title: Walking speed estimation using foot-mounted inertial sensors: comparing machine learning and strap-down integration methods.
abstract: In this paper we implemented machine learning (ML) and strap-down integration (SDI) methods and analyzed them for their capability of estimating stride-by-stride walking speed. Walking speed was computed by dividing estimated stride length by stride time using data from a foot mounted inertial measurement unit. In SDI methods stride-by-stride walking speed estimation was driven by detecting gait events using a hidden Markov model (HMM) based method (HMM-based SDI); alternatively, a threshold-based gait event detector was investigated (threshold-based SDI). In the ML method a linear regression model was developed for stride length estimation. Whereas the gait event detectors were a priori fixed without training, the regression model was validated with leave-one-subject-out cross-validation. A subject-specific regression model calibration was also implemented to personalize the ML method. Healthy adults performed over-ground walking trials at natural, slower-than-natural and faster-than-natural speeds. The ML method achieved a root mean square estimation error of 2.0% and 4.2%, with and without personalization, against 2.0% and 3.1% by HMM-based SDI and threshold-based SDI. In spite that the results achieved by the two approaches were similar, the ML method, as compared with SDI methods, presented lower intra-subject variability and higher inter-subject variability, which was reduced by personalization.
authors.affiliation: The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy. Electronic address: a.mannini@sssup.it.
authors.forename: Andrea
authors.initials: A
authors.lastname: Mannini
authors.affiliation: The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy.
authors.forename: Angelo Maria
authors.initials: AM
authors.lastname: Sabatini
mesh_terms: D000328:Adult; D001185:Artificial Intelligence; D005528:Foot; D005684:Gait; D006801:Humans; D007700:Kinetics; D008390:Markov Chains; D012815:Signal Processing, Computer-Assisted; D016138:Walking / Q000502:physiology*
keywords: Hidden Markov models; Inertial sensing; Strap down integration; Walking speed estimation