An Adaptive-Gain Complementary Filter for Real-Time Human Motion Tracking With MARG Sensors in Free-Living Environments

High-resolution, real-time data obtained by human motion tracking systems can be used for gait analysis, which helps better understanding the cause of many diseases for more effective treatments, such as rehabilitation for outpatients or recovery from lost motor functions after a stroke. In order to...

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Vydáno v:	IEEE transactions on neural systems and rehabilitation engineering Ročník 21; číslo 2; s. 254 - 264
Hlavní autoři:	Tian, Ya, Wei, Hongxing, Tan, Jindong
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.03.2013 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Acceleration Actigraphy - instrumentation Adaptive-gain complementary filter Algorithms angular rate and gravity (MARG) sensors Computer Systems Distortion Earth Equipment Design Equipment Failure Analysis Estimation Feedback Gauss-Newton optimization algorithm (GNA) Gyroscopes Human motion human motion capture (MoCap) Humans magnetic Magnetic sensors Magnetometers Magnetometry - instrumentation Mathematical analysis Movement - physiology Orientation quaternion-based orientation Real time Rehabilitation Reproducibility of Results Sensitivity and Specificity Sensors Signal Processing, Computer-Assisted - instrumentation Studies Telemetry - instrumentation Vectors
ISSN:	1534-4320, 1558-0210, 1558-0210
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Abstract	High-resolution, real-time data obtained by human motion tracking systems can be used for gait analysis, which helps better understanding the cause of many diseases for more effective treatments, such as rehabilitation for outpatients or recovery from lost motor functions after a stroke. In order to achieve real-time ambulatory human motion tracking with low-cost MARG (magnetic, angular rate, and gravity) sensors, a computationally efficient and robust algorithm for orientation estimation is critical. This paper presents an analytically derived method for an adaptive-gain complementary filter based on the convergence rate from the Gauss-Newton optimization algorithm (GNA) and the divergence rate from the gyroscope, which is referred as adaptive-gain orientation filter (AGOF) in this paper. The AGOF has the advantages of one iteration calculation to reduce the computing load and accurate estimation of gyroscope measurement error. Moreover, for handling magnetic distortions especially in indoor environments and movements with excessive acceleration, adaptive measurement vectors and a reference vector for earth's magnetic field selection schemes are introduced to help the GNA find more accurate direction of gyroscope error. The features of this approach include the accurate estimation of the gyroscope bias to correct the instantaneous gyroscope measurements and robust estimation in conditions of fast motions and magnetic distortions. Experimental results are presented to verify the performance of the proposed method, which shows better accuracy of orientation estimation than several well-known methods.
AbstractList	High-resolution, real-time data obtained by human motion tracking systems can be used for gait analysis, which helps better understanding the cause of many diseases for more effective treatments, such as rehabilitation for outpatients or recovery from lost motor functions after a stroke. In order to achieve real-time ambulatory human motion tracking with low-cost MARG (magnetic, angular rate, and gravity) sensors, a computationally efficient and robust algorithm for orientation estimation is critical. This paper presents an analytically derived method for an adaptive-gain complementary filter based on the convergence rate from the Gauss-Newton optimization algorithm (GNA) and the divergence rate from the gyroscope, which is referred as adaptive-gain orientation filter (AGOF) in this paper. The AGOF has the advantages of one iteration calculation to reduce the computing load and accurate estimation of gyroscope measurement error. Moreover, for handling magnetic distortions especially in indoor environments and movements with excessive acceleration, adaptive measurement vectors and a reference vector for earth's magnetic field selection schemes are introduced to help the GNA find more accurate direction of gyroscope error. The features of this approach include the accurate estimation of the gyroscope bias to correct the instantaneous gyroscope measurements and robust estimation in conditions of fast motions and magnetic distortions. Experimental results are presented to verify the performance of the proposed method, which shows better accuracy of orientation estimation than several well-known methods. High-resolution, real-time