# Quantified Canine: Inferring Dog Personality From Wearables

Lakmal Meegahapola<EMAIL_ADDRESS>Idiap Research Institute &
EPFL, Switzerland ,  Marios Constantinides  Nokia Bell Labs, UK ,  Zoran
Radivojevic  Nokia Bell Labs, UK ,  Hongwei Li  Nokia Bell Labs, UK ,  Daniele
Quercia  Nokia Bell Labs, UK and  Michael S. Eggleston  Nokia Bell Labs, USA

###### Abstract.

Being able to assess dog personality can be used to, for example, match
shelter dogs with future owners, and personalize dog activities. Such an
assessment typically relies on experts or psychological scales administered to
dog owners, both of which are costly. To tackle that challenge, we built a
device called “Patchkeeper” that can be strapped on the pet’s chest and
measures activity through an accelerometer and a gyroscope. In an in-the-wild
deployment involving 12 healthy dogs, we collected 1300 hours of sensor
activity data and dog personality test results from two validated
questionnaires. By matching these two datasets, we trained ten machine-
learning classifiers that predicted dog personality from activity data,
achieving AUCs in [0.63-0.90], suggesting the value of tracking the
psychological signals of pets using wearable technologies.

dog personality, wearables, passive sensing, dog activity recognition,
activity level, behavior modeling

††journalvolume: 0††journalnumber: 0††article: 0††publicationmonth:
0††copyright: none††ccs: Human-centered computing Ubiquitous and mobile
devices††ccs: Human-centered computing Computer supported cooperative
work††ccs: Human-centered computing Empirical studies in ubiquitous and mobile
computing††ccs: Human-centered computing User studies

##  1\. Introduction

When it comes to dog adoption, breed may not be the only important factor to
consider (Bradshaw, 2021; Animals, 2019) as humans tend to favor, for example,
a pet’s looks (e.g., attractiveness (Chersini et al., 2018) based on poses and
facial areas (Isgate and Couchman, 2018; Hecht and Horowitz, 2015)) and
perceived human-directed sociability (Lamb et al., 2021). However, according
to a study from the Animal Farm Foundation (Foundation, 2020), one in every
four pets that are chosen based on breed (or looks) end up in shelters and
rescues. By contrast, personality traits tend to offer a more comprehensive
behavioral description of a dog, which is consistent over time and context
(Gartner, 2015). Dog personality has been described in the literature using a
variety of traits including fearfulness, energy levels, aggression,
excitability, motivation, and amicability (Jones and Gosling, 2005; Jones,
2008; Ley et al., 2008).

Not only could dog personality assessment reduce the number of owner-dog
mismatches, but it could also put an end to (unfortunate) cases in which dogs
get flocked into shelters or destroyed by authorities when expelled from their
homes (Corsetti et al., 2021). In fact, a few dog agencies and shelters are
already experimenting with the use of dog personality traits for matchmaking
dogs with future owners (Society, 2022; Knowsley, 2021). Further, like humans,
dogs also need different levels and types of companionship, activities, and
emotional connection depending on their inherent personality traits
(burgesspetcare, 2021; Knowsley, 2021). It is therefore extremely important to
identify a set of activities that ‘work’ for a dog, and to find the right
companion dogs for socializing (BRACKIN, 2022), not least because inadequate
socialization may escalate the pet’s fear levels and may lead to aggression
(shibashake, 2017).

Dog personality assessment is typically done through observational assessments
by experts or psychological scales administered to the dog’s owner (Cox et
al., 2020; Jones, 2008). The former is expensive, time-consuming, and requires
highly specialized facilities, and the latter is time-consuming, is prone to
biases, and requires knowledge of someone who already knows the dog very well
(Jones and Gosling, 2005; Jones, 2008; Rowan, 1992). That is why we set out to
computationally assess dog personality in everyday settings (compared to
highly specialized facilities or laboratory settings) with wearables. In the
wearable sensing literature, studies used devices for monitoring dog activity
(Weiss et al., 2013; Ladha et al., 2013; Chambers et al., 2021; Byrne et al.,
2018; Ladha et al., 2013), detecting pruritic behaviors (i.e., scratching,
head shaking) (Griffies et al., 2018), and tracking breathing patterns (Cotur
et al., 2022). This stream of research recently inspired the fast-growing
market of pet wearables (Zamansky et al., 2019) with a number of consumer-
grade platforms readily available such as FitBark111https://www.fitbark.com/
(location, activity, and sleep tracking), PetPace222https://petpace.com/
(vital signs and behavior tracking), and PitPat333https://www.pitpat.com/
(activity tracking with gamified social elements).

