AI in Sustainable Pig Farming: IoT Insights into Stress and Gait

This dataset, code, and accompanying visualizations are supplementary materials for the research paper titled "AI in Sustainable Pig Farming: IoT Insights into Stress and Gait". The study presents a pioneering exploration of the environmental impacts of livestock farming, with a specific focus on the intersection of pig farming, climate change, and sustainability. The research emphasizes the transformative potential of data-driven Artificial Intelligence (AI) methodologies, specifically the Internet of Things (IoT) and multimodal data analysis, in fostering equitable and sustainable food systems. The study observes five pigs, aged 86 to 108 days, using a tripartite sensor that records heart rate, respiration rate, and accelerometer data. The unique experimental design alternates between periods of isolation during feeding and subsequent pairing, enabling the investigation of stress-induced changes. The data and code provided here allow for the exploration of key inquiries, including discerning patterns in heart rate data during isolation versus paired settings, fluctuations in respiration rates, and behavioral shifts induced by isolation or pairing. The study also explores potential detection of gait abnormalities, correlations between pigs' age and their gait or activity patterns, and the evolution of pigs' walking abilities with age. The data and code allow for the scrutiny of accelerometer data to detect activity changes when pigs are paired, potentially indicating increased stress or aggression. The materials also facilitate the examination of the adaptation of pigs to alternating isolation and pairing over time, and how their heart rate, respiration rate, and activity data reflect this process. The study considers other significant variables, such as time of day and isolation duration, affecting the pigs' physiological parameters. The sensor data is further utilized to identify behavioral patterns during periods of feeding, isolation, or pairing. In conclusion, this study harnesses IoT and multimodal data analysis in a groundbreaking approach to pig welfare research. The supplementary materials provided here underscore the compelling potential of technology to inform about overall pig welfare, particularly stress levels and gait quality, and the power of data-driven insights in fostering equitable, healthy, and environmentally conscious livestock production systems