Motion Smoothness Analysis of the Gait Cycle, Segmented by Stride and Associated with the Inertial Sensors’ Locations
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 event...
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MDPI AG
2025-01-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/2/368 |
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| author | Leonardo Eliu Anaya-Campos Luis Pastor Sánchez-Fernández Ivett Quiñones-Urióstegui |
| author_facet | Leonardo Eliu Anaya-Campos Luis Pastor Sánchez-Fernández Ivett Quiñones-Urióstegui |
| author_sort | Leonardo Eliu Anaya-Campos |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1d03024d2f4b4df9b120c8068351700b |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-1d03024d2f4b4df9b120c8068351700b2025-01-24T13:48:40ZengMDPI AGSensors1424-82202025-01-0125236810.3390/s25020368Motion Smoothness Analysis of the Gait Cycle, Segmented by Stride and Associated with the Inertial Sensors’ LocationsLeonardo Eliu Anaya-Campos0Luis Pastor Sánchez-Fernández1Ivett Quiñones-Urióstegui2Instituto Nacional de Rehabilitación Luis Guillermo Ibarra Ibarra, Mexico City 14389, MexicoCentro de Investigación en Computación, Instituto Politécnico Nacional, Mexico City 07738, MexicoInstituto Nacional de Rehabilitación Luis Guillermo Ibarra Ibarra, Mexico City 14389, MexicoPortable 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.https://www.mdpi.com/1424-8220/25/2/368smoothness analysisgait cycleinertial sensors’ locationshuman walkingmotor taskrehabilitation |
| spellingShingle | Leonardo Eliu Anaya-Campos Luis Pastor Sánchez-Fernández Ivett Quiñones-Urióstegui Motion Smoothness Analysis of the Gait Cycle, Segmented by Stride and Associated with the Inertial Sensors’ Locations Sensors smoothness analysis gait cycle inertial sensors’ locations human walking motor task rehabilitation |
| title | Motion Smoothness Analysis of the Gait Cycle, Segmented by Stride and Associated with the Inertial Sensors’ Locations |
| title_full | Motion Smoothness Analysis of the Gait Cycle, Segmented by Stride and Associated with the Inertial Sensors’ Locations |
| title_fullStr | Motion Smoothness Analysis of the Gait Cycle, Segmented by Stride and Associated with the Inertial Sensors’ Locations |
| title_full_unstemmed | Motion Smoothness Analysis of the Gait Cycle, Segmented by Stride and Associated with the Inertial Sensors’ Locations |
| title_short | Motion Smoothness Analysis of the Gait Cycle, Segmented by Stride and Associated with the Inertial Sensors’ Locations |
| title_sort | motion smoothness analysis of the gait cycle segmented by stride and associated with the inertial sensors locations |
| topic | smoothness analysis gait cycle inertial sensors’ locations human walking motor task rehabilitation |
| url | https://www.mdpi.com/1424-8220/25/2/368 |
| work_keys_str_mv | AT leonardoeliuanayacampos motionsmoothnessanalysisofthegaitcyclesegmentedbystrideandassociatedwiththeinertialsensorslocations AT luispastorsanchezfernandez motionsmoothnessanalysisofthegaitcyclesegmentedbystrideandassociatedwiththeinertialsensorslocations AT ivettquinonesuriostegui motionsmoothnessanalysisofthegaitcyclesegmentedbystrideandassociatedwiththeinertialsensorslocations |