From EMG Signal Noise to Usable Robotic Motion

Raw EMG data is not friendly. It spikes, drifts, and changes with muscle fatigue.

To move a robotic arm safely, the control pipeline needed three layers:

1. A denoising stage to suppress transient spikes.
2. A calibrated activation window per user session.
3. A smoothing controller to avoid jitter in servo movement.

Biggest surprise

Model accuracy mattered less than consistency. A slightly simpler classifier with stable timing felt better than a higher-accuracy model with occasional jumpy output.

Next iteration

I am now experimenting with adaptive thresholds that update slowly over time to handle session drift while keeping motion predictable.