I am a newbie to Machine Learning field as I am engaging to a personal project that I am trying to use the 6 degree of freedom Inertial Measurement Units(IMUs) measuring the Acceleration acting on 3 axes(x-y-z) and the Angular velocity around the same 3 axis(x-y-z). One sensor generates a set of 6 raw variables of: Acc_x, Acc_y, Acc_z, Gyro_x, Gyro_y, Gyro_z.

Initially I have 2 of those sensors that used to be attached on to the arm (one to the part above the elbow and one to the part bellow the elbow) together they spit out a dataset of 12 raw variables that represent a specific movement of the arm, I save them as a the csv file. This is the point where I really get overwhelmed with a huge amount of data that I don't know how to process this kind of data and extract the features to differentiate the gestures.

My dataset of the first movement I recorded looks like this:

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I denoted 1 for the first sensor above the elbow and 2 for the sensor below the elbow.

Looking forward to hearing the opinions from the experts and seniors on this.

Thank you in advanced.

Let me know if my question is inappropriate and lack of information as it is my first time.


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