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Wearable Sensors-Based Intelligent Sensing and Application of Animal Behaviors: A Comprehensive Review

文献类型： 外文期刊

作者： Ding, Luyu 1 ; Zhang, Chongxian 1 ; Yue, Yuxiao 1 ; Yao, Chunxia 1 ; Li, Zhuo 1 ; Hu, Yating 1 ; Yang, Baozhu 1 ; Ma, Weihong 1 ; Yu, Ligen 1 ; Gao, Ronghua 1 ; Li, Qifeng 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China

2.Natl Engn Res Ctr Informat Technol Agr NERCITA, Beijing 100097, Peoples R China

3.Natl Innovat Ctr Digital Technol Anim Husb, Beijing 100097, Peoples R China

4.Huazhong Agr Univ, Coll Engn, Wuhan 430070, Peoples R China

5.Jilin Agr Univ, Coll Informat Technol, Changchun 130118, Peoples R China

关键词： behavior monitoring; contact sensing; algorithms; tiny machine learning; monitoring applications

期刊名称：SENSORS （ 影响因子：3.5； 五年影响因子：3.7 ）

ISSN：

年卷期： 2025 年 25 卷 14 期

页码：

收录情况： SCI

摘要： Accurate monitoring of animal behaviors enables improved management in precision livestock farming (PLF), supporting critical applications including health assessment, estrus detection, parturition monitoring, and feed intake estimation. Although both contact and non-contact sensing modalities are utilized, wearable devices with embedded sensors (e.g., accelerometers, pressure sensors) offer unique advantages through continuous data streams that enhance behavioral traceability. Focusing specifically on contact sensing techniques, this review examines sensor characteristics and data acquisition challenges, methodologies for processing behavioral data and implementing identification algorithms, industrial applications enabled by recognition outcomes, and prevailing challenges with emerging research opportunities. Current behavior classification relies predominantly on traditional machine learning or deep learning approaches with high-frequency data acquisition. The fundamental limitation restricting advancement in this field is the difficulty in maintaining high-fidelity recognition performance at reduced acquisition rates, particularly for integrated multi-behavior identification. Considering that the computational demands and limited adaptability to complex field environments remain significant constraints, Tiny Machine Learning (Tiny ML) could present opportunities to guide future research toward practical, scalable behavioral monitoring solutions. In addition, algorithm development for functional applications post behavior recognition may represent a critical future research direction.

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