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      Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion

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          Abstract

          Motion tracking based on commercial inertial measurements units (IMUs) has been widely studied in the latter years as it is a cost-effective enabling technology for those applications in which motion tracking based on optical technologies is unsuitable. This measurement method has a high impact in human performance assessment and human-robot interaction. IMU motion tracking systems are indeed self-contained and wearable, allowing for long-lasting tracking of the user motion in situated environments. After a survey on IMU-based human tracking, five techniques for motion reconstruction were selected and compared to reconstruct a human arm motion. IMU based estimation was matched against motion tracking based on the Vicon marker-based motion tracking system considered as ground truth. Results show that all but one of the selected models perform similarly (about 35 mm average position estimation error).

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          Most cited references114

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          Body Area Networks: A Survey

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            Nonlinear Complementary Filters on the Special Orthogonal Group

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              Quaternion-based extended Kalman filter for determining orientation by inertial and magnetic sensing.

              R Sabatini (2006)
              In this paper, a quaternion based extended Kalman filter (EKF) is developed for determining the orientation of a rigid body from the outputs of a sensor which is configured as the integration of a tri-axis gyro and an aiding system mechanized using a tri-axis accelerometer and a tri-axis magnetometer. The suggested applications are for studies in the field of human movement. In the proposed EKF, the quaternion associated with the body rotation is included in the state vector together with the bias of the aiding system sensors. Moreover, in addition to the in-line procedure of sensor bias compensation, the measurement noise covariance matrix is adapted, to guard against the effects which body motion and temporary magnetic disturbance may have on the reliability of measurements of gravity and earth's magnetic field, respectively. By computer simulations and experimental validation with human hand orientation motion signals, improvements in the accuracy of orientation estimates are demonstrated for the proposed EKF, as compared with filter implementations where either the in-line calibration procedure, the adaptive mechanism for weighting the measurements of the aiding system sensors, or both are not implemented.
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                Author and article information

                Contributors
                Role: Academic Editor
                Role: Academic Editor
                Role: Academic Editor
                Role: Academic Editor
                Role: Academic Editor
                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                01 June 2017
                June 2017
                : 17
                : 6
                : 1257
                Affiliations
                [1 ]TeCIP Institute, Scuola Superiore Sant’Anna, 56127 Pisa, Italy; emanuele.ruffaldi@ 123456santannapisa.it
                [2 ]German Research Center for Artificial Intelligence, 67663 Kaiserslautern, Germany; nagilode@ 123456gmail.com (N.S.); gabriele.bleser@ 123456dfki.de (G.B.); Didier.Stricker@ 123456dfki.de (D.S.)
                [3 ]Junior research group wearHEALTH, Department of Computer Science, University of Kaiserslautern, 67663 Kaiserslautern, Germany; miezal@ 123456cs.uni-kl.de
                Author notes
                [* ]Correspondence: a.filippeschi@ 123456santannapisa.it ; Tel.: +39-50-882-552
                Article
                sensors-17-01257
                10.3390/s17061257
                5492902
                28587178
                aac9938c-cb2e-4581-8739-b1ad0c614b4c
                © 2017 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 28 March 2017
                : 24 May 2017
                Categories
                Review

                Biomedical engineering
                kinematics,sensor fusion,motion tracking,inertial measurements units
                Biomedical engineering
                kinematics, sensor fusion, motion tracking, inertial measurements units

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