sisa-routineguard

SISA-RoutineGuard v5b — ONNX Edition

노인 일상 패턴 이상 감지 (Galaxy Watch + Jetson Orin Nano Super)
ONNX exports for fast Jetson inference (TensorRT compatible).

📦 ONNX Models (Jetson deploy)

QueryRefiner ONNX 는 cross-attention shape 복잡으로 미지원 (PyTorch ckpt만).

Total ONNX: ~1.4 GB

🚀 Jetson Inference Example

import onnxruntime as ort
import numpy as np

# TensorRT EP (Jetson에서 자동 가속)
providers = [
    'TensorrtExecutionProvider',  # Jetson Orin Nano TensorRT
    'CUDAExecutionProvider',       # fallback CUDA
    'CPUExecutionProvider',        # last resort
]

# 1. Phone 측 PatchEncoder (Phone ONNX)
phone_sess = ort.InferenceSession("patch_encoder.onnx", providers=['CPUExecutionProvider'])
patches = phone_sess.run(None, {"acc": acc_array})  # [6, 250, 3] → [6, 256]

# 2. Jetson Pipeline
me_sess = ort.InferenceSession("minute_encoder.onnx", providers=providers)
minute = me_sess.run(None, {"patch_tokens": patches.reshape(1, 6, 256)})[0]

hs_sess = ort.InferenceSession("hourslot_encoder.onnx", providers=providers)
slot, slot_minutes = hs_sess.run(None, {"minute_embeds": minute_batch})  # [B, 60, 768]

# ... history, query, heads chain

🎯 모델 사이즈 — 444.66M params

📊 성능 (OOD: HAR-70+ 노인 70-95세, 학습 X)

🔧 Train/Val Split + Early Stopping

• Train/Val: 80/20 (3,277 / 819 samples)
• Best epoch = 1, val_loss = 0.2814 (saved)
• Early stop at epoch 13 (patience=10)
• Overfitting 방지 검증됨

📁 PyTorch checkpoints (학습 reproducibility용)

학습 데이터

CAPTURE-24 (151) + ArWISE V3 (10명/76일) + PPG-DaLiA (15) + WESAD (15) + MHEALTH (10). HAR-70+ (18명 70-95세)는 OOD 평가에만 사용.

Code

https://github.com/tlstngud/sisa-routineguard (PRESENTATION.md 포함)