Similar to how passive sensing of human personality drives the design of
personalized apps (Khwaja et al., 2019), sensor-based modeling of dog behavior
through activity trackers has the potential to benefit both dogs and owners
(Zamansky et al., 2019). It has been found to impact owners’ motivation to
increase their mutual physical activities with their dogs, and increased human
awareness to animals’ needs (Jones et al., 2014). However, computational
personality assessment techniques for dogs are non-existent. Therefore, we set
out to develop and test an automatic way of operationalizing dog personality
through passively sensed data from wearables. In so doing, we made three sets
of contributions:

  * •

We developed a wearable device, called “Patchkeeper”, which can be easily
strapped on a dog’s chest (Section 4). The device is equipped with
accelerometer and gyroscope sensors. Since its processing pipeline was
initially developed for wearable data obtained from human subjects, we
conducted a validation study of our device and the pipeline on dogs, together
with a consumer-grade dog activity monitor. We found that our device is
capable of determining four activity levels: moderate-vigorous activity with
an accuracy of 92%; light and sedentary activity with an accuracy of 96%; and
sleep with an accuracy of 98%.

  * •

We launched a data collection campaign to recruit dog owners whose pets
participated in a one-week study. The campaign was launched on four social
media platforms (i.e., posts were made on Twitter, Facebook, Instagram, and
NextDoor) and was also spread via word of mouth (Section 5), resulting in a
total of 22 dogs being successfully recruited and monitored for one week
(i.e., the entire period of study). Dog owners answered two validated
questionnaires (the Dog Personality Questionnaire (DPQ) (Jones, 2008) and the
Refined Monash Canine Personality Questionnaire (MCPQ-R) (Ley et al., 2009)),
and provided self-reports about their dog’s activities (e.g., images were
taken when walking the dog). Using the passively sensed data, we developed a
data processing pipeline and extracted two types of features: (a) activity-
level features (e.g., % of sleep in the morning, % of sedentary activity in
the afternoon) and (b) statistical features (e.g., acceleration histogram)
(Section 6). We statistically analyzed the extracted features along with the
self-reports from the two questionnaires and found that both types of features
could discriminate dog personality traits (e.g., high or low fearfulness),
with features capturing dog activity between 6am and 12pm (morning) being more
informative for personality trait inferences than features capturing activity
in the rest of the day. This is expected as most dogs will be the most active
and full of energy in the mornings after a dedicated sleep, and that was
reflected in the signal captured from our device’s sensors.

  * •

We set up an inference task to predict dog personality traits using both
activity-level and statistical features (Section 7). Our models achieved AUC
scores in the range of 0.63-0.90 with a time-window-based setup (i.e., using
the same features computed at different times of the day) (Section 8).
Interestingly, statistical features (e.g., acceleration histogram) were more
informative than activity-level features (e.g., sedentary); yet, despite
explaining more variance in personality traits, the former set of features is
less interpretable than the latter one, opening up the need for Explainable AI
in this kind of wearables too. When it comes to the usability of dog
monitoring wearables, dog owners had split opinions about battery life (some
found a day of battery life to be sufficient, while others expressed the
opposite). For the development of future monitoring wearables, the majority of
dog owners stressed their immense value, echoing a dog owner’s statement: “as
dogs cannot speak, a device that allows my dog to ‘speak’ and ‘express her
feelings’ is worth everything”.

##  2\. Background and Related Work

Next, we surveyed various lines of research that our work draws upon, and
grouped them into four main areas: _i)_ dog personality research; _ii)_
psychological scales for assessing dog personality; _iii)_ monitoring dog
activity with wearable sensing; and _iv)_ activity levels and dog personality.

