Textile ECG Motion-Artifact Filter

Heart & Motion

Heart rate 72 bpm

Motion artifact 35%

Filter adaptation rate μ 0.080

Live Readouts

Raw electrode signal (noisy) Filtered signal (LMS output)

Detected BPM

—

BPM error

—

Output SNR

— dB

Filter weight |w|

0.000

How it works

Each electrode node in the woven strap reads a raw signal d[n] = clean ECG + motion artifact. The artifact is partly predictable: it correlates with a reference motion signal x[n] (the fabric's own stretch/accelerometer channel), the same way a runner's gait couples into a chest-strap sensor.

An adaptive noise canceller (LMS — least-mean-squares) learns two weights that predict the artifact from the reference and subtracts the prediction:

y[n] = w0·x[n] + w1·x[n-1]      (artifact estimate)
e[n] = d[n] - y[n]              (filtered output)
w_i += 2·μ·e[n]·x[n-i]          (weight update, i=0,1)

Because e[n] is what remains after removing the predictable part, driving it toward the true clean ECG is exactly what minimizes mean-square error — the filter converges without ever being told what "clean" looks like.

Heart rate — sets the true R-peak spacing woven into the simulated signal.
Motion artifact — how strongly gait/stretch noise couples into the raw reading; the reference channel scales with it too.
Adaptation rate μ — how fast the weights chase the artifact; too high overshoots and rings, too low barely converges within a session.
Filter ON/OFF — toggle the subtraction off to see the raw electrode reading alone, weights frozen.

Real-world relevance: this is the same architecture behind motion-artifact cancellation in chest-strap ECG textiles and adaptive-noise-cancelling headphones — a correlated reference channel plus an LMS filter recovering signal buried in structured noise.

Raw row Filtered row

⚠ Couldn't load the 3D engineThree.js failed to load from the CDN. Check your connection and reload.