An adaptive complementary filter for inertial sensor based data fusion to track upper body motion

Karunarathne, M. Sajeewani; Ekanayake, Samitha W.; Pathirana, Pubudu N.

doi:10.1109/iciafs.2014.7069606

Cited by 17 publications

(12 citation statements)

References 18 publications

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“…Of course, it is slightly inferior to the traditional optical systems in [6,7], but its price is relatively lower and can meet the needs of the public. In [22], the author proposed an adaptive complementary filter for identifying human upper arm movements by replacing the coefficients which can be dynamically bound with a linear relationship to variables to minimize error of angle estimation. It demonstrated root mean squared error of 8.77 ∘ for upper body limb orientation estimation when compared to gold standard VICON optical motion capture system.…”

Section: Resultsmentioning

confidence: 99%

Human Motion Capture Algorithm Based on Inertial Sensors

Chen

Kuang

2016

Journal of Sensors

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On the basis of inertial navigation, we conducted a comprehensive analysis of the human body kinematics principle. From the direction of two characteristic parameters, namely, displacement and movement angle, we calculated the attitude of a node during the human motion capture process by combining complementary and Kalman filters. Then, we evaluated the performance of the proposed attitude strategy by selecting different platforms as the validation object. Results show that the proposed strategy for the real-time tracking of the human motion process has higher accuracy than the traditional strategy.

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Section: Resultsmentioning

confidence: 99%

Human Motion Capture Algorithm Based on Inertial Sensors

Chen

Kuang

2016

Journal of Sensors

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“…The sensor fusion results are compared with the BTS SMART-D optical motion tracker. The research work in Karunarathne et al (2014), used adaptive complementary filter on four subjects on lifting water bottle from body front to mouth. In the proposed method data is fused for finding upper body orientation based on inertial sensor measurements and was capable to combine the gyroscope data and accelerometer data.…”

Section: Evaluation Of Motion Capture System Techniquesmentioning

confidence: 99%

Motion capture sensing techniques used in human upper limb motion: a review

Yahya

Shah

Kadir

et al. 2019

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Purpose Motion capture system (MoCap) has been used in measuring the human body segments in several applications including film special effects, health care, outer-space and under-water navigation systems, sea-water exploration pursuits, human machine interaction and learning software to help teachers of sign language. The purpose of this paper is to help the researchers to select specific MoCap system for various applications and the development of new algorithms related to upper limb motion. Design/methodology/approach This paper provides an overview of different sensors used in MoCap and techniques used for estimating human upper limb motion. Findings The existing MoCaps suffer from several issues depending on the type of MoCap used. These issues include drifting and placement of Inertial sensors, occlusion and jitters in Kinect, noise in electromyography signals and the requirement of a well-structured, calibrated environment and time-consuming task of placing markers in multiple camera systems. Originality/value This paper outlines the issues and challenges in MoCaps for measuring human upper limb motion and provides an overview on the techniques to overcome these issues and challenges.

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“…These technologies have shortcomings such as distortion by parallax error, radiation exposure, cost and the requirement of dedicated laboratory facilities, which restrict use in non-clinical settings. Some investigators have measured limb lengths directly using instruments such as anthropometric callipers and measuring tapes [7][8][9][10][11][12][13]. However, they are cumbersome to use and susceptible to human error especially when determining the pelvic bone of LLD patients [1].…”

Section: Introductionmentioning

confidence: 99%

“…With the advancement in MicroElectroMechanical Sensors (MEMSs), miniaturised and low‐cost sensors which can be packaged as wearable devices are decorously considered for human motion capture [7–11, 13, 14]. More importantly, MEMS sensory devices can be used in non‐clinical settings to provide continuous monitoring for an extended period of time.…”

Section: Introductionmentioning

confidence: 99%

Entropy‐based method to quantify limb length discrepancy using inertial sensors

Maddumage

Pathirana

et al. 2018

IET wirel. sens. syst.

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Limb length is a useful parameter in the assessment of common musculoskeletal disorders such as limb length discrepancy. The measurement variation among rates adversely affects the quantitative aspect of assessments and introduces a greater subjectivity in the course of treatment. Common practise for measuring limb length is based on radiographic imaging techniques which are inconvenient, costly and require clinical knowledge. Direct instruments are difficult to use with patients due to susceptibility to human error in determining the position of the rotational joint. In this study, the determination of limb length is automated using a contemporary algorithm which applies curvature to the measurements from a low-cost and miniaturised inertial sensor, primarily used in the bio-kinematic research. The motion artefacts contribute to the ultimate estimations and, in this approach, a least noise threshold model is employed to address the robustness. The proposed estimation technique was validated with real-data observed from 14 healthy subjects comparing with radiographic and direct measurements. The experimental results indicate greater accuracy compared with manual measurements with low root mean squared error percentages with values ranging from 5.34 to 5.84%. Additionally, the mean limb length difference between our estimator and both radiographic measurements and direct measurement was <1.6 cm.

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An adaptive complementary filter for inertial sensor based data fusion to track upper body motion

Cited by 17 publications

References 18 publications

Human Motion Capture Algorithm Based on Inertial Sensors

Human Motion Capture Algorithm Based on Inertial Sensors

Motion capture sensing techniques used in human upper limb motion: a review

Entropy‐based method to quantify limb length discrepancy using inertial sensors

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