###  2.1. Dog Personality Research

Dogs have personality (Jones, 2008; Ley et al., 2009, 2008), which refers to a
set of dog behaviors and traits that are consistent over time and context
(Gartner, 2015; Gosling, 2008). These traits stem from the Five-Factor Model
of personality, a.k.a. the Big-Five Traits (Costa Jr and McCrae, 1992). As
with personality, temperament is also being used in literature to describe
both human and animal behavior. Researchers on animals and human infants tend
to use the term temperament, while those studying human children and adults
tend to use the term personality, with the two terms often being used
interchangeably (McCrae et al., 2000). On the one hand, temperament has been
defined as the inherited, early appearing tendencies that continue throughout
life and serve as the foundation for personality (Goldsmith et al., 1987;
Jones and Gosling, 2005); a definition that has not been widely adopted by
animal researchers (Gosling, 2001). On the other hand, personality
psychologists often study phenomena including temperament and character
traits, attitudes, physical and bodily states, moods, and life stories (John
and Gosling, 2000). Therefore, a broad definition includes characteristics of
individuals that describe and account for consistent patterns of feeling,
thinking, and behaving (Pervin and John, 1997). As the distinction between
temperament and personality has not been maintained consistently in the
literature, we echo the statement by Jones and Gosling (Jones and Gosling,
2005; Jones, 2008), that is, _the term “temperament” is used whenever possible
while the term “personality” is more appropriate when, for example, referring
to work that explicitly discusses personality research. Hence, we use the term
personality throughout the paper._

In the scientific literature, Elliott Humphrey first hinted at the idea of
dogs having personality in 1934 (HUMPHREY, 1934). He described German Shepherd
dogs with the traits of jealousy, apport, wildness-tameness, affection,
initiative, attentiveness, curiosity, alertness, fighting and protection
instincts, willingness to bite humans, confidence, self-right, energy,
willingness, and intelligence. Seventy years later, by reviewing more than 50
scientific articles on dog personality, Jones and Gosling (Jones and Gosling,
2005) found several inconsistencies, and proposed the first five-factor dog
personality instrument, covering the dimensions of reactivity, fearfulness,
responsiveness to training, submissiveness, and aggression. Building on Jones
and Gosling’s seminal work, researchers have incrementally added other
dimensions such as calmness, boldness, trainability, and sociability (Kubinyi
et al., 2009); extraversion, neuroticism, self-assuredness (motivation),
training focus, and amicability (Ley et al., 2009); stranger-directed
sociability, activity, aggressiveness, and trainability (Mirkó et al., 2012);
and playfulness, chase-proneness, curiosity/fearlessness, sociability, and
aggressiveness (Svartberg, 2006). Researchers, however, have split views when
it comes to predictors of dog personality. Some studies found that different
breeds have similar personalities (Ley et al., 2009; Schneider et al., 2013;
Svartberg, 2006), while others reported the lack of evidence for it (Mirkó et
al., 2012; Sinn et al., 2010). Two other attributes linked to personality
traits are whether the dog is neutered or not (neutering is a surgical
procedure to prevent a dog from reproducing) and its sex. Kubinyi et al.
(Kubinyi et al., 2009) found that not neutered dogs are more calm, while
Lofgren et al. (Lofgren et al., 2014) found that neutered female dogs were
less excitable and sought lower levels of attention. There is also evidence
that older dogs are more calm (Kubinyi et al., 2009) with lower amounts of
fear (Lofgren et al., 2014) compared to their younger counterparts. Hence, as
mentioned above, even though not conclusive, there is evidence that static
attributes such as sex, age, and neutering could be associated with dog
personality (Kubinyi et al., 2009; Lofgren et al., 2014).

###  2.2. Psychological Scales for Assessing Dog Personality

While there are many dog personality measurement questionnaires (Salonen et
al., 2021; Posluns et al., 2017), two widely established and validated
psychological scales are: _a)_ the Dog Personality Questionnaire (DPQ) (Jones,
2008), and _b)_ the Refined Monash Canine Personality Questionnaire (MCPQ-R)
(Ley et al., 2009). Next, we explain each scale.

  * •

DPQ: Building on the work of Jones and Gosling (Jones and Gosling, 2005), the
development of this scale aimed at reducing the time and resources (i.e.,
trained assessors, money, facilities) for dog personality assessment. Amanda
Jones started from 1200 dog descriptors (i.e., statements describing dog
behavior) identified in the literature and narrowed them down to 360
statements (Jones, 2008). Then, in two studies with over 6000 participants,
they narrowed these statements down to 75 items, grouped in five factors of
_Fearfulness_ , _Aggression towards People_ , _Excitability_ , _Responsiveness
to Training_ , and _Aggression towards Animals_. Scores for these traits can
be derived using a list of statements marked by the dog owner on a Likert
scale from 1 to 7 (1: disagree strongly; 7: agree strongly).

