Ambulatory Position and Orientation Tracking Fusing Magnetic and Inertial Sensing

By Daniel Roetenberg, Per J. Slycke, Peter H. Veltink. IEEE Transactions on Biomedical Engineering, 2007

369 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/25721950092296f4ef4dfd441bcc222ec12ae584

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Citations in the corpus

Comparative study on classifying human activities with miniature inertial and magnetic sensors (2010, methodology): “Fusing the data of inertial sensors and magnetometers is also reported in the literature [38,46,47].”
Quantified self and human movement: a review on the clinical impact of wearable sensing and feedback for gait analysis and intervention. (2014, background): “The small size and light weight of accelerometers, gyroscopes, and magnetometers make these a convenient and practical choice for mobile measurements, and the combined packaging of accelerometers and gyroscopes in an inertial measurement unit [6] or accelerometers, gyroscopes, and magnetometers in a”
Complementary Observer for Body Segments Motion Capturing by Inertial and Magnetic Sensors (2014, positive): “This triad of sensors is used also in [15], [16] to develop an Extended Kalman Filter (EKF).”
Xsens MVN: Full 6DOF Human Motion Tracking Using Miniature Inertial Sensors (2009, background): “By combining the inertial estimates with other body worn aiding systems, such as a acoustic [9] or a magnetic tracker [10], unbound integration drift can be prevented.”
Toward Pervasive Gait Analysis With Wearable Sensors: A Systematic Review (2016, methodology): “Signal processing techniques, such as high-pass filtering [87], complementary filtering [88], and Kalman filtering [89]–[91], have been used to remove the drift.”
Separating Movement and Gravity Components in an Acceleration Signal and Implications for the Assessment of Human Daily Physical Activity (2013, methodology): “The use of gyroscopes in addition to acceleration sensors could be regarded as the solution for separating the gravitational component from the acceleration signal [14,15,16].”
A review on vision techniques applied to Human Behaviour Analysis for Ambient-Assisted Living (2012, methodology): “It also mentions some other papers in which vision and the mentioned sensors are used in hybrid systems [114], or as a method to check the correct classification when using only data from IMSUs, as in [3, 75, 97].”
Recognizing Daily and Sports Activities in Two Open Source Machine Learning Environments Using Body-Worn Sensor Units (2014, methodology): “Fusing data from inertial sensors and magnetometers are also reported in the literature [21, 28, 29].”
Human Activity Recognition Using Inertial/Magnetic Sensor Units (2010, background): “Fusion of inertial sensors with magnetometers is also reported in the literature [18,21].”
Detecting Falls with Wearable Sensors Using Machine Learning Techniques (2014, methodology): “A commonly used approach is to fix various sensors to the environment, such as cameras, acoustic, pressure, vibration, force, infrared sensors, lasers, Radio Frequency Identification (RFID) tags, inertial sensors and magnetometers [6,7].”
SHIMMER™ – A Wireless Sensor Platform for Noninvasive Biomedical Research (2010, background): “Wireless sensor applications have demonstrated the ability to monitor a variety of multiple parameters including physiological [7], [8], kinematics [9], [10], ambient [11], [12], and en-”
Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion (2017, methodology): “The motion reconstruction algorithm also benefits from the work of Schepers, Roetenberg and Slycke on the exploitation of disturbed magnetic field signals [99,112].”