Intelligent sports action error correction and feedback system based on multi-mode perception
By dynamically updating the individual capability model through a multimodal perception system and generating an individualized action tolerance domain, the misjudgment problem caused by individual differences and physiological state changes in existing technologies is solved, achieving more accurate error correction feedback and training security.
CN121606872APending Publication Date: 2026-03-06聊城幼儿师范学校
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Patent Information
- Application Number
- CN202511804879.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-03
- Publication Date
- 2026-03-06
AI Technical Summary
Technical Problem
Existing intelligent error correction systems for sports movements cannot dynamically adapt to individual differences and real-time changes in physiological state, leading to increased misjudgments and training risks.
Method used
A multimodal perception system is adopted, which integrates physiological data and action feature data, uses a latent variable observation model and an online Bayesian filtering algorithm to dynamically update the individual ability model, generate an individualized action tolerance domain, and provide error correction feedback.
Benefits of technology
It enables dynamic adaptation to individual differences and physiological conditions of athletes, reduces misjudgments, provides more accurate error correction feedback, and reduces training risks.
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Figure CN121606872A_ABST
Abstract
The invention discloses a multi-modal perception-based intelligent error correction and feedback system for sports actions, which relates to the technical field of sports training assistance and comprises a data interface module, a storage module, a capability estimation module, a capability updating module and a decision and instruction transmission module, the ability estimation module calls a latent variable observation model in the storage module to calculate a real-time ability observation vector, the ability updating module updates an individual ability model through online Bayesian filtering to obtain an individualized ability variable, and the decision making module generates an individualized action tolerance domain based on the variable and individual attribute data; an error correction instruction is generated when the comparison action data exceed an allowable domain, and the instruction transmission module sends the error correction instruction to a receiving end to prompt a user to adjust; the method has the advantages that the method can dynamically adapt to individual differences and real-time physiological state changes, the misjudgment phenomenon is reduced, and more accurate error correction feedback is provided.
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