A method and collar for pet posture recognition and health monitoring

By combining a lightweight classification model with temporal fusion processing of acceleration and angular velocity features, accurate identification and health assessment of cat postures are achieved in low-power devices. This solves the problems of misjudgment and insufficient closed-loop feedback in existing posture recognition algorithms, and provides accurate health monitoring information.

CN122296260APending Publication Date: 2026-06-30SHENZHEN SMART TRAVELER TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SHENZHEN SMART TRAVELER TECH CO LTD
Filing Date
2026-04-30
Publication Date
2026-06-30

AI Technical Summary

Technical Problem

Existing posture recognition technology lacks optimization for cat-specific behavioral characteristics, making it difficult to operate efficiently in low-power embedded devices and failing to achieve closed-loop feedback for health management.

Method used

A lightweight classification model is adopted, combined with temporal fusion processing of acceleration and angular velocity features. Attitude recognition is performed using triaxial acceleration data and triaxial angular velocity data, and periodic health status assessment is conducted to achieve a complete closed loop from attitude acquisition to health assessment.

Benefits of technology

It improves the accuracy of posture recognition and the timeliness of health assessment, reduces the system's dependence on external computing power, adapts to the battery life requirements of low-power devices, and provides accurate health reference information.

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Abstract

This invention discloses a pet posture recognition and health monitoring method and collar, belonging to the field of pet wearable devices and embedded intelligent sensing technology. The method includes: collecting raw motion data of the target pet and preprocessing it; extracting acceleration and angular velocity features based on the preprocessed data to obtain temporal fusion features; inputting the temporal fusion features into a preset lightweight classification model to output the pet's posture category; performing periodic quantitative statistics on the posture categories to obtain behavioral statistics data; and comparing the behavioral statistics data with preset health judgment conditions to obtain relevant health status data of the target pet. This invention effectively distinguishes between effective and ineffective motion through temporal fusion features, achieves local offline inference through a lightweight classification model, and realizes pet health monitoring through a complete closed loop from posture recognition to health assessment. It features high recognition accuracy, low power consumption, and long battery life.
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