Literature DB >> 33873111

Detection of foot contact in treadmill running with inertial and optical measurement systems.

Jasper Reenalda1, Marit A Zandbergen2, Jelle H D Harbers3, Max R Paquette4, Clare E Milner5.   

Abstract

In running assessments, biomechanics of the stance phase are often measured to understand external loads applied to the body. Identifying time of initial foot contact can be challenging in runners with different strike patterns. Peak downward velocity of the pelvis (PDVP) has been validated in a laboratory setting to detect initial contact. Inertial measurement units (IMUs) allow measurements of kinematic variables outside laboratory settings. The aim of this study was to validate the PDVP method using an inertial and optical motion capture system to detect initial contact at different speeds and foot strike patterns compared to the force sensing criterion. Twenty healthy runners ran for two minutes at 11, 13, and 15 km/h on a force-instrumented treadmill. 3D kinematics were obtained from an optical motion capture system and an 8-sensor inertial system. A generalized estimating equation showed no effect of footstrike pattern on the time difference (offset) between initial contact based on an inertial or optical system and the force sensing criterion. There was a significant main effect of speed on offset, in which offsets decreased with higher speeds. There was no interaction effect of speed and foot strike pattern on the offsets. Offsets ranged from 21.7 ± 0.2 ms for subjects running at 15 km/h (inertial versus force sensing criterion) to 27.2 ± 0.1 ms for subjects running at 11 km/h (optical versus force sensing criterion). These findings support the validity of the PDVP method obtained from optical and inertial systems to detect initial contact in different footstrike patterns and at different running speeds.
Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Year:  2021        PMID: 33873111     DOI: 10.1016/j.jbiomech.2021.110419

Source DB:  PubMed          Journal:  J Biomech        ISSN: 0021-9290            Impact factor:   2.712


  1 in total

1.  Towards Machine Learning-Based Detection of Running-Induced Fatigue in Real-World Scenarios: Evaluation of IMU Sensor Configurations to Reduce Intrusiveness.

Authors:  Luca Marotta; Jaap H Buurke; Bert-Jan F van Beijnum; Jasper Reenalda
Journal:  Sensors (Basel)       Date:  2021-05-15       Impact factor: 3.576

  1 in total

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