Outfitting wildlife with tracking devices has long been used by animal researchers to study wild animals behavior. Using tracking devices and other bio-loggers is become increasingly popular among scientists studying the movement and patterns of domestic cats.
Despite their widespread popularity, the activity patterns and behavioral rhythms of cats remain under-researched and poorly understood. This lack of understanding can lead to misinterpreting normal feline behavior, potentially causing missed health issues. Emerging technologies, such as bio-loggers and accelerometers, are beginning to shed light on the hidden aspects of a cat’s daily life, offering promising tools for better understanding and caring for our feline friends.
Domestic cat behavior is poorly researched
Unlike dogs, which generally have a more predictable daily routine aligned with human schedules, cats are more driving by their biological rhythms. For instance, many cats are naturally crepuscular, meaning they are most active during dawn and dusk.
Cats are also masters at concealing discomfort or illness, which can delay medical intervention until symptoms become severe. Subtle changes in behavior—such as reduced grooming, altered eating habits, or changes in activity levels—can signal health issues, but these signs are often missed by owners. Understanding what constitutes “normal” behavior for a specific cat is crucial for identifying deviations that may indicate underlying problems.
How bio-loggers and artificial intelligence are being used to understand cat behavior
Bio-loggers are small devices attached to a cat’s collar or harness that monitor activity levels and patterns. Among these, accelerometers have proven to be particularly effective in studying feline behavior. By measuring changes in movement across one or more axes, accelerometers provide a continuous record of a cat’s activity. When paired with Artificial Intelligence (AI) tools, these devices can classify behaviors such as eating, walking, grooming, and even jumping.
Early studies using bio-loggers have already provided insights into cats’ biological rhythms. For example, domestic cats with limited outdoor access often synchronize their activity patterns with their human caregivers, showing peaks of activity in the morning and evening. In contrast, free-roaming or feral cats exhibit a bimodal activity pattern aligned with prey availability, weather conditions, and predator presence.
How AI is transforming cat behavior analysis
Machine learning models can now analyze accelerometer data to differentiate between various behaviors with increasing accuracy. For instance, studies have successfully classified behaviors like grooming, walking, and eating based on movement patterns. AI models have even been able to identify specific actions, such as jumping, with minimal misclassification.
Benefits of data tracking domestic cats
Detailed activity reports could help cat owners monitor their pets’ health and detect subtle behavioral changes that might indicate illness. For example, a decrease in grooming—a critical marker of feline well-being—could be flagged early, allowing for timely veterinary intervention.
References
Garcia, S. P., & Chebly, A. (2024). Accelerometers Contribution to the Knowledge of Domestic Cats’(Felis catus) Behavior: A Comprehensive Review. Applied Animal Behaviour Science, 106287. DOI: 10.1016/j.applanim.2024.106287
