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Filtering Techniques for Accurate Identification of Clinician Hand Posture

[+] Author Affiliations
Alex T. Hodes, Huazhen Fang, Sarah K. Kieweg, Sara E. Wilson

University of Kansas, Lawrence, KS

Carl P. Weiner

University of Kansas Medical Center, Kansas City, KS

Paper No. IMECE2015-52290, pp. V003T03A070; 6 pages
doi:10.1115/IMECE2015-52290
From:
  • ASME 2015 International Mechanical Engineering Congress and Exposition
  • Volume 3: Biomedical and Biotechnology Engineering
  • Houston, Texas, USA, November 13–19, 2015
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-5738-0
  • Copyright © 2015 by ASME

abstract

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 [2]. 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.

Copyright © 2015 by ASME

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