Abstract
Inertial sensors are widely used in telerehabilitation systems since they permit to monitor the patient’s movement and determine the position of their limbs. Limbs angle measurement is carried out through the integration of the angular velocity measured by a rate sensor and the decomposition of the components of static gravity acceleration measured by an accelerometer. Different factors derived from the sensors nature, such as the Angle Random Walk (ARW), and dynamic bias lead to erroneous measurements. Dynamic bias 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 LMS and RLS variations are tested with the purpose of finding the best method leading to a more accurate limb angle measurement. An angle wander compensation sensor fusion approach based on Least Mean Squares (LMS) and Recursive Least Squares (RLS) filters has been developed.
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Olivares, A., Górriz, J.M., Ramírez, J., Olivares, G. (2010). Accurate Human Limb Angle Measurement in Telerehabilitation: Sensor Fusion through Kalman, LMS and RLS Adaptive Filtering. In: Augusto, J.C., Corchado, J.M., Novais, P., Analide, C. (eds) Ambient Intelligence and Future Trends-International Symposium on Ambient Intelligence (ISAmI 2010). Advances in Soft Computing, vol 72. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13268-1_12
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DOI: https://doi.org/10.1007/978-3-642-13268-1_12
Publisher Name: Springer, Berlin, Heidelberg
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