PigSense: Structural vibration-based activity and health monitoring system for pigs

Authors: DONG, YIWEN - Stanford University; BONDE, AMELIE - Carnegie Mellon University; CODLING, JESSE - University Of Michigan; BANNIS, ADEOLA - Carnegie Mellon University; CAO, JINPU - Stanford University; MACON, AYSA - University Of Nebraska; Rohrer, Gary; Miles, Jeremy; SHARMA, SUDHENDU - University Of Nebraska; BROWN-BRANDL, TAMI - University Of Nebraska; SANGPETCH, AKKARIT - Carnegie Mellon University; SANGPETCH, ORATHAI - Carnegie Mellon University; ZHANG, PEI - University Of Michigan; NOH, HAEYOUNG - Stanford University

Submitted to: ACM Transactions on Sensor Networks
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/6/2023
Publication Date: 10/18/2023
Citation: Dong, Y., Bonde, A., Codling, J.R., Bannis, A., Cao, J., Macon, A., Rohrer, G., Miles, J., Sharma, S., Brown-Brandl, T., Sangpetch, A., Sangpetch, O., Zhang, P., Noh, H. 2023. PigSense: Structural vibration-based activity and health monitoring system for pigs. ACM Transactions on Sensor Networks. 20(1):1-43. https://doi.org/10.1145/3604806.
DOI: https://doi.org/10.1145/3604806

Interpretive Summary: A robust monitoring system, named PigSense, was developed based on vibration data collected from the flooring of pig pens. The system developed is durable enough to withstand the adverse environmental conditions under the flooring of a commercial swine barn. Extensive analysis and evaluation for all-round swine activities and scenarios from a 6-month field deployment across 2 pig farms in Thailand and USA in farrowing and nursery pens was conducted. PigSense uses physical knowledge of the structural vibration characteristics caused by pig activity to recognize different behaviors of the sow and piglets. Behaviors identified by PigSense include nursing, sleeping and active movement of piglets and can monitor postural changes of the sow. To assess the risk of crushing and sickness, PigSense achieves an average of 97.8% and 94% for sow posture and motion monitoring, respectively, and an average of 96% and 71% for eating and excretion behaviors of the sow. To help farmers monitor piglet feeding, starvation, and illness, PigSense achieves an average of 87.7%, 89.4%, and 81.9% in predicting different levels of nursing, sleeping, and being active, respectively. In addition, we show that our monitoring of signal energy changes, allows timely status tracking during the farrowing process and can detect farrowing issues. Our observation also demonstrates the sow nesting activity can be used to effectively predict farrowing. Furthermore, PigSense also predicts the daily pattern and weight gain during lactation with 89% accuracy, a metric that can be used to monitor the piglets’ growth progress. PigSense can effectively monitor animal activity and inform caretakers of potential issues to improve animal well-being in commercial production barns.

Technical Abstract: Precision Swine Farming has the potential to directly benefit swine health and industry profit by automatically monitoring the growth and health of pigs. We introduce the first system to use structural vibration to track animals and the first system for automated characterization of piglet group activities, including nursing, sleeping, and active times. PigSense uses physical knowledge of the structural vibration characteristics caused by pig-activity-induced load changes to recognize different behaviors of the sowand piglets. For our system to survive the harsh environment of the farrowing pen for three months, we designed simple, durable sensors for physical fault tolerance, then installed many of them, pooling their data to achieve algorithmic fault tolerance even when some do stop working. The key focus of this work was to create a robust system that can withstand challenging environments, has limited installation and maintenance requirements, and uses domain knowledge to precisely detect a variety of swine activities in noisy conditions while remaining flexible enough to adapt to future activities and applications. We provided an extensive analysis and evaluation of all-round swine activities and scenarios from our one-year field deployment across two pig farms in Thailand and the USA. To help assess the risk of crushing, farrowing sicknesses, and poor maternal behaviors, PigSense achieves an average of 97.8% and 94% for sow posture and motion monitoring, respectively, and an average of 96% and 71% for ingestion and excretion detection. To help farmers monitor piglet feeding, starvation, and illness, PigSense achieves an average of 87.7%, 89.4%, and 81.9% in predicting different levels of nursing,sleeping, and being active, respectively. In addition, we show that our monitoring of signal energy changes allows the prediction of farrowing in advance, as well as status tracking during the farrowing process and on the occasion of farrowing issues. Furthermore, PigSense also predicts the daily pattern and weight gain in the lactation cycle with 89% accuracy, a metric that can be used to monitor the piglets’ growth progress over the lactation cycle.