Unlocking health insights: Estimating advanced walking metrics with smartwatches

A deep learning model for estimating spatio-temporal gait metrics (e.g., walking speed, step length) from smartwatch IMU data.

The model uses a temporal convolutional network with multi-head output and was validated on 246 participants with ~70,000 walking segments, showing strong correlation (Pearson r>0.80) and excellent reliability (ICC>0.80) for most metrics compared to a lab-grade system.

This is primarily a research paper sharing findings.