121
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Estimating Three-Dimensional Orientation of Human Body Parts by Inertial/Magnetic Sensing

      review-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          User-worn sensing units composed of inertial and magnetic sensors are becoming increasingly popular in various domains, including biomedical engineering, robotics, virtual reality, where they can also be applied for real-time tracking of the orientation of human body parts in the three-dimensional (3D) space. Although they are a promising choice as wearable sensors under many respects, the inertial and magnetic sensors currently in use offer measuring performance that are critical in order to achieve and maintain accurate 3D-orientation estimates, anytime and anywhere. This paper reviews the main sensor fusion and filtering techniques proposed for accurate inertial/magnetic orientation tracking of human body parts; it also gives useful recipes for their actual implementation.

          Related collections

          Most cited references76

          • Record: found
          • Abstract: found
          • Article: not found

          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.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Three-axis attitude determination from vector observations

              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers

              The use of on-body wearable sensors is widespread in several academic and industrial domains. Of great interest are their applications in ambulatory monitoring and pervasive computing systems; here, some quantitative analysis of human motion and its automatic classification are the main computational tasks to be pursued. In this paper, we discuss how human physical activity can be classified using on-body accelerometers, with a major emphasis devoted to the computational algorithms employed for this purpose. In particular, we motivate our current interest for classifiers based on Hidden Markov Models (HMMs). An example is illustrated and discussed by analysing a dataset of accelerometer time series.
                Bookmark

                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel, Switzerland)
                Molecular Diversity Preservation International (MDPI)
                1424-8220
                2011
                26 January 2011
                : 11
                : 2
                : 1489-1525
                Affiliations
                The BioRobotics Institute, Scuola Superiore Sant’Anna, Piazza Martiri della Libertà 33, 56124 Pisa, Italy; E-Mail: sabatini@ 123456sssup.it ; Tel.: +39-050-883-415; Fax: +39-050-883-101
                Article
                sensors-11-01489
                10.3390/s110201489
                3274035
                22319365
                f97d849f-24ce-41d3-ac98-3a3403388f04
                © 2011 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 license ( http://creativecommons.org/licenses/by/3.0/).

                History
                : 15 December 2010
                : 13 January 2011
                : 15 January 2011
                Categories
                Review

                Biomedical engineering
                quaternion,inertial/magnetic sensing,sensor fusion,kalman filtering,human body motion tracking,strap-down inertial navigation

                Comments

                Comment on this article