STM32H7 Fatigue Detection: 1M Rows → 85k Rows, 512KB RAM, <100ms Inference — Is 4Hz Resampling The Right Move?

Building a real-time fatigue detection system for STM32H7 deployment.

Constraints:

• 512KB RAM
• <100ms inference
• preprocessing on laptop
• inference on-device only

Dataset:
~1M rows from asynchronous wearable sensors.

Labels:

• fatigue
• activity
• baseline

Current preprocessing strategy:
Resample everything to 4Hz.

Result:
~1M rows → ~85k synchronized rows.

Current doubts:

1. ACC to 4Hz: Using only the mean feels too lossy. Should I also include:

• std
• max/min
• magnitude
• energy

per 250ms window?

1. IBI: Forward-fill feels mathematically dirty for HRV-related information. Would it be better to:

• keep IBI irregular
• compute RMSSD/SDNN at native timing
• feed only HRV features downstream?

1. HR/Breathing: Does interpolating 1Hz → 4Hz introduce fake temporal resolution? Would keeping them at 1Hz be cleaner?

Considering switching to a multi-rate pipeline:

Question:
For embedded ML / TinyML deployment, is multi-rate worth the added pipeline complexity, or is synchronized 4Hz generally the better engineering tradeoff?

Would appreciate advice from anyone working with:

• wearable signals
• HRV
• TinyML
• embedded inference
• multimodal physiological data