A method for full-body pose estimation based on sparse inertial sensors

Acceleration and orientation data were synthesized by finite difference method and forward kinematics algorithm. A multilayer perceptron decoder was designed by combining Transformer Encoder and bidirectional long short-term memory network encoder to solve the shortcomings of whole body attitude estimation by sparse inertial sensor and realize high-precision and real-time whole body attitude reconstruction.

CN119904557BActive Publication Date: 2025-11-21HANGZHOU DIANZI UNIV
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Patent Information

Application Number
CN202411753077.6
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-12-02
Publication Date
2025-11-21
Estimated Expiration
2044-12-02

AI Technical Summary

Technical Problem

Existing whole-body attitude estimation methods based on sparse inertial sensors are insufficient in terms of motion diversity and user comfort, and have high computational complexity and management costs.

Method used

Acceleration and orientation data are synthesized using the finite difference method and forward kinematics algorithm. An encoder based on a multilayer perceptron is designed by combining a Transformer Encoder and a bidirectional long short-term memory network. The whole-body posture is estimated by sparse inertial sensors, and joint rotation is estimated in stages using a human kinematics model.

Benefits of technology

It improves the robustness and prediction accuracy of attitude estimation, generates high-quality animation effects in real time, overcomes the defects of sparse inertial input, and enhances the real-time performance and accuracy of the system.

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Abstract

The application discloses a whole body posture estimation method based on a sparse inertial sensor. The application comprises the following steps: 1: synthesizing acceleration data, direction data and angular velocity data through a finite difference method and a forward kinematics algorithm; 2: performing alignment processing on original acceleration data and original direction data collected by three inertial sensors worn on the user's head joint, left wrist joint and right wrist joint through a rotation transformation matrix; 3: extracting deep features of inertial motion data samples by combining a Transformer Encoder and a bidirectional long short-term memory network encoder, designing a decoder based on a multilayer perceptron to calculate the positions and relative rotations of joints, and combining a human body kinematics model to estimate the relative rotations of joints in different parts in multiple stages to realize whole body posture estimation. The application can effectively overcome the defects of using sparse inertial input for whole body motion reconstruction in the existing posture estimation method, and effectively improve the robustness and prediction accuracy of the system.
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