Using interval particle filtering for marker less 3D human motion capture

“…Kim et al [6] and Lee et al [7] employed a particle filter to perform both body detection and motion feature computation for estimating human posture. Saboune and Charpillet [8] presented an Interval Particle Filtering (IPF) algorithm to track human movements efficiently. To enhance accuracy, some studies integrated the training model into the particle filter.…”

Section: Introductionmentioning

confidence: 99%

3D human motion tracking based on a progressive particle filter

Chang

Lin

2010

Pattern Recognition

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“…By measuring the lower limbs of healthy volunteers of a certain species, the anatomical characteristics and movement parameters of the knee were obtained, which can provide reference for the design and optimization of artificial knee prosthesis in line with the characteristics of this species. With the development of motion capture technology [4][5][6], Some scholars carry out motion capture measurement of human gait [7][8][9], Some scholars have made motion capture measurement and analysis of human knee joint squatting [10][11], and some scholars use the motion capture system to measure and analyze the motion characteristics of the lower extremity joints during the stair climbing process [12][13][14][15], most of them use the commercial motion capture systems such as Visual3D (C-Motion, USA) and Vicon (Oxford Metrics Limited, UK) to calculate and analyze the kinematic parameters of the human knee joint. This method needs to be manually processed one by one, which is time-consuming and labor-intensive [16].…”

Section: Introductionmentioning

confidence: 99%

A Processing Method for Kinematics Data of Human Knee Joint Obtained by Motion-Capture Measurement

Wang

Zhao

et al. 2020

Preprint

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Background: The data obtained by motion capture method is many and complex, so it is the key to find an efficient method to process the data. Therefore, to develop a processing method for kinematics data of human knee joint by programming on MATLAB sofware. Methods: The coordinate data of markers on human lower limb measured by motion capture system were firstly read and repaired through the program. Then the coordinate data of anatomical points in the movement of human lower limb were obtained by program processing. The local coordinate systems of human femur and tibia were established with anatomical points. After that the flexion/extension, abduction/adduction and internal/external rotation of human knee tibiofemoral joint in the movement of lower limb were obtained by coordinate transformation. Lastly, the motion capture and measurement of healthy volunteers were carried out and the MATLAB program was used for data processing.Results: Using the above methods, motion capture measurements and batch data processing were carried out on squatting and ascending stairs of 29 healthy volunteers to obtain the motion characteristics of the knee joint. As follows, the maximum range of internal and external rotation in squatting is 30.5 degrees, and the maximum range of internal and external rotation in climbing stairs is 16.5 degrees, etc., respectively compared with the results processed by other research methods, it is found that their movement results are basically consistent, thus verifying the reliability of our research method.Conclusion: The kinematics data of human knee joint could be processed accurately and effectively with the method by using MATLAB software, and the kinematics characteristics of human knee tibiofemoral joint were obtained. The processing method provides a reference for the designing and optimization of knee prosthesis, and the program can be modified for different purposes. At the same time, it is helpful to study knee joint movement of patients after total knee arthroplasty. Keywords: Knee joint, Motion capture measurement, MATLAB, Kinematics

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Using interval particle filtering for marker less 3D human motion capture

Cited by 17 publications

References 5 publications

Factored Interval Particle Filtering for Gait Analysis

Factored Interval Particle Filtering for Gait Analysis

3D human motion tracking based on a progressive particle filter

A Processing Method for Kinematics Data of Human Knee Joint Obtained by Motion-Capture Measurement

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