Human motion capture algorithm for creating digital shadows of the movement process

Obukhov, Artem; Volkov, Andrey; Vekhteva, Nadezhda; Teselkin, Daniil; Arkhipov, Alexey E

doi:10.1088/1742-6596/2388/1/012033

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…A solution is proposed based on this: because the correlation value is affected by phase shift, when transmitting a set of signals with different phase deviations, the receiver can distinguish which pulse has the highest correlation value by the size of the correlation value, and then find the best capture point. [6]…”

Section: Methodsmentioning

confidence: 99%

Multi-pulse joint acquisition algorithm

Peng,

Zhang,

Han

2024

Fourth International Conference on Digital Signal and Computer Communications (DSCC 2024)

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This paper investigates the impact of phase deviation between the transmitter and receiver on the correlation value when capturing in the GMSK modulation mode, and proposes a multi pulse joint capture algorithm that adds different phases to the captured pulse. This algorithm adds different phase offsets to multiple captured pulses during transmission based on the original GMSK related capture. At the same time, quantize the sampling signal of the receiver, fit a set of captured correlation values to the data, and find the phase shift position with the highest correlation value. And it is believed that this is the best capture point, further reducing the capture error during capture and improving the accuracy of capture time.

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Section: Methodsmentioning

confidence: 99%

Multi-pulse joint acquisition algorithm

Peng,

Zhang,

Han

2024

Fourth International Conference on Digital Signal and Computer Communications (DSCC 2024)

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“…Обученные нейронные сети и проект, реализующий интеграцию предложенной концепции по реконструкции модели тела человека, размещен в репозитории GitHub 1 .…”

Section: результаты экспериментальных исследованийunclassified

“…На текущем этапе развития систем виртуальной реальности (VR) одной из актуальных проблем является точное воспроизведение тела пользователя в виртуальном пространстве [1]. Под виртуальным пространством подразумевается смоделированная среда, имитирующая реальное или вымышленное окружение посредством трехмерных моделей, анимации, звуковых и визуальных эффектов, а также обеспечивающее пользователю возможность взаимодействия с виртуальными объектами с помощью специального оборудования, например систем VR [2].…”

Section: Introductionunclassified

Optimization of human tracking systems in virtual reality based on a neural network approach

Obukhov,

Teselkin

2023

Naučno-teh. vestn. inf. tehnol. meh. opt.

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The problem of determining the optimal number and location of tracking points on the human body to ensure the necessary accuracy of reconstruction of kinematic parameters of human movements in virtual space is considered. Optimization of the human tracking system in virtual reality has been performed to reduce the amount of transmitted information, computational load and cost of motion capture systems by reducing the number of physical sensors. The task of optimizing the number and location of tracking points on the human body necessary for the reconstruction of a virtual body model from a limited set of input points using numerical approximation of the regression function is set. An algorithm has been developed for collecting a large amount of data from a human body model in a virtual scene and from a motion capture suit in the real world. The smallest number of human body tracking points and their location were obtained using the proposed algorithm. Various neural network topologies have been trained and tested to approximate the regression relationship between a vector of tracking points limited in size (from 3 to 13) and a vector of 18 virtual points used for the complete reconstruction of the human body model. The necessary accuracy of reconstruction of kinematic parameters of human movements is provided at 5 and 7 input points. The proposed approach made it possible to use 5 or 7 physical sensors to build a model of the human body and restore the kinetic parameters of its movements in virtual reality. The approach can be applied to solving inverse kinematics problems in order to reduce the number of physical sensors placed on the surface of the object under study, to simplify the processing and transmission of information. By combining data from both the motion capture suit and the virtual avatar, the process of collecting information has been significantly accelerated, the volume of the training sample has been expanded and various patterns of user body movements have been modeled. Keywords virtual reality, human movement tracking, inverse kinematics, optimization of motion tracking and capture systems, digital representation of a person

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Modeling of Nonlinear Dynamic Processes of Human Movement in Virtual Reality Based on Digital Shadows

et al. 2023

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In virtual reality (VR) systems, a problem is the accurate reproduction of the user’s body in a virtual environment using inverse kinematics because existing motion capture systems have a number of drawbacks, and minimizing the number of key tracking points (KTPs) leads to a large error. To solve this problem, it is proposed to use the concept of a digital shadow and machine learning technologies to optimize the number of KTPs. A technique for movement process data collecting from a virtual avatar is implemented, modeling of nonlinear dynamic processes of human movement based on a digital shadow is carried out, the problem of optimizing the number of KTP is formulated, and an overview of the applied machine learning algorithms and metrics for their evaluation is given. An experiment on a dataset formed from virtual avatar movements shows the following results: three KTPs do not provide sufficient reconstruction accuracy, the choice of five or seven KTPs is optimal; among the algorithms, the most efficient in descending order are AdaBoostRegressor, LinearRegression, and SGDRegressor. During the reconstruction using AdaBoostRegressor, the maximum deviation is not more than 0.25 m, and the average is not more than 0.10 m.

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Human motion capture algorithm for creating digital shadows of the movement process

Cited by 3 publications

References 8 publications

Multi-pulse joint acquisition algorithm

Multi-pulse joint acquisition algorithm

Optimization of human tracking systems in virtual reality based on a neural network approach

Modeling of Nonlinear Dynamic Processes of Human Movement in Virtual Reality Based on Digital Shadows

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