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Sparse imu real-time human motion capture and joint force prediction method and system

A technology of human motion and joints, applied in the field of human motion capture, can solve problems such as hindering human motion

Active Publication Date: 2022-06-24
TSINGHUA UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] This application provides a sparse IMU real-time human motion capture and joint force prediction method and system to solve the problem of hindering human motion by binding a large number of sensors for human motion capture and joint force prediction

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  • Sparse imu real-time human motion capture and joint force prediction method and system
  • Sparse imu real-time human motion capture and joint force prediction method and system

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Embodiment Construction

[0059] Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary, and are intended to be used to explain the present application, but should not be construed as a limitation to the present application.

[0060] The following describes the method and system for sparse IMU real-time human motion capture and joint force prediction according to the embodiments of the present application with reference to the accompanying drawings. In view of the problem mentioned by the above-mentioned background technology center that needs to bind a large number of sensors for human motion capture and joint force prediction, thereby hindering human movement, the present application provides...

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Abstract

This application relates to the technical field of human motion capture, in particular to a sparse IMU real-time human motion capture and joint force prediction method and system, the method includes: collecting the left wrist, right wrist, left knee lower part, right knee lower part, Inertial data of the head and waist, get the bone orientation and acceleration of the bone in the human coordinate system, input the bone orientation and acceleration into the preset human kinematics model, predict the human body posture, motion, and human-ground contact information and input Based on the preset human dynamics model, it predicts human posture, movement, joint force and ground force, and solves the problem of hindering human movement by binding a large number of sensors for human motion capture and joint force prediction. The dual PD controller connects the human kinematics model and the human dynamics model, and integrates them into an overall sparse inertial motion capture and force prediction system, realizing reliable real-time human motion capture and joint force prediction.

Description

technical field [0001] The present application relates to the technical field of human motion capture, and in particular, to a sparse IMU (Inertial Measurement Unit, inertial measurement unit) method and system for real-time human motion capture and joint force prediction. Background technique [0002] Human motion capture and human joint force prediction have important applications in film production, motion analysis, game production, medical diagnosis and rehabilitation. With the development and popularization of IMU technology, more and more works try to use pure inertial sensors for motion capture. These methods can avoid the problem of restricting the motion range of the human body in optical motion capture, and can capture high-speed, large-scale motion without being affected by light and occlusion. [0003] In related technologies, it is necessary to use precise instruments, professional equipment or bind a large number of sensors (more than 10) to collect informatio...

Claims

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Application Information

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IPC IPC(8): G06F30/27G06N3/04G06N3/08G06F119/14
CPCG06F30/27G06N3/08G06F2119/14G06N3/044
Inventor 徐枫伊昕宇
Owner TSINGHUA UNIV
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