Using Accelerometer Data to Tune the Parameters of an Extended Kalman Filter for Optical Motion Capture: Preliminary Application to Gait Analysis

Optical motion capture is currently the most popular method for acquiring motion data in biomechanical applications. However, it presents a number of problems that make the process difficult and inefficient, such as marker occlusions and unwanted reflections. In addition, the obtained trajectories m...

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Main Authors: Javier Cuadrado, Florian Michaud, Urbano Lugrís, Manuel Pérez Soto
Format: Article
Language:English
Published: MDPI AG 2021-01-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/21/2/427
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author Javier Cuadrado
Florian Michaud
Urbano Lugrís
Manuel Pérez Soto
author_facet Javier Cuadrado
Florian Michaud
Urbano Lugrís
Manuel Pérez Soto
author_sort Javier Cuadrado
collection DOAJ
description Optical motion capture is currently the most popular method for acquiring motion data in biomechanical applications. However, it presents a number of problems that make the process difficult and inefficient, such as marker occlusions and unwanted reflections. In addition, the obtained trajectories must be numerically differentiated twice in time in order to get the accelerations. Since the trajectories are normally noisy, they need to be filtered first, and the selection of the optimal amount of filtering is not trivial. In this work, an extended Kalman filter (EKF) that manages marker occlusions and undesired reflections in a robust way is presented. A preliminary test with inertial measurement units (IMUs) is carried out to determine their local reference frames. Then, the gait analysis of a healthy subject is performed using optical markers and IMUs simultaneously. The filtering parameters used in the optical motion capture process are tuned in order to achieve good correlation between the obtained accelerations and those measured by the IMUs. The results show that the EKF provides a robust and efficient method for optical system-based motion analysis, and that the availability of accelerations measured by inertial sensors can be very helpful for the adjustment of the filters.
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spelling doaj.art-17a9ee8046e74613913afe0088f1e0892023-12-03T12:37:38ZengMDPI AGSensors1424-82202021-01-0121242710.3390/s21020427Using Accelerometer Data to Tune the Parameters of an Extended Kalman Filter for Optical Motion Capture: Preliminary Application to Gait AnalysisJavier Cuadrado0Florian Michaud1Urbano Lugrís2Manuel Pérez Soto3Laboratory of Mechanical Engineering, University of La Coruña, 15403 Ferrol, SpainLaboratory of Mechanical Engineering, University of La Coruña, 15403 Ferrol, SpainLaboratory of Mechanical Engineering, University of La Coruña, 15403 Ferrol, SpainLaboratory of Mechanical Engineering, University of La Coruña, 15403 Ferrol, SpainOptical motion capture is currently the most popular method for acquiring motion data in biomechanical applications. However, it presents a number of problems that make the process difficult and inefficient, such as marker occlusions and unwanted reflections. In addition, the obtained trajectories must be numerically differentiated twice in time in order to get the accelerations. Since the trajectories are normally noisy, they need to be filtered first, and the selection of the optimal amount of filtering is not trivial. In this work, an extended Kalman filter (EKF) that manages marker occlusions and undesired reflections in a robust way is presented. A preliminary test with inertial measurement units (IMUs) is carried out to determine their local reference frames. Then, the gait analysis of a healthy subject is performed using optical markers and IMUs simultaneously. The filtering parameters used in the optical motion capture process are tuned in order to achieve good correlation between the obtained accelerations and those measured by the IMUs. The results show that the EKF provides a robust and efficient method for optical system-based motion analysis, and that the availability of accelerations measured by inertial sensors can be very helpful for the adjustment of the filters.https://www.mdpi.com/1424-8220/21/2/427Kalman filtermotion capturegait analysisinertial sensor
spellingShingle Javier Cuadrado
Florian Michaud
Urbano Lugrís
Manuel Pérez Soto
Using Accelerometer Data to Tune the Parameters of an Extended Kalman Filter for Optical Motion Capture: Preliminary Application to Gait Analysis
Sensors
Kalman filter
motion capture
gait analysis
inertial sensor
title Using Accelerometer Data to Tune the Parameters of an Extended Kalman Filter for Optical Motion Capture: Preliminary Application to Gait Analysis
title_full Using Accelerometer Data to Tune the Parameters of an Extended Kalman Filter for Optical Motion Capture: Preliminary Application to Gait Analysis
title_fullStr Using Accelerometer Data to Tune the Parameters of an Extended Kalman Filter for Optical Motion Capture: Preliminary Application to Gait Analysis
title_full_unstemmed Using Accelerometer Data to Tune the Parameters of an Extended Kalman Filter for Optical Motion Capture: Preliminary Application to Gait Analysis
title_short Using Accelerometer Data to Tune the Parameters of an Extended Kalman Filter for Optical Motion Capture: Preliminary Application to Gait Analysis
title_sort using accelerometer data to tune the parameters of an extended kalman filter for optical motion capture preliminary application to gait analysis
topic Kalman filter
motion capture
gait analysis
inertial sensor
url https://www.mdpi.com/1424-8220/21/2/427
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