  * •

MCPQ-R: This is the refined version of the original MCPQ questionnaire (Ley et
al., 2008). The original questionnaire was developed using an adjective-based
technique similar to the Big-Five Model of personality (John, 1990). Ley et
al. (Ley et al., 2009) revised the original MCPQ in a study with more than 450
participants. This led to the development of MCPQ-R, which consists of five
factors: _Extraversion_ (perceived energy level of the dog), _Motivation_
(perceived persistence in the face of distractions—e.g., begging for food,
finding a particular toy), _Training Focus_ (perceived trainability of the
dog), _Amicability_ (perceived tolerance of the dog while being around humans
and animals), and _Neuroticism_ (perceived nervous or cautious behavior of the
dog). To assess these traits, dog owners rate 26 words (e.g., friendly,
obedient, hyperactive) that describe their dog’s personality by marking each
word with the appropriate number from 1 to 6 (1 = really does not describe my
dog; 6 = really describes my dog).

Even though the two scales come with different constructs, a fair amount of
convergence has been observed (Posluns et al., 2017) between neuroticism
(MCPQ-R) and fearfulness (DPQ); excitability (DPQ) and extraversion (MCPQ-R);
responsiveness to training (DPQ) and training focus (MCPQ-R). While other
widely used questionnaires such as the Canine Behavioral Assessment and
Research Questionnaire (C-BARQ) (Hsu and Serpell, 2003) were developed, recent
research suggested that it is not suitable for general research use because it
was designed to identify specific dog behavioral problems (Cox et al., 2020).
Hence, in the current study, we focused on DPQ and MCPQ-R questionnaires that
capture a total of ten personality traits (factors).

###  2.3. Dog Monitoring with Wearable Sensing

Dog tracking and activity detection have gained much popularity due to
advancements in sensor technology (Hussain et al., 2022), which led to a
number of commercial dog monitoring products (e.g., FitBark, PetPace, PitPat).
However, tying wearable sensing to behavioral tests (like dog personality in
our case) is just starting to gain traction. In Animal-Computer Interaction
research, prior studies focused on systems that facilitate better
communication and interaction between dogs and owners (Hirskyj-Douglas et al.,
2021) as well as among dogs (Hirskyj-Douglas and Lucero, 2019). Personality
and dog behavior were also studied as part of certain games such as the spin-
the-bottle (Cox et al., 2020), concluding that dogs’ preferences for human
involvement were likely attributed to subtle differences in personality traits
or prior training experiences.

Brugarolas et al. (Brugarolas et al., 2015) developed a non-invasive wearable
sensor system for measuring dogs’ vital signs using electrocardiogram (ECG),
photoplethysmogram (PPG), and inertial measurement units (IMU). In a
longitudinal study of monitoring puppies’ cardiac changes, Foster et al.
(Foster et al., 2020) developed machine learning models for predicting
puppies’ Behavior Checklist (BCL) scores (including changes in energy and
smoothness of movement, vocalization, tongue flicking, use of coping
strategies, body language, and changes in responsiveness to the handler),
achieving up to 90% of accuracy. Weiss et al. (Weiss et al., 2013) developed
WagTag that infers three dog activity levels (i.e., walk, run, and minimal),
and concluded that personal models for predicting activity levels are better
than universal models. Ladha et al. (Ladha et al., 2013) also demonstrated
that 17 dog activities (e.g., barking, running, chewing, digging) can be
inferred with an accuracy of 70% from a collar-worn wearable with
accelerometers. More recently, Chambers et al. (Chambers et al., 2021) used
deep-learning models to infer dog activities with a collar-worn accelerometer,
and showed that activities such as eating and drinking could be inferred with
high accuracy, while behaviors such as licking, petting, rubbing, and sniffing
were harder to identify. Beyond activity tracking, Griffies et al. (Griffies
et al., 2018) used wearables to detect pruritic behaviors (i.e., scratching,
head shaking). In a laboratory study with over 360 dogs, they showed that
algorithms could be trained to infer head shaking and scratching with
sensitivities over 70% and specificities over 90%. Wearable devices have also
been used to monitor dog breathing patterns with reasonable accuracies (Cotur
et al., 2022).

