Exploiting kinematic constraints to compensate magnetic disturbances when calculating joint angles of approximate hinge joints from orientation estimates of inertial sensors
Laidig D, Schauer T, Seel T (2017)
Publication Type: Conference contribution
Publication year: 2017
Journal
Publisher: IEEE Computer Society
Pages Range: 971-976
Conference Proceedings Title: IEEE International Conference on Rehabilitation Robotics
Event location: London
ISBN: 9781538622964
DOI: 10.1109/ICORR.2017.8009375
Abstract
Inertial Measurement Units (IMUs) have become a widely used tool for rehabilitation and other application domains in which human motion is analyzed using an ambulatory or wearable setup. Since the magnetic field is inhomogeneous in indoor environments and in the proximity of ferromagnetic material, standard orientation estimation and joint angle calculation algorithms often lead to inaccurate or even completely wrong results. One approach to circumvent this is to exploit the kinematic constraint that is induced by mechanical hinge joints and also by approximate hinge joints such as the knee joint and the finger (interphalangeal) joints of the human body. We propose a quaternion-based method for joint angle measurement for approximate hinge joints moving through inhomogeneous magnetic fields. The method exploits the kinematic constraint to compensate the error that the magnetic disturbances induce in the IMU orientation estimates. This is achieved by realtime estimation and correction of the relative heading (azimuth) error that is caused by the disturbance. Since the kinematic constraint does not allow heading correction when the joint axis is vertical, we extend the proposed method such that it improves accuracy and robustness when the joint is close to that singularity. We evaluate the method by simulations of a quick hand motion and study the effect of inaccurate sensor-to-segment (anatomical) calibration and joint constraint relaxations. As a main result, the proposed method is found to reduce the root-mean-square error of the joint angle from 25.8° to 2.6° in the presence of large magnetic disturbances.
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Germany (DE)How to cite
APA:
Laidig, D., Schauer, T., & Seel, T. (2017). Exploiting kinematic constraints to compensate magnetic disturbances when calculating joint angles of approximate hinge joints from orientation estimates of inertial sensors. In Arash Ajoudani, Panagiotis Artemiadis, Philipp Beckerle, Giorgio Grioli, Olivier Lambercy, Katja Mombaur, Domen Novak, Georg Rauter, Carlos Rodriguez Guerrero, Gionata Salvietti, Farshid Amirabdollahian, Sivakumar Balasubramanian, Claudio Castellini, Giovanni Di Pino, Zhao Guo, Charmayne Hughes, Fumiya Iida, Tommaso Lenzi, Emanuele Ruffaldi, Fabrizio Sergi, Gim Song Soh, Marco Caimmi, Leonardo Cappello, Raffaella Carloni, Tom Carlson, Maura Casadio, Martina Coscia, Dalia De Santis, Arturo Forner-Cordero, Matthew Howard, Davide Piovesan, Adriano Siqueira, Frank Sup, Masia Lorenzo, Manuel Giuseppe Catalano, Hyunglae Lee, Carlo Menon, Stanisa Raspopovic, Mo Rastgaar, Renaud Ronsse, Edwin van Asseldonk, Bram Vanderborght, Madhusudhan Venkadesan, Matteo Bianchi, David Braun, Sasha Blue Godfrey, Fulvio Mastrogiovanni, Andrew McDaid, Stefano Rossi, Jacopo Zenzeri, Domenico Formica, Nikolaos Karavas, Laura Marchal-Crespo, Kyle B. Reed, Nevio Luigi Tagliamonte, Etienne Burdet, Angelo Basteris, Domenico Campolo, Ashish Deshpande, Venketesh Dubey, Asif Hussain, Vittorio Sanguineti, Ramazan Unal, Glauco Augusto de Paula Caurin, Yasuharu Koike, Stefano Mazzoleni, Hyung-Soon Park, C. David Remy, Ludovic Saint-Bauzel, Nikos Tsagarakis, Jan Veneman, Wenlong Zhang (Eds.), IEEE International Conference on Rehabilitation Robotics (pp. 971-976). London, GB: IEEE Computer Society.
MLA:
Laidig, Daniel, Thomas Schauer, and Thomas Seel. "Exploiting kinematic constraints to compensate magnetic disturbances when calculating joint angles of approximate hinge joints from orientation estimates of inertial sensors." Proceedings of the 2017 International Conference on Rehabilitation Robotics, ICORR 2017, London Ed. Arash Ajoudani, Panagiotis Artemiadis, Philipp Beckerle, Giorgio Grioli, Olivier Lambercy, Katja Mombaur, Domen Novak, Georg Rauter, Carlos Rodriguez Guerrero, Gionata Salvietti, Farshid Amirabdollahian, Sivakumar Balasubramanian, Claudio Castellini, Giovanni Di Pino, Zhao Guo, Charmayne Hughes, Fumiya Iida, Tommaso Lenzi, Emanuele Ruffaldi, Fabrizio Sergi, Gim Song Soh, Marco Caimmi, Leonardo Cappello, Raffaella Carloni, Tom Carlson, Maura Casadio, Martina Coscia, Dalia De Santis, Arturo Forner-Cordero, Matthew Howard, Davide Piovesan, Adriano Siqueira, Frank Sup, Masia Lorenzo, Manuel Giuseppe Catalano, Hyunglae Lee, Carlo Menon, Stanisa Raspopovic, Mo Rastgaar, Renaud Ronsse, Edwin van Asseldonk, Bram Vanderborght, Madhusudhan Venkadesan, Matteo Bianchi, David Braun, Sasha Blue Godfrey, Fulvio Mastrogiovanni, Andrew McDaid, Stefano Rossi, Jacopo Zenzeri, Domenico Formica, Nikolaos Karavas, Laura Marchal-Crespo, Kyle B. Reed, Nevio Luigi Tagliamonte, Etienne Burdet, Angelo Basteris, Domenico Campolo, Ashish Deshpande, Venketesh Dubey, Asif Hussain, Vittorio Sanguineti, Ramazan Unal, Glauco Augusto de Paula Caurin, Yasuharu Koike, Stefano Mazzoleni, Hyung-Soon Park, C. David Remy, Ludovic Saint-Bauzel, Nikos Tsagarakis, Jan Veneman, Wenlong Zhang, IEEE Computer Society, 2017. 971-976.
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