data obtained by human motion tracking systems can be used for gait analysis, which helps better understanding the cause of many diseases for more effective treatments, such as rehabilitation for outpatients or recovery from lost motor functions after a stroke. In order to achieve real-time ambulatory human motion tracking with low-cost MARG (magnetic, angular rate, and gravity) sensors, a computationally efficient and robust algorithm for orientation estimation is critical. This paper presents an analytically derived method for an adaptive-gain complementary filter based on the convergence rate from the Gauss-Newton optimization algorithm (GNA) and the divergence rate from the gyroscope, which is referred as adaptive-gain orientation filter (AGOF) in this paper. The AGOF has the advantages of one iteration calculation to reduce the computing load and accurate estimation of gyroscope measurement error. Moreover, for handling magnetic distortions especially in indoor environments and movements with excessive acceleration, adaptive measurement vectors and a reference vector for earth's magnetic field selection schemes are introduced to help the GNA find more accurate direction of gyroscope error. The features of this approach include the accurate estimation of the gyroscope bias to correct the instantaneous gyroscope measurements and robust estimation in conditions of fast motions and magnetic distortions. Experimental results are presented to verify the performance of the proposed method, which shows better accuracy of orientation estimation than several well-known methods.High-resolution, real-time data obtained by human motion tracking systems can be used for gait analysis, which helps better understanding the cause of many diseases for more effective treatments, such as rehabilitation for outpatients or recovery from lost motor functions after a stroke. In order to achieve real-time ambulatory human motion tracking with low-cost MARG (magnetic, angular rate, and gravity) sensors, a computationally efficient and robust algorithm for orientation estimation is critical. This paper presents an analytically derived method for an adaptive-gain complementary filter based on the convergence rate from the Gauss-Newton optimization algorithm (GNA) and the divergence rate from the gyroscope, which is referred as adaptive-gain orientation filter (AGOF) in this paper. The AGOF has the advantages of one iteration calculation to reduce the computing load and accurate estimation of gyroscope measurement error. Moreover, for handling magnetic distortions especially in indoor environments and movements with excessive acceleration, adaptive measurement vectors and a reference vector for earth's magnetic field selection schemes are introduced to help the GNA find more accurate direction of gyroscope error. The features of this approach include the accurate estimation of the gyroscope bias to correct the instantaneous gyroscope measurements and robust estimation in conditions of fast motions and magnetic distortions. Experimental results are presented to verify the performance of the proposed method, which shows better accuracy of orientation estimation than several well-known methods.
Author	Wei, Hongxing Tan, Jindong Tian, Ya
Author_xml	– sequence: 1 givenname: Ya surname: Tian fullname: Tian, Ya email: tya@mtu.edu organization: Department of Mechanical, Aerospace and Biomedical Engineering, the University of Tennessee, Knoxville, TN, USA – sequence: 2 givenname: Hongxing surname: Wei fullname: Wei, Hongxing email: weihongxing@buaa.edu.cn organization: School of Mechanical Engineering and Automation, Beihang University, Beijing, China – sequence: 3 givenname: Jindong surname: Tan fullname: Tan, Jindong email: tan@utk.edu organization: Department of Mechanical, Aerospace and Biomedical Engineering, The University of Tennessee, Knoxville, TN, USA
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SubjectTerms	Acceleration Actigraphy - instrumentation Adaptive-gain complementary filter Algorithms angular rate and gravity (MARG) sensors Computer Systems Distortion Earth Equipment Design Equipment Failure Analysis Estimation Feedback Gauss-Newton optimization algorithm (GNA) Gyroscopes Human motion human motion capture (MoCap) Humans magnetic Magnetic sensors Magnetometers Magnetometry - instrumentation Mathematical analysis Movement - physiology Orientation quaternion-based orientation Real time Rehabilitation Reproducibility of Results Sensitivity and Specificity Sensors Signal Processing, Computer-Assisted - instrumentation Studies Telemetry - instrumentation Vectors
Title	An Adaptive-Gain Complementary Filter for Real-Time Human Motion Tracking With MARG Sensors in Free-Living Environments
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