A joint torque prediction method fusing biomechanical information, medium and device

By optimizing a joint torque prediction neural network model embedded with biomechanical information and a personalized biomechanical model in parallel, the problems of insufficient real-time performance, physical rationality, and generalization ability in existing joint torque prediction technologies are solved, achieving efficient and accurate joint torque prediction to meet the needs of rehabilitation therapy and exercise optimization.

CN122158092APending Publication Date: 2026-06-05SOUTH CHINA UNIV OF TECH
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
SOUTH CHINA UNIV OF TECH
Filing Date
2026-04-23
Publication Date
2026-06-05

AI Technical Summary

Technical Problem

Existing methods for predicting joint torque have significant limitations in terms of real-time performance, physical plausibility, generalization ability, and adaptive adjustment mechanisms, making it difficult to meet the needs of rehabilitation assessment, athlete performance optimization, and assistive device design.

Method used

A joint torque prediction neural network model embedded with biomechanical information is adopted. By parallel optimization of the joint torque prediction neural network model and the personalized biomechanical model, gradient bidirectional backpropagation is achieved. By integrating prior biomechanical knowledge, personalized calibration parameters are constructed to predict joint torque.

Benefits of technology

It achieves efficient and biomechanically sound joint torque prediction, meets the needs of real-time applications, improves generalization ability and robustness, enhances prediction accuracy and personalized fit, and solves the model generalization problem under complex physical constraints.

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Abstract

The present application relates to the technical field of healthcare informatics, and specifically provides a joint torque prediction method, medium and equipment fused with biomechanical information; the method is to predict joint torque by using a joint torque prediction neural network model embedded with biomechanical information; a personalized biomechanical model is embedded in the joint torque prediction neural network model; when training the joint torque prediction neural network model, the joint torque prediction neural network model and the personalized biomechanical model are cooperatively driven and parallelly optimized based on data errors and physical residual errors, and bidirectional back transmission of gradients between the joint torque prediction neural network model and the personalized biomechanical model is realized. The intelligent estimation framework fused with biomechanical prior knowledge realizes efficient and physiologically reasonable torque prediction.
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