Most of the validation work on vaas-x so far had been industrial sensor data — turbofans, machine telemetry. I wanted to know if the same zero-config channel classifier actually transfers to a completely different domain: a wearable IMU strapped to a moving human. No feature engineering, no per-sport tuning, no hints about what any channel means.
I'm writing this one up slightly differently than my other posts, because the first version of this test gave me a wrong answer, and I think the reason it was wrong is more useful than the result itself.
The dataset
UCI's Daily and Sports Activities set (Altun, Barshan & Tunçel, 2010): 8 subjects, each wearing five Xsens IMU units — torso, both arms, both legs — 9 axes per unit (accelerometer, gyroscope, magnetometer × x/y/z), sampled at 25Hz. 45 channels total. It includes both a sedentary activity (sitting) and dynamic sport activities (basketball, rowing), which gives a clean, checkable question: does a classifier that's never seen this data correctly tell apart "person sitting still" from "person playing basketball," using channel statistics alone?
import pandas as pd
