AI-Enabled Sensor Systems for Animal Behavior Tracking and Environmental Insights

Session details:

Tracking animal behavior presents unique challenges due to the complexity of movement patterns, environmental variables, and the need for continuous monitoring. At Villanova University, our research explored how modern sensor technologies—ranging from camera-based observation to wireless tracking devices—can be combined with AI-driven analytics to transform raw behavioral data into actionable insights.

This session will share lessons learned from designing and deploying sensor-driven solutions for monitoring animal activity, highlighting how multi-sensor data (optical, motion, and positional inputs) was integrated to identify behavioral patterns with higher accuracy and less manual intervention. We will also discuss how intelligent sensing and edge AI techniques reduced the data-processing burden, enabled faster feedback loops, and supported long-term studies with constrained resources.

The talk will connect these findings to broader applications of intelligent sensing, from wildlife research and agricultural monitoring to smart city systems that rely on real-time environmental data. By examining both the technical considerations (sensor selection, IoT connectivity, energy efficiency) and the analytical methods (machine learning models, behavioral data pipelines), attendees will gain a practical understanding of how sensor fusion and AI can drive innovation in life sciences and beyond.

This case study demonstrates how cross-disciplinary approaches—merging engineering, AI, and behavioral science—can push the boundaries of what sensing technologies achieve in real-world environments.