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Development of a Device and Algorithm Research for Akhal-Teke Activity Level Analysis

文献类型： 外文期刊

作者： Chen, Xuan 1 ; Li, Fuzhong 1 ; Li, Jinxing 2 ; Fan, Qijie 1 ; Kwan, Paul 4 ; Zheng, Wenxin 5 ; Guo, Leifeng 2 ;

作者机构： 1.Shanxi Agr Univ, Sch Software, Jinzhong 030801, Peoples R China

2.Chinese Acad Agr Sci, Agr Informat Inst, Beijing 100081, Peoples R China

3.Xinjiang Agr Univ, Coll Comp & Informat Engn, Urumqi 830052, Peoples R China

4.CQUniv Brisbane, Sch Engn & Technol, 160 Ann St, Brisbane, Qld 4000, Australia

5.Xinjiang Acad Anim Sci, Inst Anim Husb Qual Stand, Urumqi 830011, Peoples R China

关键词： activity level classification; horse; sensors; threshold analysis

期刊名称：APPLIED SCIENCES-BASEL （ 影响因子：2.5； 五年影响因子：2.7 ）

ISSN：

年卷期： 2024 年 14 卷 13 期

页码：

收录情况： SCI

摘要： Featured Application This research introduces a novel wearable device that uses an acceleration threshold behavior recognition method to classify horse activities into three levels: low (standing), medium (walking), and high (trotting, cantering, and galloping). The recognition algorithm is directly implemented in the hardware, which horses wear during their training sessions. This device allows for the real-time analysis of horse activity levels and the accurate calculation of the time spent in each activity state. This method provides scientific data support for horse training, facilitating the optimization of training programs.Abstract This study demonstrated that wearable devices can distinguish between different levels of horse activity, categorized into three types based on the horse's gaits: low activity (standing), medium activity (walking), and high activity (trotting, cantering, and galloping). Current research in activity level classification predominantly relies on deep learning techniques, known for their effectiveness but also their demand for substantial data and computational resources. This study introduces a combined acceleration threshold behavior recognition method tailored for wearable hardware devices, enabling these devices to classify the activity levels of horses directly. The approach comprises three sequential phases: first, a combined acceleration interval counting method utilizing a non-linear segmentation strategy for preliminary classification; second, a statistical analysis of the variance among these segments, coupled with multi-level threshold processing; third, a method using variance-based proximity classification for recognition. The experimental results show that the initial stage achieved an accuracy of 87.55% using interval counting, the second stage reached 90.87% with variance analysis, and the third stage achieved 91.27% through variance-based proximity classification. When all three stages are combined, the classification accuracy improves to 92.74%. Extensive testing with the Xinjiang Wild Horse Group validated the feasibility of the proposed solution and demonstrated its practical applicability in real-world scenarios.

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