###  2.4. Dog Activity Levels and Personality Traits

Prior work in animal-computer interaction and canine behavior has highlighted
certain relationships between personality traits and activity levels. For
example, previous studies found that more extroverted dogs showed higher
activity levels in the park (Carrier et al., 2013), higher energy levels
(Gosling et al., 2003a), with significantly greater proportions of time spent
with other dogs. Amicable dogs showed frequent behaviors indicative of play
(high activity level), while neurotic dogs showed higher frequencies of
hunched posture (low activity level) (Carrier et al., 2013). Hence,
extraversion, amicability, and neuroticism (traits that come from MCPQ-R) can
be directly linked to activity levels. Further, even though not directly
studied, prior studies linked psychological aspects such as fearfulness and
aggression (corresponding to the three traits of DPQ— fearfulness, aggression
towards people, and aggression towards animals) to activity levels. For
example, in domestic dogs, it has been found that a higher degree of
impulsivity correlates with high activity levels (Wan et al., 2013), poor
attention span (Vas et al., 2007), and human-directed aggression (Rayment et
al., 2015; Peremans et al., 2003). Further, previous studies linked activity
levels to negative emotions and stress (Beaudet et al., 1994; Rayment et al.,
2015; Jones et al., 2014), which, in turn, can be seen as the roots of
fearfulness and aggression (Beaudet et al., 1994). Moreover, neuroticism has
been directly linked to activity levels in some studies (Carrier et al.,
2013), but it has also been observed to be converging with fearfulness
according to other studies (Posluns et al., 2017), hence providing evidence on
how activity-levels could be indirectly informative of fearfulness. Studies
have also found that excessively high or low activity levels are predictive of
successful dog training (i.e., trainability and certain levels of fearfulness
(Weiss, 2002); traits captured from DPQ and MCPQ-R).

In summary, previous wearable sensing literature explored aspects such as
monitoring dog activity, detecting pruritic behavior, and tracking breathing
patterns. While previous literature explored a few aspects concerning the
relationship between dog activity and personality traits, this relationship
still represents an under-explored area. Our study aims to partly fill this
gap by exploring the relationship between ten personality traits captured from
two canine personality questionnaires and dog activity.

##  3\. Research Questions

We set out to explore whether dog personality can be automatically inferred
from wearable data in everyday settings by answering three questions:

  * RQ1:

Which dog activity-level features and statistical ones can be extracted from
wearable data?

  * RQ2:

Which dog activity-level features and statistical ones are associated with dog
personality?

  * RQ3:

To what extent activity-level, statistical, and demographic features are
predictive of dog personality?

##  4\. Patchkeeper

Figure 1. _A:_ The Patchkeeper device is attached to an elastic adjustable
band. The device comes with two lights: a _green light_ indicating whether the
device is ON; and a _red light_ indicating whether the device is in charging
mode. _B:_ The band can be strapped on the dog’s chest.

Patchkeeper (Figure 1a) is a wearable device developed at Nokia Bell Labs for
behavioral monitoring of both humans and animals. It contains a
photoplethysmography (PPG) sensor, an electrocardiogram (ECG) sensor, an
accelerometer, a gyroscope, and a microphone. In the current study, only the
inertial measurement unit (IMU) sensors (i.e., accelerometer and gyroscope)
were used, and PPG, ECG, and microphone were not used due to the dog hair and
privacy concerns (more details in Section 5.1.1). The IMU sensor is a BMI160
from Bosch Sensortec444https://www.bosch-sensortec.com/products/motion-
sensors/imus/bmi160/. It is a small, low-power, low-noise 16-bit chip designed
for mobile applications. It provides highly accurate gyroscope and
accelerometer data in real time. The IMU’s sampling rate was set to 50 samples
per second. This sampling rate allowed for striking the right balance between
obtaining reasonably fine-grained data for our analysis and storage capacity
requirements. The microcontroller unit (MCU) is an nRF52840 from Nordic
Semiconductor555https://www.nordicsemi.com/products/nrf52840, which contains a
64 MHz Cortex-M4 processor with floating point unit (FPU). All data was saved
in a micro-SD card on the printed circuit board (PCB).

The device dimensions are 76x52x15mm with a weight of 56 grams. It contains a
400mAh lithium polymer battery, which can last more than 24 hours while
continuously recording data. The battery takes around two hours to be fully
charged and comes with a USB-C charging port for hassle-free charging with any
commercially available charger. With a two-hour daily charge, the device runs
continuously without any loss of data. The device has a switch with ON and OFF
sides marked with red and green colors. For better user experience, we
included different lights on the device (Figure 1a): _(i)_ a green light
flashing every 10 seconds indicates that the device is ON, it is working
properly, and data is being recorded; _(ii)_ a static red light indicates that
the device is fully charged, and _(iii)_ a flashing red light indicates an
issue with the device or the memory card.

