Can 1D-CNN method apply to real-time time series classification?
The inputs of a CNN must have the same shape during prediction as when it was trained. So if you have a CNN trained on 50 time-steps windows, then you can make predictions on a stream of input by updating the data in this window continuously. Each new time step you push the most recent row onto the end, and drop the earliest row.
Of course it is possible to make a CNN model that take 1x19 as the input also. But such a model would not have any ability to detect patterns over time, which would probably limit its predictive power quite a lot.