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

التفاصيل البيبلوغرافية
العنوان: Development of a Device and Algorithm Research for Akhal-Teke Activity Level Analysis
المؤلفون: Xuan Chen, Fuzhong Li, Jinxing Li, Qijie Fan, Paul Kwan, Wenxin Zheng, Leifeng Guo
المصدر: Applied Sciences, Vol 14, Iss 13, p 5424 (2024)
بيانات النشر: MDPI AG, 2024.
سنة النشر: 2024
المجموعة: LCC:Technology
LCC:Engineering (General). Civil engineering (General)
LCC:Biology (General)
LCC:Physics
LCC:Chemistry
مصطلحات موضوعية: activity level classification, horse, sensors, threshold analysis, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
الوصف: 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.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2076-3417
Relation: https://www.mdpi.com/2076-3417/14/13/5424; https://doaj.org/toc/2076-3417
DOI: 10.3390/app14135424
URL الوصول: https://doaj.org/article/04b4f7426af745e2bf3d9e602c7e771e
رقم الأكسشن: edsdoj.04b4f7426af745e2bf3d9e602c7e771e
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20763417
DOI:10.3390/app14135424