Inertial and magnetic sensors are commonly used to determine orientation as they do not rely on a line of sight [1, 2]. There are many different techniques to fuse inertial measurement unit (IMU) data and obtain useful rotational data [1–3]. This study uses two separate data fusion techniques; a direction cosine matrix-based (DCM) technique and a quaternion-based Extended Kalman Filter (EKF) technique [1–3]. These techniques were altered based on performance metrics to weight sensor data when certain sensors proved not as reliable as others . IMU sensors were tested on a hand mannequin and filters were developed using MATLAB software. Simulation results displayed a root-mean-squared error of less than .06° for each rotation angle. Experimental results maintained errors of less than 8° in each rotation angle.
Filtering Techniques for Accurate Identification of Clinician Hand Posture
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Hodes, AT, Weiner, CP, Fang, H, Kieweg, SK, & Wilson, SE. "Filtering Techniques for Accurate Identification of Clinician Hand Posture." Proceedings of the ASME 2015 International Mechanical Engineering Congress and Exposition. Volume 3: Biomedical and Biotechnology Engineering. Houston, Texas, USA. November 13–19, 2015. V003T03A070. ASME. https://doi.org/10.1115/IMECE2015-52290
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