Devlog #1: eyeballing raw emg was never gonna work

yea so trying to read raw emg by eye and call it a gesture was not the move.

started by digging out a myoware 2.0 i had left over from a old project, strapped the electrodes to my forearm and wired the raw output straight into an esp32 adc pin, no shield, no filtering, just wanted to see what a fist clench looked like on the serial plotter before doing anything smarter. flexed a few times expecting clean spikes i could just threshold.

instead the raw trace was just noise on top of noise. every tiny arm movement gave a spike that looked identical to an actual clench, and the esp32’s adc added its own jitter on top of that. so, signal soup basically, couldnt tell a real flex from garbage.

so i backed off and started looking at how people actually do this instead of freehanding it, found stuff like https://github.com/csce585-mlsystems/EMG-Based-Hand-Recognition which bandpass filters and extracts features before a classifier ever sees the signal, plus a bunch of stuff training on the ninapro dataset that everyone in this space seems to use.

but yea, overall today i felt kind of dumb for expecting raw signal to just work, and now im thinking the real first step isnt gesture classification at all, its building the filtering/envelope pipeline properly before i touch a single label. UI code linked below