Hello everyone. I’m Marios Constantinides and I’m a senior research scientist at Nokia Bell Labs, in Cambridge, in the UK.
I’ll present our paper on Quantified Canine: Inferring Dog Personality From Wearable devices.
This is is a joint work between Nokia Bell Labs researchers and Lakmal Meegahapola from EPFL, and this work was done during Lakmal’s internship last summer in our lab.
Wearables have become increasingly popular over the past few years, both for humans and pets.
For humans, today’s wearables allow us to monitor our health, our sleep patterns, or even our stress levels.
Similar wearables have also been developed to track pets’ health. For example, we’ve seen new wearables and apps like FitBark, Pet-Pace, or PitPat.
PitPat, for example, tracks the pet’s exercise, the levels of rest and play, as well as distance travelled or calories burned, and through an app the dog owner can monitor their pet’s health.
But what if we had new wearables and apps that go beyond the obvious activity tracking and health monitoring.
What if we had new wearables that can detect different dog activities and patterns, and from these patterns determine a pet’s personality?
In that case, we can unlock new capabilities beyond health monitoring to allow for effective matchmaking between owners and dogs.
In fact, a few dog agencies and shelters are already experimenting with the use of dog personality traits for matchmaking dogs with future owners.
And that is why, in this work, we posed a question whether we can assess dog personality with wearables.
But how do we measure dog personality in the first place?
By reviewing more than 50 scientific articles on dog personality, the psychologists Amanda Jones and Samuel Gosling proposed the first five-factor dog personality instrument.
And this instrument covers the dimensions of reactivity, fearfulness, responsiveness to training, submissiveness, and aggression.
Building on this seminal work, many other researchers have then incrementally added other dimensions such as calmness, sociability, extraversion, and neuroticism.
And now how can we measure dog personality in practice from wearables?
To do that, we developed a new wearable device, called the PatchKeeper that can be attached to an elastic adjustable band, and can be easily strapped around the chest of a dog.
PatchKeeper has inertial measurement unit sensors like accelerometer and gyroscope to unobtrusively measure a dog’s movements.
And then we identify distinct signatures in these movements that are associated with different dog personality traits.
We conducted a 1 week in-the-wild study, where we initially reached out to 23 dog owners who expressed their interest to participate, and later we successfully recruited 12 of them.
To reach out to dog owners, we employed a variety of techniques, which were proven successful to varying degrees.
For example, social media announcements were effective to some extent, but more local communities such as Nextdoor and more traditional techniques like word of mouth were proven to be more successful.
In total, we collected more than 13 hundred hours of sensed data. We also collected self-reported answers provided from dog owners to two dog personality questionnaires.
The first one was the Dog Personality Questionnaire, or DPQ in short, and the second one was the Refined Monash Canine Personality Questionnaire, or MCPQ-R in short.
Once we have collected this dataset, then we developed a data processing pipeline to convert the raw sensed data into meaningful features that we then used to model dog personality.
First, using a standard procedure of processing accelerometer and gyroscope data, we extracted activity level features using a pre-trained model on human activity wearable data.
In particular, we extracted 4 types of activity: sleep, sedentary, light, and moderate-vigorous activity.
and we extracted these features for the whole day as well as for three time periods within a day: morning, afternoon, and evening.
Using these features, then we trained machine learning models to determine a dog’s personality.
But given that the pre-trained model we used was trained on human data, we wanted to ensure its validity on pets.
So in a small pilot study we obtained data from another consumer-grade wearable, the PitPat, and compared it with our data.
We found that to a very large extent our measurements from PatchKeeper aligned with the measurements from PitPat.
For example, our model performed with 98% accuracy in detecting sleep, 96% in detecting sedentary or light activity, and 92% in detecting moderate-vigorous activity.
Then we set up a classification task to predict 10 personality traits from the extracted features, and our models were able to predict these traits at varying degrees from 0.6 up to 0.9 accuracy.
When looking at the associations between activity levels and personality traits, we identified three main findings.
The first one is about regular patterns of rest.
Specifically, we found that dogs that engaged in regular patterns of rest were more likely to show characteristics
such as fearfulness, excitability, motivation, and amicability, rather than being aggressive.
The second finding is about high movements in the morning.
And here, we found that dogs with high movements in the morning, meaning vigorous activity, were more extraverted and potentially anxious or neurotic.
And finally, the third finding is about light activity in the morning.
and here, we found that light activity in the morning tends to improve the dogs' alertness and responsiveness, and is likely to result in better training focus and outcomes.
I’d like to conclude this talk with one quote from a dog owner whose dog participated in our study.
As dogs cannot speak, a device that allows a dog to speak and express its feelings is worth everything.
Thank you.