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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 sow and 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. 

Abstract Exposure to climate hazards is increasing, and the experiences of frontline communities warrant meaningful and urgent attention towards how to mitigate, manage, and adapt to hazards. We report results from a community-engaged pilot (November 2021–June 2022) ofN= 30 participants in four frontline communities of the San Francisco Bay Area, California, USA. The study region is an area where low-income, non-English-speaking residents are inequitably exposed and vulnerable to wildfire smoke, extreme heat, and other climate hazards. Building from a yearslong partnership of researchers, community organizations, and community members, we report the feasibility of a project piloting (1) instruments to monitor indoor air quality, temperature, and participant sleep health, and (2) interventions to improve indoor air quality and support protective behaviors. Data collection included experience-based survey data (via in-person administered surveys and a smartphone application) and interviews about heat and air quality, as well as data from an air monitoring protocol. Results cover the prevalence of hazard exposure and protective actions among participants. We discuss throughout methods for conducting and evaluating a community-engaged pilot, particularly by using a community ambassador program. Implications include the feasibility of community-engaged research projects, including discussion of resources required to accomplish this work.