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Sensors 2016, 16(11), 1914; doi:10.3390/s16111914

A New Multi-Sensor Fusion Scheme to Improve the Accuracy of Knee Flexion Kinematics for Functional Rehabilitation Movements

1
Sorbonne University, Université de Technologie de Compiègne, CNRS, UMR 7338 Biomechanics and Bioengineering, 60200 Compiègne, France
2
Sorbonne University, Université Pierre et Marie Curie, L2E, Place Jussieu, 75252 Paris, France
*
Author to whom correspondence should be addressed.
Academic Editor: Xue Wang
Received: 13 September 2016 / Revised: 9 November 2016 / Accepted: 10 November 2016 / Published: 15 November 2016
(This article belongs to the Section Sensor Networks)
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Abstract

Exergames have been proposed as a potential tool to improve the current practice of musculoskeletal rehabilitation. Inertial or optical motion capture sensors are commonly used to track the subject’s movements. However, the use of these motion capture tools suffers from the lack of accuracy in estimating joint angles, which could lead to wrong data interpretation. In this study, we proposed a real time quaternion-based fusion scheme, based on the extended Kalman filter, between inertial and visual motion capture sensors, to improve the estimation accuracy of joint angles. The fusion outcome was compared to angles measured using a goniometer. The fusion output shows a better estimation, when compared to inertial measurement units and Kinect outputs. We noted a smaller error (3.96°) compared to the one obtained using inertial sensors (5.04°). The proposed multi-sensor fusion system is therefore accurate enough to be applied, in future works, to our serious game for musculoskeletal rehabilitation. View Full-Text
Keywords: multi-sensor fusion; orientation estimation; home-based functional rehabilitation; exergames multi-sensor fusion; orientation estimation; home-based functional rehabilitation; exergames
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Tannous, H.; Istrate, D.; Benlarbi-Delai, A.; Sarrazin, J.; Gamet, D.; Ho Ba Tho, M.C.; Dao, T.T. A New Multi-Sensor Fusion Scheme to Improve the Accuracy of Knee Flexion Kinematics for Functional Rehabilitation Movements. Sensors 2016, 16, 1914.

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