurements and action labels for 9 people for the training part and 1 sequence for the test part. However, I don't know how to train my model on the 9 different independent measurement sequences (they have also different lengths). My idea is to first apply masking/padding to make the sequences of equal length L, then concatenate the padded sequences and for the training to use a batch size of n, where L is divisible by n without remainder. I am not sure though if this is the right way to go. Maybe Keras already supports training sequential models on independent sequences? I would be happy to hear your recommendations. Thank you!