##  5\. Animal Study

Having developed our custom-made wearable device to collect dog activity data,
we conducted a one week in-the-wild study to understand the link between dog
behavior and personality.

###  5.1. Materials and Apparatus

Each dog owner received a package, fitted in a medium-sized letter envelope,
weighing approximately 500 grams. The package contained: a Patchkeeper device,
a charging cable, three black elastic straps, a consent form, an information
sheet, questionnaires (i.e., DPQ, MCPQ-R, and a post-study questionnaire), and
a pre-paid return envelope. Upon completion of the study, the owner shipped
back the package using the pre-paid return package.

####  5.1.1. Patchkeeper and Elastic Straps.

As the device can be used on both human and animal subjects (Section 4), and
given the requirement of continuous monitoring for one week, we decided to
deactivate the ECG and PPG sensors, and the audio microphone. ECG and PPG were
disabled for two reasons: first, they relied on skin conductance, which is
made difficult by dog hair; and second, they required additional straps, which
would place additional effort on the owners, making it more likely for them to
drop out. Audio was also deactivated due to privacy reasons as the device
would otherwise continuously capture audio throughout the day. It would be
extremely awkward to listen to intimate moments or any audio conversation that
creeps into the device due to the pet’s movements. To ensure that the device
would fit various dog sizes, we used an adjustable elastic band that can be
strapped to the pet’s chest (Figure 1b). These are off-the-shelf straps that
can be found on Amazon and are comfortable to wear. The device can be simply
attached to the strap using a sticky patch. We also considered alternative
areas (e.g., neck) to place the device, weighing various aspects. First, some
breeds have a more pronounced dewlap (loose, saggy skin around the
neck/throat) than others, whilst the chest is generally not affected in such a
way. Second, it has been found that the skin near the axilla (armpit) and
ventral abdomen (lower chest/thorax, top of the belly) is significantly
thinner than that in the dorsal (top of the dog) areas (Theerawatanasirikul et
al., 2012). Third, double coated breeds (e.g., the golden retriever, Samoyed,
and German shepherd included in this study) have coarse guard hairs and dense
undercoats, with this being particularly pronounced in the dorsal areas but
less so near the axilla and ventral abdomen areas. Taking all these aspects
into consideration, the chest area (behind the forelegs) has the benefits of
thinner skin whilst removing breed-to-breed variation in our sample (previous
work also favored the chest area (Foster et al., 2020)). To ensure that the
position of the device did not impact the results, we intentionally used wider
straps with 5cm wide rubbered features and strong tensioning force to make the
device intimately connected to the fixed location at the pet’s chest. During
pilot studies in the design of the device, we estimated an approximate sensor
dislocation of $\pm$2cm, which was sufficient to guarantee fixed sensor
location over a long period of use.

####  5.1.2. Questionnaires.

Dog owners answered two types of questionnaires. A Pre-Study Questionnaire
(Q1) and a Post-Study Questionnaire (Q2). Q1 was completed before the study
and had two sections. The first section captured demographic information of
the owner (i.e., age, sexual identity, occupation status, and ethnicity),
followed up with the Personality Inventory (TIPI) (Gosling et al., 2003b),
which is a 10-item measure of the Big Five (or Five-Factor Model) dimensions.
The second section captured basic information about the dog (i.e., the dog’s
age, breed, sexual identity, weight, typical activity levels, disease
conditions, and whether it is neutered or not). Q2 was completed after the
study and had two sections as well. The first section captured user experience
and dog owners’ perceived utility of wearable platforms for dog monitoring.
The user experience of the Patchkeeper was captured by a Likert scale of 1 to
7 (1 = very bad, 7 very good) and corresponding feedback. In a similar vein,
we captured the perceived utility of commercial wearable pet monitoring
devices in general by a Likert scale of 1-7 (1 = not very important; 7 = very
important) and corresponding feedback. Additionally, we asked dog owners to
rate on a scale of 1-7 (1 = strongly not preferred, 7 = strong preferred)
their likelihood of adopting a mobile app that uses Patchkeeper’s data for dog
monitoring. We provided sample options including: monitoring activity types,
identifying when dogs are not in a healthy state, finding a community of dogs
with a similar personality, or monitoring the mood and stress of dogs. The
second section of Q2 asked owners to complete the Dog Personality
Questionnaire (DPQ) (Jones, 2008) and the Refined Monash Canine Personality
Questionnaire (MCPQ-R) (Ley et al., 2009).

