Wearable Sensors-Based Intelligent Sensing and Application of Animal Behaviors: A Comprehensive Review
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 u...
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MDPI AG
2025-07-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/14/4515 |
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| author | Luyu Ding Chongxian Zhang Yuxiao Yue Chunxia Yao Zhuo Li Yating Hu Baozhu Yang Weihong Ma Ligen Yu Ronghua Gao Qifeng Li |
| author_facet | Luyu Ding Chongxian Zhang Yuxiao Yue Chunxia Yao Zhuo Li Yating Hu Baozhu Yang Weihong Ma Ligen Yu Ronghua Gao Qifeng Li |
| author_sort | Luyu Ding |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d091b33a14c340c088d0caa83e1d58c7 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-d091b33a14c340c088d0caa83e1d58c72025-08-20T02:47:10ZengMDPI AGSensors1424-82202025-07-012514451510.3390/s25144515Wearable Sensors-Based Intelligent Sensing and Application of Animal Behaviors: A Comprehensive ReviewLuyu Ding0Chongxian Zhang1Yuxiao Yue2Chunxia Yao3Zhuo Li4Yating Hu5Baozhu Yang6Weihong Ma7Ligen Yu8Ronghua Gao9Qifeng Li10Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaAccurate 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.https://www.mdpi.com/1424-8220/25/14/4515behavior monitoringcontact sensingalgorithmstiny machine learningmonitoring applications |
| spellingShingle | Luyu Ding Chongxian Zhang Yuxiao Yue Chunxia Yao Zhuo Li Yating Hu Baozhu Yang Weihong Ma Ligen Yu Ronghua Gao Qifeng Li Wearable Sensors-Based Intelligent Sensing and Application of Animal Behaviors: A Comprehensive Review Sensors behavior monitoring contact sensing algorithms tiny machine learning monitoring applications |
| title | Wearable Sensors-Based Intelligent Sensing and Application of Animal Behaviors: A Comprehensive Review |
| title_full | Wearable Sensors-Based Intelligent Sensing and Application of Animal Behaviors: A Comprehensive Review |
| title_fullStr | Wearable Sensors-Based Intelligent Sensing and Application of Animal Behaviors: A Comprehensive Review |
| title_full_unstemmed | Wearable Sensors-Based Intelligent Sensing and Application of Animal Behaviors: A Comprehensive Review |
| title_short | Wearable Sensors-Based Intelligent Sensing and Application of Animal Behaviors: A Comprehensive Review |
| title_sort | wearable sensors based intelligent sensing and application of animal behaviors a comprehensive review |
| topic | behavior monitoring contact sensing algorithms tiny machine learning monitoring applications |
| url | https://www.mdpi.com/1424-8220/25/14/4515 |
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