title: Ambulatory Assessment of Instantaneous Velocity during Walking Using Inertial Sensor Measurements.
abstract: A novel approach for estimating the instantaneous velocity of the pelvis during walking was developed based on Inertial Measurement Units (IMUs). The instantaneous velocity was modeled by the sum of a cyclical component, decomposed in the Medio-Lateral (ML), VerTical (VT) and Antero-Posterior (AP) directions, and the Average Progression Velocity (APV) over each gait cycle. The proposed method required the availability of two IMUs, attached to the pelvis and one shank. Gait cycles were identified from the shank angular velocity; for each cycle, the Fourier series coefficients of the pelvis and shank acceleration signals were computed. The cyclical component was estimated by Fourier-based time-integration of the pelvis acceleration. A Bayesian Linear Regression (BLR) with Automatic Relevance Determination (ARD) predicted the APV from the stride time, the stance duration, and the Fourier series coefficients of the shank acceleration. Healthy subjects performed tasks of Treadmill Walking (TW) and Overground Walking (OW), and an optical motion capture system (OMCS) was used as reference for algorithm performance assessment. The widths of the limits of agreements (±1.96 standard deviation) were computed between the proposed method and the reference OMCS, yielding, for the cyclical component in the different directions: ML: ±0.07 m/s (±0.10 m/s); VT: ±0.03 m/s (±0.05 m/s); AP: ±0.06 m/s (±0.10 m/s), in TW (OW) conditions. The ARD-BLR achieved an APV root mean square error of 0.06 m/s (0.07 m/s) in the same conditions.
authors.affiliation: The BioRobotics Institute, Scuola Superiore Sant'Anna, 56127 Pisa, Italy. angelo.sabatini@santannapisa.it.
authors.forename: Angelo Maria
authors.initials: AM
authors.lastname: Sabatini
authors.affiliation: The BioRobotics Institute, Scuola Superiore Sant'Anna, 56127 Pisa, Italy. andrea.mannini@santannapisa.it.
authors.forename: Andrea
authors.initials: A
authors.lastname: Mannini
keywords: Bayesian methods; Fourier harmonic analysis; body center of mass; inertial measurement unit; regression; walking speed