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Adaptive Interface for Personalized Center of Mass Self-Identification in Home Rehabilitation

Authors :
Philippe Fraisse
Mitsuhiro Hayashibe
Alejandro Gonzalez
Artificial movement and gait restoration (DEMAR)
Inria Sophia Antipolis - Méditerranée (CRISAM)
Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM)
Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)
Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM)
Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM)
Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM)
Source :
IEEE Sensors Journal, IEEE Sensors Journal, Institute of Electrical and Electronics Engineers, 2015, 15 (5), pp.2814-2823. ⟨10.1109/JSEN.2014.2379431⟩, IEEE Sensors Journal, 2015, 15 (5), pp.2814-2823. ⟨10.1109/JSEN.2014.2379431⟩
Publication Year :
2015
Publisher :
HAL CCSD, 2015.

Abstract

International audience; As the center of mass (CoM) position can be used to determine stability, current rehabilitation standards may be improved by tracking it. A personalized CoM estimate can be obtained using the statically equivalent serial chain (SESC) once the model parameters are identified. The identification phase can be completed using low-cost sensors (Kinect and WBB) outside the laboratory making CoM estimation feasible in a patient’s home. This work focuses on: (1) improving the SESC identification quality and speed, and (2) using the estimated CoM to determine stability. Identification time is reduced by creating a visual adaptive interface where the subject’s limbs are colored based on the convergence of the SESC parameters. A study was conducted on eight subjects and showed a faster convergence and lower root mean square error (rmse) when the adaptive interface was used. We found that a model capable of estimating the CoM position with an rmse of 27 mm could be obtained after only 90 s of identification when the interface was used, whereas twice as much time was needed when the interface was not used. The interface that was developed can be used by a subject to track his/her CoM position in a self-directed way. Stability was determined for a squat task using a dynamic index obtained from the estimated CoM trajectory and using only Kinect measurements. This shows one potential application for home rehabilitation and monitoring.

Details

Language :
English
ISSN :
1530437X
Database :
OpenAIRE
Journal :
IEEE Sensors Journal, IEEE Sensors Journal, Institute of Electrical and Electronics Engineers, 2015, 15 (5), pp.2814-2823. ⟨10.1109/JSEN.2014.2379431⟩, IEEE Sensors Journal, 2015, 15 (5), pp.2814-2823. ⟨10.1109/JSEN.2014.2379431⟩
Accession number :
edsair.doi.dedup.....268f27354b7edc18729db07b6d292410
Full Text :
https://doi.org/10.1109/JSEN.2014.2379431⟩