Abstract
Inertial sensors are widely used in body movement monitoring systems. Different factors derived from the sensors nature, such as the Angle Random Walk (ARW), and dynamic bias lead to erroneous measurements. Moreover, routines including intense exercises are subject to high dynamic accelerations that distort the angle measurement. Such negative effects can be reduced through the use of adaptive filtering based on sensor fusion concepts. Most existing published works use a Kalman filtering sensor fusion approach. Our aim is to perform a comparative study among different adaptive filters. Several Least Mean Squares (LMS) and Recursive Least Squares (RLS) filters variations are tested with the purpose of finding the best method leading to a more accurate angle measurement. An angle wander compensation and dynamic acceleration bursts filtering method has been developed by the implementing a sensor fusion approach based on LMS and RLS filters.
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Olivares, A., Górriz, J.M., Ramírez, J., Olivares, G. (2010). Sensor Fusion Adaptive Filtering for Position Monitoring in Intense Activities. In: Graña Romay, M., Corchado, E., Garcia Sebastian, M.T. (eds) Hybrid Artificial Intelligence Systems. HAIS 2010. Lecture Notes in Computer Science(), vol 6076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13769-3_59
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DOI: https://doi.org/10.1007/978-3-642-13769-3_59
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