Unlocking health insights: Estimating advanced walking metrics with smartwatches

Gait metrics — measures like walking speed, step length, and double support time (i.e., the proportion of gait cycle when both feet are on the ground) — are known to be vital biomarkers for assessing a person’s overall health, risk of falling, and progression of neurological or musculoskeletal conditions. Analyzing how a person walks, known as gait analysis, offers valuable, non-invasive insights into general well-being, injuries, and health concerns.

Historically, measuring gait required expensive, specialized laboratory equipment, making continuous tracking impractical. While smartphones now offer a portable alternative using their embedded inertial measurement units (IMUs), they demand precise placement — such as a thigh pocket or belt — for the most accurate results. In contrast, smartwatches are worn on the wrist in a fixed location. This provides a much more practical and consistent platform for continuous tracking, even expanding the tracking window to phone-less scenarios like walking around the house.

Despite this crucial logistical advantage, smartwatches have historically lagged behind smartphones in comprehensive gait metric evaluation. In our work, "Smartwatch-Based Walking Metrics Estimation", we sought to bridge this gap. We demonstrated that consumer smartwatches are a highly viable, accurate, and reliable platform for estimating a comprehensive suite of spatio-temporal gait metrics, with performance comparable to smartphone-based methods.

A deep learning approach for the wrist

For data segmentation, we used 5-second windows with a 1-second overlap. We utilized mean absolute percentage error (MAPE) for the loss function, which uniquely optimizes for the relative accuracy across all multi-unit outputs (e.g., step length in cm, double support time in ms).

Rigorous validation in a large-scale study

The study protocol included a diverse range of walking patterns to ensure comprehensive evaluation: