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An adaptive complementary filter for inertial sensor based data fusion to track upper body motion
conference contribution
posted on 2014-01-01, 00:00 authored by Sajeewani Maddumage, Samitha Ekanayake, Pubudu PathiranaPubudu PathiranaRemote 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.
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Event
Information and Automation for Sustainability Conference (7th : 2014 : Colombo, Sri Lanka)Pagination
1 - 5Publisher
IEEELocation
Colombo, Sri LankaPlace of publication
Piscataway, NJStart date
2014-12-22End date
2014-12-24ISBN-13
9781479945986Language
EnglishPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2014, IEEETitle of proceedings
Proceedings of the 7th International Conference on Information and Automation for Sustainability: Sharpening the Future with Sustainable Technology; ICIAfS 2014Usage metrics
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