Signal-based feature analysis of behavioral trajectories for predicting calving time and classifying assistance needs

• Vision-based, non-invasive system for predicting calving time and assistance needs. • Analyzes cattle trajectory data from four surveillance cameras without wearable sensors. • Applies Amplitude, Frequency, and Power Spectral Density (PSD) analyses behavioral movement signals. • Achieves 100 % accuracy in calving time prediction without any model training. • Classifies assistance need with up to 65 % accuracy in real farm environments. Accurately predicting calving time and recognizing when a cow needs help during delivery are essential for effective livestock management. These factors directly influence animal welfare, how labor is distributed on the farm, and overall productivity. Without close monitoring, calving complications can lead to serious health issues or even death for the cattle. Moreover, delayed assistance during difficult births (dystocia) can significantly harm both the cow and the calf. These problems remain challenging due to the subtle and highly variable nature of cattle behavior, especially within large-scale farming environments where continuous manual monitoring is impractical. This research proposes a fully vision-based, non-invasive system that relies solely on cattle trajectory data derived from images to address these challenges. To analyze signal-based behavioral trajectories associated with calving, we applied three signal-based image processing techniques aimed at predicting calving time and identifying individuals likely to require human assistance during parturition. Our system allows for continuous, automated monitoring using four surveillance cameras eliminating the need for wearable sensors or invasive equipment. We employed three analytical approaches such as amplitude analysis, frequency analysis, and power spectral density analysis (PSD) to interpret cattle movement patterns from camera-derived trajectory data. For predicting calving time, our system achieved 100 % accuracy across all methods. Specifically, the amplitude analysis predicted calving within 9 h, the frequency analysis provided predictions within 5 h, and the PSD analysis predicted calving within 6 h. Moreover, in classifying cattle requiring human assistance during parturition, our system achieved accuracy of 60 %, 60 %, and 65 % for the amplitude, frequency, and PSD analyses, respectively. Unlike conventional methods that rely on wearable sensors, manual observation, or AI models requiring extensive training, our prediction system operates without any model training phase, instead directly analyzing motion patterns from trajectory data to generate predictions. This makes our prediction simpler, more interpretable, and highly scalable, offering a practical and robust solution for improving livestock monitoring and timely intervention in modern farming environments. This work paves the way for further development of automated, non-invasive livestock monitoring technologies.