The problem

Activity recognition from wearables sounds solved until you look at raw

accelerometer data. A tri-axial accelerometer on a smartwatch produces a

violent, noisy signal: high-frequency motion artifacts, orientation shifts every

time the wrist rotates, and baseline drift. Telling "walking" from "running" from

"standing still" in real time, on a small model, with sub-20ms inference, is a

genuinely hard signal processing problem before it is a machine learning

problem.

I built a motion inference suite to work through this properly: not just one

model, but four architectures trained and compared side by side — a 1D CNN, a

simple RNN, an LSTM, and a CNN-LSTM hybrid — with a live workbench for

watching them classify streaming sensor data.

Why 1D CNN + LSTM

Accelerometer data is a multivariate time series: three channels (x, y, z) sampled

at 50 Hz. The two architectures that matter most for this kind of data do

complementary jobs.

The 1D CNN handles local feature extraction.…