Fangyuan Chang
ABSTRACT
Wearable technologies hold promise for revolutionizing disease management in the cattle farming industry by enabling real-time monitoring of cattle health through continuous physiological and behavioral data collection. However, the practical implementation of these technologies, along with approaches to have a paramount consideration of animal welfare and farmer needs during the process, remain underexplored. This study adopts animal-computer interaction (ACI) principles to address these gaps, with a focus on harmonizing technological advancements with on-ground realities. The paper details the design process of a leg-worn sensor-based system for cattle health monitoring, covering hardware and software facets from ideation to implementation. The contributions of this study extend to both the ACI field and the broader livestock industry. By embracing ACI principles, we showcase the potential of wearable sensor technology to transform cattle health monitoring. The developed leg-worn system exemplifies the integration of ACI-driven design with practical farm management needs, offering a model for the advancement of livestock health and management practices.
- Barkema, H. W., von Keyserlingk, M. A., Kastelic, J. P., Lam, T. J., Luby, C., Roy, J. P., ... & Kelton, D. F. (2015). Invited review: Changes in the dairy industry affecting dairy cattle health and welfare. Journal of dairy science, 98(11), 7426-7445.Google Scholar
Cross Ref
- Garcia, R., Aguilar, J., Toro, M., Pinto, A., & Rodriguez, P. (2020). A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture, 179, 105826.Google ScholarCross Ref
- Neethirajan, S. Recent advances in wearable sensors for animal health management. Sensing and Bio-Sensing Research 12 (2017), 15–29.Google Scholar
- Rutten, C., Velthuis, A., Steeneveld, W., and Hogeveen, H. Invited review: Sensors to support health management on dairy farms. Journal of Dairy Science 96, 4 (2013), 1928–1952.Google Scholar
- Lee, M., & Seo, S. (2021). Wearable wireless biosensor technology for monitoring cattle: A review. Animals, 11(10), 2779.Google ScholarCross Ref
- Zamansky, A., Sinitca, A., van der Linden, D., & Kaplun, D. (2021). Automatic animal behavior analysis: opportunities for combining knowledge representation with machine learning. Procedia Computer Science, 186, 661-668.Google ScholarCross Ref
- Makinde, A., Islam, M. M., & Scott, S. D. (2019, November). Opportunities for ACI in PLF: applying animal-and user-centred design to precision livestock farming. In Proceedings of the Sixth International Conference on Animal-Computer Interaction (pp. 1-6).Google Scholar
- Qiao, Y., Kong, H., Clark, C., Lomax, S., Su, D., Eiffert, S., & Sukkarieh, S. (2021). Intelligent perception-based cattle lameness detection and behaviour recognition: A review. Animals, 11(11), 3033.Google ScholarCross Ref
- Benaissa, S., Tuyttens, F. A., Plets, D., Cattrysse, H., Martens, L., Vandaele, L., ... & Sonck, B. (2019). Classification of ingestive-related cow behaviours using RumiWatch halter and neck-mounted accelerometers. Applied animal behaviour science, 211, 9-16.Google Scholar
- Shen, W., Zhang, A., Zhang, Y., Wei, X., & Sun, J. (2020). Rumination recognition method of dairy cows based on the change of noseband pressure. Information Processing in Agriculture, 7(4), 479-490.Google ScholarCross Ref
- Grinter, L. N., Campler, M. R., & Costa, J. H. C. (2019). Validation of a behavior-monitoring collar's precision and accuracy to measure rumination, feeding, and resting time of lactating dairy cows. Journal of dairy science, 102(4), 3487-3494.Google ScholarCross Ref
- Zambelis, A., Wolfe, T., & Vasseur, E. (2019). Validation of an ear-tag accelerometer to identify feeding and activity behaviors of tiestall-housed dairy cattle. Journal of dairy science, 102(5), 4536-4540.Google ScholarCross Ref
- Aquilani, C., Confessore, A., Bozzi, R., Sirtori, F., & Pugliese, C. (2022). Precision Livestock Farming technologies in pasture-based livestock systems. Animal, 16(1), 100429.Google ScholarCross Ref
- Neethirajan, S. Recent advances in wearable sensors for animal health management. Sensing and Bio-Sensing Research 12 (2017), 15–29.Google Scholar
