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An adaptive complementary filter for inertial sensor based data fusion to track upper body motion

Sajeewani Karunarathne, M, Ekanayake,S and Pathirana,PN 2014, An adaptive complementary filter for inertial sensor based data fusion to track upper body motion, in Proceedings of the 7th International Conference on Information and Automation for Sustainability: Sharpening the Future with Sustainable Technology; ICIAfS 2014, IEEE, Piscataway, NJ, pp. 1-5.

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Title An adaptive complementary filter for inertial sensor based data fusion to track upper body motion
Author(s) Sajeewani Karunarathne, M
Ekanayake,S
Pathirana,PNORCID iD for Pathirana,PN orcid.org/0000-0001-8014-7798
Conference name Information and Automation for Sustainability Conference (7th : 2014 : Colombo, Sri Lanka)
Conference location Colombo, Sri Lanka
Conference dates 2014/12/22 - 2014/12/24
Title of proceedings Proceedings of the 7th International Conference on Information and Automation for Sustainability: Sharpening the Future with Sustainable Technology; ICIAfS 2014
Publication date 2014
Start page 1
End page 5
Total pages 5
Publisher IEEE
Place of publication Piscataway, NJ
Summary   Remote human activity monitoring is critical and essential in physiotherapy with respect to the skyrocketing healthcare expenditure and the fast aging population. One of frequently used method to monitor human activity is wearing inertial sensors since it is low-cost and accurate. However, the measurements of those sensors are able only to estimate the orientation and rotation angles with respect to actual movement angles, because of differences in the body’s co-ordination system and the sensor’s co-ordination system. There were numerous studies being conducted to improve the accuracy of estimation, though there is potential for further discussions on improving accuracy by replacing heavy algorithms to less complexity. This research is an attempt to propose an adaptive complementary filter for identifying human upper arm movements. Further, this article discusses a feasibility of upper arm rehabilitation using the proposed adaptive complementary filter and inertial measurement sensors. The proposed algorithm is tested with four healthy subjects wearing an inertial sensor against gold standard, which is the VICON system. It demonstrated root mean squared error of 8.77◦ for upper body limb orientation estimation when compared to gold standard VICON optical motion capture system.
ISBN 9781479945986
Language eng
Field of Research 090302 Biomechanical Engineering
090305 Rehabilitation Engineering
090602 Control Systems, Robotics and Automation
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2014, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30071917

Document type: Conference Paper
Collection: School of Engineering
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