How AI Can Help You Understand Your Dog’s Behaviour
You know the look that means “walk?” You can hear a snack packet open from the other end of the house and predict exactly who will appear. You probably have several hundred photos of the same sleeping face.
Knowing your dog is one of the best parts of loving them. But even the most devoted dog person cannot watch every moment or remember every change in their routine.
AI can help make those patterns easier to see. Paired with suitable sensors and properly tested software, it can recognise certain behaviours, compare observations over time and highlight changes worth a closer look. That offers a useful extra layer of understanding alongside everything you already notice.
How can a collar recognise dog behaviour?
Some smart collars contain an accelerometer: a sensor that measures movement. Software analyses patterns in the readings to estimate what a dog is doing.
To develop a behaviour recognition model, researchers can match sensor recordings with observations, such as video showing a dog walking, resting or scratching. The model learns patterns associated with those labelled examples, then estimates the behaviour behind new readings.
Research has demonstrated that collar sensors and machine learning can identify particular canine behaviours. However, performance varies by behaviour and system. Results from one study cannot establish how well a different collar works.[1][2]
That is why “powered by AI” is only the beginning of the explanation. What matters is what the system has been trained to recognise and how it performs on dogs outside its training data.
What turns a reading into an insight?
A reading tells you something was measured. An insight helps you understand why that information might deserve your attention.
Imagine a collar estimates that your dog was less active today. On its own, that leaves plenty of questions. Was today different from their usual Tuesday? Did you skip a walk because of the rain? Was the collar charging for part of the afternoon?
An AI system could compare the available readings with previous days and highlight a repeated change. Your own observations help fill in the context.
For example, a hypothetical insight might say: “Recorded afternoon activity has been lower than usual on several recent days. Has your dog’s routine changed?”
That is a more useful starting point than a number without explanation. It still leaves room for uncertainty and for what you know about your dog.
Recognising behaviour and predicting problems are different
In AI, “prediction” can mean estimating what a dog is doing right now from sensor data. It does not always mean forecasting what will happen next.
Recognising a movement pattern, identifying a change from a dog’s routine and predicting a future health condition are separate tasks. Each needs its own evidence.
A system might flag unusual activity without knowing the cause. A claim that it can predict illness would need specific testing for that condition, including evidence about missed cases and false alarms. Behaviour recognition research alone does not prove that capability.
Can AI tell how my dog feels?
A movement signal cannot, by itself, tell the full story of an emotion. More movement might happen during play, excitement or difficulty settling. Less movement might occur during a quiet afternoon or for another reason entirely.
Your dog’s body language, surroundings and recent experiences matter. If their behaviour changes suddenly or you notice changes in eating, drinking, sleeping or social interactions, seek veterinary advice. Notes and videos can help explain what you have observed.[3]
AI can support that conversation. It cannot replace an examination or your relationship with your dog.
Where Floofe fits in
Floofe is being developed around a simple ambition: help you get more understanding from your dog’s everyday patterns.
Because another graph is only useful if it helps you notice something, ask a better question or feel more connected to the dog sharing your life. Our approach is to pair behaviour data with AI insights, while being clear about what those insights can tell you.
You already bring the love, the attention and the camera roll. We want to help you see more of the everyday story.
Get to know Floofe and follow our progress towards more useful insights into life with your dog.
Frequently asked questions
Do all smart collars recognise the same behaviours?
No. Sensors, software and testing vary. Check the specific behaviours supported by a product rather than assuming every AI collar offers the same capabilities.
Can an AI collar diagnose my dog?
Behaviour monitoring does not establish a diagnosis. A change in recorded behaviour can be useful context, but a vet needs to assess health concerns.
Does more data always mean better AI?
Data quality, accurate labels and appropriate validation matter. A large dataset does not remove the need to test how a system performs in real use.[4]
Related reading: What’s Normal for Your Dog? Why Their Own Patterns Matter and Dog Activity Data: What Do Those Numbers Actually Mean?.
Sources
Deep Learning Classification of Canine Behavior Using a Single Collar-Mounted Accelerometer: Real-World Validation, Animals, 2021.
The Use of Triaxial Accelerometers and Machine Learning Algorithms for Behavioural Identification in Domestic Dogs: A Validation Study, Sensors, 2024.
How to Understand Your Dog’s Body Language, Dogs Trust.
Practical guidelines for validation of supervised machine learning models in accelerometer-based animal behaviour classification, Journal of Animal Ecology, 2025.
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