Emotion Recognition with Multi-task Learning I am beginner in Data Science and currently working on a learning project aimed at emotion recognition from a bio-medical sensor dataset. I want to use a Multi-task Learning to generate the output of three emotion levels - Arousal, Dominance &amp; Valance.
The dataset consists of 8 sensors data from 20 subjects, here I have attached a screenshot of very small part of the dataset to give youbetter insight about it:

So as you can see, the Dataset is about:

Multiple subjects and for each subject there are 11 columns of data.
Columns 1-8 are raw sensor data (EEG, EOG, GSR, Skin Temperature, IR Response,... ) at 250 Hz.
Columns 9-11 are the corresponding labels.
I suppose your question is .... &quot;How to do multi-task learning?&quot;

Should you take a neural network approach, for instance:
A simple example with the above diagram is to pass Columns 1-8 into the &quot;Shared Layers&quot;.
The task specific layers would each be related to your three targets
ie. Task 1 - Arousal,
Task 2 - Dominance &amp;,
Task 3 - Valance.
Do have a look at this review article on multi-task learning and its various types. A more accessible article with some code examples here.