- Duncan, E. (2018). An exploration of how the relationship between farmers and retailers influences precision agriculture adoption (Doctoral dissertation, University of Guelph).Google Scholar
- Paci, P., Mancini, C., & Nuseibeh, B. (2022). The case for animal privacy in the design of technologically supported environments. Frontiers in Veterinary Science, 8, 1611.Google ScholarCross Ref
- Duncan, E. (2018). An exploration of how the relationship between farmers and retailers influences precision agriculture adoption (Doctoral dissertation, University of Guelph).Google Scholar
- Hostiou, N., Fagon, J., Chauvat, S., Turlot, A., Kling, F., Boivin, X., & Allain, C. (2017). Impact of precision livestock farming on work and human-animal interactions on dairy farms. A review. Bioscience, Biotechnology and Biochemistry, 21, 1-8.Google Scholar
- Pavlovic, D., Davison, C., Hamilton, A., Marko, O., Atkinson, R., Michie, C., ... & Tachtatzis, C. (2021). Classification of cattle behaviours using neck-mounted accelerometer-equipped collars and convolutional neural networks. Sensors, 21(12), 4050.Google ScholarCross Ref
- Benaissa, S., Tuyttens, F. A., Plets, D., Cattrysse, H., Martens, L., Vandaele, L., ... & Sonck, B. (2019). Classification of ingestive-related cow behaviours using RumiWatch halter and neck-mounted accelerometers. Applied animal behaviour science, 211, 9-16.Google Scholar
- Abdiansah, A., & Wardoyo, R. (2015). Time complexity analysis of support vector machines (SVM) in LibSVM. International journal computer and application, 128(3), 28-34.Google ScholarCross Ref
- Vázquez Diosdado, J. A., Barker, Z. E., Hodges, H. R., Amory, J. R., Croft, D. P., Bell, N. J., & Codling, E. A. (2015). Classification of behaviour in housed dairy cows using an accelerometer-based activity monitoring system. Animal Biotelemetry, 3(1), 1-14.Google ScholarCross Ref
- Berckmans, D. (2022). Advances in precision livestock farming. Burleigh Dodds Science Publishing.Google ScholarCross Ref
- Hartung, J., Banhazi, T., Vranken, E., & Guarino, M. (2017). European farmers' experiences with precision livestock farming systems. Animal Frontiers, 7(1), 38-44.Google ScholarCross Ref
- Alipio, M., & Villena, M. L. (2022). Intelligent wearable devices and biosensors for monitoring cattle health conditions: A review and classification. Smart Health, 100369.Google Scholar
- Mancini, C. (2017). Towards an animal-centred ethics for Animal–Computer Interaction. International Journal of Human-Computer Studies, 98, 221-233.Google ScholarDigital Library
- Westerlaken, M., & Gualeni, S. (2016, November). Becoming with: towards the inclusion of animals as participants in design processes. In Proceedings of the Third International Conference on Animal-Computer Interaction (pp. 1-10).Google ScholarDigital Library
- Mancini, C. (2011). Animal-computer interaction: a manifesto. interactions, 18(4), 69-73.Google ScholarDigital Library
- Webber, S., Cobb, M. L., & Coe, J. (2022). Welfare through competence: a framework for animal-centric technology design. Frontiers in veterinary science, 741.Google Scholar
- Paci, P., Mancini, C., & Price, B. A. (2020, July). Understanding the interaction between animals and wearables: The wearer experience of cats. In Proceedings of the 2020 ACM Designing Interactive Systems Conference (pp. 1701-1712).Google ScholarDigital Library
- Abrego-Ulloa, E. R., Aguilar-Lazcano, C. A., Pérez-Espinosa, H., Rodríguez-Vizzuett, L., Hernández-Luquin, M. F., Espinosa-Curiel, I. E., & Escalante, H. J. (2022, December). Towards a monitoring and emergency alarm system activated by the barking of assistant dogs. In Proceedings of the Ninth International Conference on Animal-Computer Interaction (pp. 1-10).Google ScholarDigital Library
- Ricardo Nathaniel Holder, T., Williams, E., Martin, D., Kligerman, A., Summers, E., Cleghern, Z., ... & Bozkurt, A. (2021, November). From Ideation to Deployment: A Narrative Case Study of Citizen Science Supported Wearables for Raising Guide Dogs. In Eight International Conference on Animal-Computer Interaction (pp. 1-13).Google Scholar
- Lawson, S., Kirman, B., Linehan, C., Feltwell, T., & Hopkins, L. (2015, April). Problematising upstream technology through speculative design: the case of quantified cats and dogs. In Proceedings of the 33rd annual ACM conference on human factors in computing systems (pp. 2663-2672).Google Scholar
