agk4444 / aditya369-wesad-binary

Aditya369-WESAD-Binary — wrist stress detector

by AGK FIRE INC

Small 1D-CNN (~225K params) on wrist Empatica E4 signals — BINARY stress vs non-stress (baseline + amusement merged, the WESAD paper's binary setup), leave-one-subject-out evaluated.

Results (LOSO, 15 folds)

Usage

import torch, json
from model import StressCNN

labels = json.load(open("label_map.json"))          # {"0": "non-stress", "1": "stress"}
model = StressCNN(n_ch=6, n_cls=2)
model.load_state_dict(torch.load("best_model.pt", map_location="cpu"))
model.eval()
# x: (1, 6, 1920) float32 — 60s window @32Hz, per-subject z-scored,
#    channels [BVP, EDA, TEMP, ACCx, ACCy, ACCz]
with torch.no_grad():
    pred = model(x).argmax(1).item()                # 0=non-stress, 1=stress

Training

• Data: WESAD, wrist only (BVP 64Hz, EDA/TEMP 4Hz, ACC 32Hz), resampled to 32Hz
• 60s windows, 50% overlap, per-subject z-norm, majority-vote labels
• Binary labels: stress (WESAD 2) -> 1; baseline (1) + amusement (3) -> 0
• Leave-one-subject-out, early stopping on a val split of train subjects
• best_model.pt = state_dict of the best fold

© 2026 AGK FIRE INC. Released under Apache 2.0.