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High-performance flexible wearable electronics for sheep physiological information wireless sensing and health assessment

文献类型： 外文期刊

作者： Yin, Maosong 1 ; Ma, Ruiqin 1 ; Huang, Wentao 1 ; Chi, Fumin 2 ; Zhao, Qinan 3 ; Zhang, Mengjie 1 ;

作者机构： 1.China Agr Univ, Beijing 100083, Peoples R China

2.Tibet Agr & Anim Husb Univ, Nyingchi 860000, Peoples R China

3.Inner Mongolia Acad Agr & Anim Husb Sci, Hohhot 010000, Peoples R China

关键词： Flexible electronics; Wearable multi-sensor; Health precise tracking; Supervised learning; Farm animals

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）

ISSN： 0168-1699

年卷期： 2025 年 231 卷

页码：

收录情况： SCI

摘要： Accurately tracking the physiological information of farm animals is an important step in achieving precision livestock farming, and animal wearable devices provide a convenient and practical way to monitor and manage animals. To overcome the problem of the large size and heavy mass of rigid wearables, the objective of this paper is to develop high-performance flexible wearable electronics (HFWE) for sheep farming. This system integrates multiple sensors and is designed to be compact, lightweight, and comfortable to wear. It collects body temperature, blood oxygen saturation and step count and transmits the data to a wireless smart terminal. Additionally, a sheep health assessment model was constructed based on Analytic Hierarchy Process (AHP) and supervised learning networks (GA-BPNN, PSO-BPNN). The results demonstrated that: (1) Through simulation and actual testing, the HFWE has excellent flexibility and robustness (2) The HFWE performs well in continuously collecting physiological data, and the stability and coefficient of variation (CV) of blood oxygen saturation are (93.5 +/- 0.9) % and 1.8 %, respectively. The stability and CV of body temperature reach (37.1 +/- 0.2) degrees C and 0.6 %, respectively. (3) AHP model provides a basis for dividing health labels. In terms of GA-BPNN and PSOBPNN model, although the two optimization strategies differed, both achieved an overall accuracy of over 97 %. This significantly enhances transparency in animal health status, and has the potential to safeguard animal welfare. Therefore, this study provides theoretical and technical support for physiological detection and health assessment of farm animals, which is conducive to the sustainable development of the precision livestock industry.

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