Walking speed in real-life conditions is typically
estimated through wearable inertial sensors mounted on waist,
lower limbs, or wrists. Very recently, head-mounted inertial
sensors are emerging for gait assessment. The present study
explores the feasibility of measuring the stride speed with a
head-mounted inertial sensor in both laboratory and real-world
settings. The developed algorithm exploits a Temporal
Convolutional Network for the detection of the gait events and a
Gaussian Process Regression for the stride speed estimation.
The experimental evaluation was carried out on healthy young
participants during both standardised indoor and real-world
walking trials. For indoor trials, errors were smaller than
previous studies (0.05 m/s). As expected, errors increased at
lower speed regimes due to a reduced signals amplitude. During
2.5-hours real-world evaluation, errors were slightly larger but
acceptable (0.1 m/s). Reported results are encouraging and show
the feasibility of estimating gait speed with a single head-worn
inertial sensor
