#StackBounty: #distributions #neural-networks #classification Confusion over concatenating EMG data from different muscles to a single …

Bounty: 50

I’m trying to predict Freezing of Gait (FoG) for Parkinson’s patients using EMG signals recorded from three types of muscles of the subjects – tibialis anterior muscle of right leg, gastrocnemius muscle of right leg, and tibialis anterior muscle of left leg. It’s a two class classification problem.

Should I concatenate the data of these three columns to a single column before applying some window function to them? Or should I store the data from these three muscles in three different columns and process them separately because data from different muscles have different distributions hence putting them in a single column may confuse the deep learning model we are going to build?

I’ve shared signals (4000 samples) from three muscles for a particular subject below:

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