Dance treatment posture correction method based on biological feedback

By constructing a closed-loop system for multimodal data acquisition and physiological state quantification, the problems of subjectivity in posture guidance and feedback lag in dance therapy have been solved, achieving high-precision, real-time posture correction and improving the scientific nature and personalized adaptability of dance therapy.

CN120809065APending Publication Date: 2025-10-17CHANGSHA NORMAL UNIV
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Patent Information

Application Number
CN202510867302.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-25
Publication Date
2025-10-17

AI Technical Summary

Technical Problem

Current dance therapy methods often suffer from highly subjective posture guidance, delayed feedback mechanisms, and a lack of personalized adaptation strategies, leading to large diagnostic errors and inaccurate feedback, making it difficult to meet the demand for efficient and personalized posture correction.

Method used

A closed-loop system based on biofeedback is constructed, which achieves accurate identification, assessment and real-time intervention of posture state by combining deep neural networks and individual adaptive learning through multimodal data acquisition, posture deviation modeling, physiological state quantification and feedback regulation control.

Benefits of technology

It achieves high-precision, real-time posture correction feedback, improves the scientific and intelligent level of dance therapy, adapts to individual differences and dynamically adjusts intervention strategies, thereby improving correction effects and user experience.

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Abstract

The invention relates to a dance treatment posture correction method based on biological feedback, and belongs to the technical field of intelligent rehabilitation and action behavior correction. According to the method, posture data and physiological parameters of a dance participant are synchronously collected through a wearable device, a posture deviation evaluation model is constructed by using a deep neural network, and a physiological state weight adjusting function is constructed by combining heart rate, skin electricity and electromyographic signals; multi-mode feedback control instructions such as directional vibration, voice prompt or augmented reality visual guidance are dynamically generated and act on key joint parts in real time, and high-precision and self-adaptive correction of dancing postures is achieved. The system has an individualized learning function, can update an individualized model based on historical data, and improves the accuracy and robustness of attitude intervention. The method can be widely applied to scenes such as dance training, autism rehabilitation and nerve injury posture reconstruction, and has the advantages of being high in response speed, scientific in feedback mechanism, high in adaptability and the like.
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