Unlocking health insights: Estimating advanced walking metrics with smartwatches (opens in new tab)

Google researchers have validated that smartwatches are a highly reliable and accurate platform for estimating complex spatio-temporal gait metrics, rivaling the performance of smartphone-based methods. By utilizing a multi-head deep learning model, the study demonstrates that wrist-worn devices can provide continuous, lab-grade health insights into a user's walking speed, step length, and balance without requiring the specific pocket placement or specialized laboratory equipment previously necessary for such data.

Multi-Head Deep Learning for Wrist-Based Sensors

• The researchers developed a temporal convolutional network (TCN) architecture designed to process raw inertial measurement unit (IMU) data, specifically 3-axis accelerometer and gyroscope signals sampled at 50 Hz.
• Unlike traditional models that only track temporal events and are prone to integration drift, this multi-head approach directly estimates both unilateral and bilateral metrics simultaneously.
• The model architecture extracts embeddings from the IMU signals and concatenates them with user height (a demographic scalar input) to improve the precision of spatial predictions.
• The system estimates a comprehensive suite of metrics, including gait speed, double support time (the proportion of time both feet are on the ground), step length, swing time, and stance time.

Large-Scale Validation and Study Protocol

• To ensure rigorous results, the study involved a diverse cohort of 246 participants across two international sites, generating approximately 70,000 walking segments.
• Ground truth measurements were captured using a professional-grade Zeno Gait Walkway system to provide high-precision reference data for comparison.
• The study protocol included various walking conditions to test the model's versatility: a self-paced six-minute walk test (6MWT), fast-paced walking, and induced physical asymmetry created by wearing hinged knee braces at specific angles.
• Researchers employed a five-fold cross-validation strategy, ensuring that all data from a single participant remained within a single split to prevent data leakage and ensure the model generalizes to new users.

Clinical Validity and Comparative Performance

• Smartwatch estimates demonstrated strong validity and excellent reliability, with Pearson correlation coefficients (r) and intraclass correlation coefficients (ICC) exceeding 0.80 for most metrics.
• Performance comparisons showed non-significant differences in Mean Absolute Percentage Error (MAPE) between the Pixel Watch and Pixel phone, establishing the smartwatch as a viable alternative to smartphone-based tracking.
• While double support time showed slightly lower but acceptable reliability (ICC 0.56–0.60), other metrics like step length and gait speed proved highly consistent across different walking speeds and styles.
• The model’s success suggests that smartwatches can effectively bridge the gap in gait analysis, providing a more practical and consistent platform for continuous health tracking than handheld devices.

This research establishes smartwatches as a powerful tool for longitudinal health monitoring, enabling the detection of neurological or musculoskeletal changes through passive, continuous gait analysis in everyday environments.