What is your end goal? When modeling, if you are using PyTorch to handle gradient descent, the model parameters will only be updated once per batch.
When you batch the data each batch has time steps equivalent to the batch size. Hence the model would be handling each person individually, and then would be updating all of the gradients once per batch at the end.
What you are doing is the same as using batch size = 1. In other words your gradients would be updated after every person. This is more computationally costly, and you lose some of the benefits from batching.
see last image here:

  
      
      Machine Learning Mastery – 20 Jan 19
  
  
    

How to Control the Stability of Training Neural Networks With the Batch Size 1

Neural networks are trained using gradient descent where the estimate of the error used to update the weights is calculated based on a subset of the training dataset. The number of examples from the training dataset used in the estimate of the error...

  Est. reading time: 19 minutes
    

  
  
    
    
  
  


observe how the accuracy in training is more erratic with lower batch size.
For more help please provide sample data, and explain what you are trying to accomplish.