- Kleinberger, R., Cunha, J., Vemuri, M. M., & Hirskyj-Douglas, I. (2023, April). Birds of a Feather Video-Flock Together: Design and Evaluation of an Agency-Based Parrot-to-Parrot Video-Calling System for Interspecies Ethical Enrichment. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1-16).Google ScholarDigital Library
- Kleinberger, R., Vemuri, M., Sands, J., Sareen, H., & Baker, J. (2022, December). TamagoPhone: A Framework for Augmenting Artificial Incubators to Enable Vocal Interaction Between Bird Parents and Eggs. In Proceedings of the Ninth International Conference on Animal-Computer Interaction (pp. 1-7).Google ScholarDigital Library
- Hirskyj-Douglas, I., & Kankaanpää, V. (2021). Exploring how white-faced sakis control digital visual enrichment systems. Animals, 11(2), 557.Google ScholarCross Ref
- Kleinberger, R., Harrington, A. H., Yu, L., Van Troyer, A., Su, D., Baker, J. M., & Miller, G. (2020, April). Interspecies interactions mediated by technology: An avian case study at the zoo. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-12).Google ScholarDigital Library
- French, F., Mancini, C., & Sharp, H. (2017, November). Exploring research through design in animal computer interaction. In Proceedings of the Fourth International Conference on Animal-Computer Interaction (pp. 1-12).Google ScholarDigital Library
- Rychen, J., Semoroz, J., Eckerle, A., Hahnloser, R. H., & Kleinberger, R. (2022). Full-duplex acoustic interaction system for cognitive experiments with cetaceans. bioRxiv, 2022-05.Google Scholar
- Hirskyj-Douglas, I., Piitulainen, R., & Lucero, A. (2021). Forming the Dog Internet: Prototyping a Dog-to-Human Video Call Device. Proc. ACM Hum. Comput. Interact., 5(ISS), 1-20.Google Scholar
- Karl, S., Boch, M., Zamansky, A., van der Linden, D., Wagner, I. C., Völter, C. J., ... & Huber, L. (2020). Exploring the dog–human relationship by combining fMRI, eye-tracking and behavioural measures. Scientific reports, 10(1), 22273.Google Scholar
- Gemperle, F., Kasabach, C., Stivoric, J., Bauer, M., & Martin, R. (1998, October). Design for wearability. In digest of papers. Second international symposium on wearable computers (cat. No. 98EX215) (pp. 116-122). IEEE.Google ScholarCross Ref
- Chambers, R. D., & Yoder, N. C. (2020). FilterNet: A many-to-many deep learning architecture for time series classification. Sensors, 20(9), 2498.Google ScholarCross Ref
- Valentin, G., Alcaidinho, J., Howard, A., Jackson, M. M., & Starner, T. (2016, September). Creating collar-sensed motion gestures for dog-human communication in service applications. In Proceedings of the 2016 ACM International Symposium on Wearable Computers (pp. 100-107).Google ScholarDigital Library
- Gleerup, K. B., Forkman, B., Otten, N. D., Munksgaard, L., & Andersen, P. H. (2017). Identifying pain behaviors in dairy cattle. WCDS Adv Dairy Technol, 29, 231-239.Google Scholar
Index Terms
- Advancing Cattle Health Monitoring through ACI-Driven Wearable Sensor Technology: A Case Study of Leg-Worn System Development
Recommendations
IoT-Based Cow Health Monitoring System
Computational Science – ICCS 2020AbstractGood health and wellbeing of animals are essential to dairy cow farms and sustainable production of milk. Unfortunately, day-to-day monitoring of animals condition is difficult, especially in large farms where employees do not have enough time to ...
Cattle Care: An IoT Cattle Health Monitoring System (CHMS) for Backyard Dairy Cattle Farmers
CCIOT '23: Proceedings of the 2023 8th International Conference on Cloud Computing and Internet of ThingsCattle health monitoring is not unusual in today's time, there are techniques or practices used to monitor their health conditions consistently to maintain wellness to be able to produce as much milk as they can. Unfortunately, it is laborious and time-...
Development of sound-based poultry health monitoring tool for automated sneeze detection
Highlights- Monitor bioacoustics to detect respiratory problems.
- Algorithm to detect ...
AbstractRespiratory diseases are a major health challenge in meat chicken production. As sneezing is a clinical sign of many respiratory diseases, sound has a great potential in monitoring these diseases. This study focussed on the development ...
Comments