Human modeling and real-time motion reconstruction for micro-sensor motion capture

Tao, Guanhong; Sun, Shuyan; Huang, Shuai; Huang, Zhipei; Wu, Jiankang

doi:10.1109/vecims.2011.6052193

Cited by 13 publications

(5 citation statements)

References 4 publications

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“…Zhang and Zhang [26] proposed an inexpensive human motion capture system prototype using a flexible architecture and distributed computing technology. Tao et al [27] proposed a wearable motion capture microsensor system to capture the motion in real time, then a 3-D model of the human body is employed to reconstruct the motion. The introduction of time-of-flight cameras provided depth cues of the human body, which makes using wearable devices less required for capturing the human motion.…”

Section: A Motion Capture Systemsmentioning

confidence: 99%

Efficient Body Motion Quantification and Similarity Evaluation Using 3-D Joints Skeleton Coordinates

Kamel

Sheng

et al. 2021

IEEE Trans. Syst. Man Cybern, Syst.

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Evaluating whole-body motion is challenging because of the articulated nature of the skeleton structure. Each joint moves in an unpredictable way with uncountable possibilities of movements direction under the influence of one or many of its parent joints. This paper presents a method for human motion quantification via three-dimensional (3-D) body joints coordinates. We calculate a set of metrics that influence the joints movement considering the motion of its parent joints without requiring prior knowledge of the motion parameters. Only the raw joints coordinates data of a motion sequence are needed to automatically estimate the transformation matrix of the joints between frames. We also consider the angles between limbs as a fundamental factor to follow the joints directions. We classify the joints motion as global motion and local motion. The global motion represents the joint movement according to a fixed joint, and the local motion represents the joint movement according to its first parent joint. In order to evaluate the performance of the proposed method, we also propose a comparison algorithm between two skeletons motions based on the quantified metrics. We measured the comparative similarity between the 3-D joints coordinates on Microsoft Kinect V2 and UTD-MHAD dataset. User studies were conducted to evaluate the performance under different factors. Various results and comparisons have shown that our method effectively quantifies and evaluates the motion similarity.

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Section: A Motion Capture Systemsmentioning

confidence: 99%

Efficient Body Motion Quantification and Similarity Evaluation Using 3-D Joints Skeleton Coordinates

Kamel

Sheng

et al. 2021

IEEE Trans. Syst. Man Cybern, Syst.

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“…Positions are analyzed using forward kinematics [12]. To describe the rotation of joints and skeletons in the space, it's essential to define four coordinate systems: Global Coordinate System, Body Coordinate System, Sensor Coordinate System and Model Coordinate System.…”

Section: A Human Modelmentioning

confidence: 99%

Human motion tracking based on complementary Kalman filter

Wang

Yang

Huang

et al. 2017

2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)

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Abstract-Miniaturized Inertial Measurement Unit (IMU) has been widely used in many motion capturing applications. In order to overcome stability and noise problems of IMU, a lot of efforts have been made to develop appropriate data fusion method to obtain reliable orientation estimation from IMU data. This article presents a method which models the errors of orientation, gyroscope bias and magnetic disturbance, and compensate the errors of state variables with complementary Kalman filter in a body motion capture system. Experimental results have shown that the proposed method significantly reduces the accumulative orientation estimation errors.

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“…1) Model Constraints: Current skeleton models used in inertial capture systems are typically comprised of a simple structure of connected joints and segments [75], and each joint can admit three degrees-of-freedom. However, some joints, such as the elbow and knee, cannot rotate freely about three axes, thus geometric constraints should be taken into consideration for processing of processing inertial data.…”

Section: B Processing Techniquesmentioning

confidence: 99%

Wearable Sensing for Solid Biomechanics

Wong

Zhang

et al. 2015

IEEE Sensors J.

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Abstract-Understanding the solid biomechanics of the human body is important to the study of structure and function of the body, which can have a range of applications in healthcare, sport, wellbeing, and workflow analysis. Conventional laboratorybased biomechanical analysis systems and observation-based tests are only designed to capture brief snapshots of the mechanics of movement. With recent developments in wearable sensing technologies, biomechanical analysis can be conducted in less constrained environments, thus allowing continuous monitoring and analysis beyond laboratory settings. In this paper, we review the current research in wearable sensing technologies for biomechanical analysis, focusing upon sensing and analytics that enable continuous, long-term monitoring of kinematics and kinetics in a free-living environment. The main technical challenges, including measurement drift, external interferences, nonlinear sensor properties, sensor placement, and muscle variations that can affect the accuracy and robustness of existing methods, and different methods for reducing the impact of these sources of errors are described in this review. Recent developments in motion estimation in kinematics, mobile force sensing in kinematics, sensor reduction for electromyography, as well as the future direction of sensing for biomechanics are also discussed.

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Human modeling and real-time motion reconstruction for micro-sensor motion capture

Cited by 13 publications

References 4 publications

Efficient Body Motion Quantification and Similarity Evaluation Using 3-D Joints Skeleton Coordinates

Efficient Body Motion Quantification and Similarity Evaluation Using 3-D Joints Skeleton Coordinates

Human motion tracking based on complementary Kalman filter

Wearable Sensing for Solid Biomechanics

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