machine learning on multiple signal at once
You say you're "a beginner" so I will suggest using Keras (https://keras.io/). It is written in Python and runs on top of Tensorflow which is a neural network toolbox written by Google. Keras allows you to build a network layer-by-layer in an easy way. For example, here is an example of building a 1D CNN (https://keras.io/examples/imdb_cnn/), please note that the example contains many things that you do not need or are optional. My advice, start with a simple network and know how to identify its problems and how to fix them. 

A while back, I worked on a similar problem involving inputting two time domain signals and classifying it (input: [# samples]-by-2, output: probabilities corresponding to each class). Now you will input the two time domain signals and you need to think carefully about how your output is formatted because sometimes you might have one activity, other times you might have five activities, or sometimes you might have no activity.

For example, say you input two signals ($x_1[n], x_2[n]$) of length 1024 and say you had activity #1 during samples 0-255, activity #2 from 256-511, and all other time is activity #0. Then you could format your output to be a signal of length 1024 (equal to length of input signals), to be:
$y[n]=
 \begin{cases} 
      1, &amp; 0 \leq n \leq 255 \\
      2, &amp; 256 \leq n \leq 511 \\
      0, &amp; n \geq 512 
   \end{cases}
$. I bet this requires a bit of preprocessing of your dataset but this is not uncommon at all when doing these sorts of things.