Motion Smoothness Analysis of the Gait Cycle, Segmented by Stride and Associated with the Inertial Sensors’ Locations

• Published in: Sensors (Basel, Switzerland) Vol. 25; no. 2; p. 368
• Main Authors: Anaya-Campos, Leonardo Eliu, Sánchez-Fernández, Luis Pastor, Quiñones-Urióstegui, Ivett
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.01.2025; MDPI
• Subjects: Acceleration; Accelerometry; Adult; Algorithms; Analysis; Biomechanical Phenomena; Biomechanics; Female; Foot - physiology; Fourier transforms; Gait; Gait - physiology; Gait Analysis - methods; gait cycle; human walking; Humans; inertial sensors’ locations; Kinematics; Male; Motion; Motion capture; motor task; Multiple sclerosis; Photogrammetry; rehabilitation; Sensors; Signal Processing, Computer-Assisted; smoothness analysis; Velocity; Walking - physiology; Young Adult; Mexico; smoothness analysis; rehabilitation; human walking; gait cycle; inertial sensors’ locations; motor task
• DOI: 10.3390/s25020368
• ISSN: 1424-8220

Portable monitoring devices based on Inertial Measurement Units (IMUs) have the potential to serve as quantitative assessments of human movement. This article proposes a new method to identify the optimal placements of the IMUs and quantify the smoothness of the gait. First, it identifies gait events: foot-strike (FS) and foot-off (FO). Second, it segments the signals of linear acceleration and angular velocities obtained from the IMUs at four locations into steps and strides. Finally, it applies three smoothness metrics (SPARC, PM, and LDLJ) to determine the most reliable metric and the best location for the sensor, using data from 20 healthy subjects who walked an average of 25 steps on a flat surface for this study (117 measurements were processed). All events were identified with less than a 2% difference from those obtained with the photogrammetry system. The smoothness metric with the least variance in all measurements was SPARC. For the smoothness metrics with the least variance, we found significant differences between applying the metrics with the complete signal (C) and the signal segmented by strides (S). This method is practical, time-effective, and low-cost in terms of computation. Furthermore, it is shown that analyzing gait signals segmented by strides provides more information about gait progression.