Small Ruminant Parturition Detection Based on Inertial Sensors-A Review

Simple Summary Animal births, much like human births, can encounter complications that threaten the well-being of both the mother and the offspring. While monitoring the birth process is essential for ensuring proper care, human supervision can be costly. Commercial systems do exist for large animals, but there are currently no comparable solutions for small ruminants, despite various research efforts. This study explores the application of inertial sensors to detect parturition in small ruminants. This study also highlights which factors most significantly influence the outcomes of each investigation and summarizes the main results regarding birth detection. The review shows that different approaches focus on recognizing particular animal behaviors and designing detection algorithms. Although none of the studies presented a completely reliable detection method, most produced encouraging results, indicating noticeable behavioral changes in the hours preceding birth.Abstract The birth process in animals, much like in humans, can encounter complications that pose significant risks to both offspring and mothers. Monitoring these events can provide essential nursing support, but human monitoring is expensive. Although there are commercial monitoring systems for large ruminants, there are no effective solutions for small ruminants, despite various attempts documented in the literature. Inertial sensors are very convenient given their low cost, low impact on animal life, and their flexibility for monitoring animal behavior. This study offers a systematic review of the literature on detecting parturition in small ruminants using inertial sensors. The review analyzed the specifics of published research, including data management and monitoring processes, behaviors indicative of parturition, processing techniques, detection algorithms, and the main results achieved in each study. The results indicated that some methods for detecting birth concentrate on classifying unique animal behaviors, employing diverse processing techniques, and developing detection algorithms. Furthermore, this study emphasized that employing techniques that include analyzing animal activity peaks, specifically recurrent lying down and getting up occurrences, could result in improved detection precision. Although none of the studies provided a completely valid detection algorithm, most results were promising, showing significant behavioral changes in the hours preceding delivery.