Table 1. Overview of dog demographics. Dog ID | Breed | Sex |  Weight | Neutered? | Birth Year
---|---|---|---|---|---
#1 | Golden Retriever | Female | 30 kg | Yes | 2018
#2 | Golden Retriever | Male | 35 kg | No | 2021
#3 | Poodle (Toy) | Male | 8 kg | No | 2020
#4 | Dalmadoodle - 75% Poodle, 25% Dalmatian | Female | 13 kg | Yes | 2020
#5 | Golden Retriever | Male | 40 kg | No | 2021
#6 | Working English Setter | Male | 29 kg | Yes | 2017
#7 | Boxer | Female | 25 kg | No | 2020
#8 | Samoyed | Male | 25 kg | No | 2018
#9 | Cockapoo | Female | 10 kg | Yes | 2011
#10 | Working English Setter | Female | 31 kg | No | 2016
#11 | Mixed | Male | 15 kg | Yes | 2021
#12 | Cavalier King Charles Spaniel | Female | 8.5 kg | Yes | 2019

####  5.1.3. Information Sheet and Consent Form.

The information sheet described the study protocol (Section 5.2). The consent
form highlighted two aspects: confidentiality and voluntary participation. In
terms of confidentiality, the form explained that all data would be kept
confidential except in cases where the researchers were legally obligated to
report specific incidents (e.g., dog abuse). The collected phone numbers and
email addresses will not be used in any scientific output, and that
confidentiality will be preserved by: _a)_ assigning code numbers for dog
owners in all research documents; and _b)_ keeping notes, data, and any other
dog owner identifiers in a password-protected hard drive, securely stored at
the facilities of the Nokia Bell Labs. In terms of voluntary participation,
the form explained that a signature was required to participate. Additionally,
withdrawal from the study was allowed at any time and without giving a reason,
even after signing the consent form. Upon withdrawal, all data will be
deleted.

####  5.1.4. Pre-Paid Letter Cover and WhatsApp Hotline.

To ease dog owners participation, we included in our package a pre-paid return
package. Upon completion, they placed all materials received into the return
package and posted it. To have effective communication with the dog owners
throughout the study, we used a dedicated WhatsApp number as a hotline. This
number was used by the first author to deal with matters related to the study
(e.g., unable to place the strap or charge the device).

Figure 2. Our study protocol has three phases. In the _pre-study_ phase, dog
owners received the study package, including the PatchKeeper device and
questionnaires; in the _study_ phase, data collection for seven days took
place; and, in the _post-study_ phase, dog owners returned the package and
answered a follow-up survey about their experience using the device.

###  5.2. Study Protocol

The study protocol has three periods: pre-study, study period, and post-study
(Figure 2).

####  5.2.1. Pre-Study

Once the dog owners received the package, they familiarized themselves with
the device and answered the pre-study questionnaire. During that period, they
were encouraged to ask questions via the hotline, and were instructed to fully
charge the Patchkeeper and send a picture of the dog with the device turned
on, after a full charge every day (this was a preemptive measure to ensure
compliance, but, at the same time, to guarantee data quality).

####  5.2.2. Study Period.

During the seven-day period, the device captured sensor data between 12am and
6pm (continuously for 18 hours), and it could be charged for two hours between
6pm and 12am. Enforcing the same charging schedule across all dog owners
enabled us to obtain comparable data across dogs. Of course, this comes with
the caveat that we might not have 2-3 hours of data between evening hours; a
drawback that we were willing to accept to ensure high quality data during
other time slots. In summary, each evening, the dog owners would remove the
strap from the dog, turn the device off, fully charge it, turn it back on, and
put it back on the dog. Afterward, they would send a message with a picture of
the dog wearing the device via the hotline. In the morning, they would again
be asked to check that the device was working and was correctly positioned
around the dog’s chest. During all other time periods, no interaction was
required from dog owners as the device would automatically capture all data.
Furthermore, we encouraged the dog owners to voluntarily send us in-situ self-
reports (in the form of images or short video clips) of various dog activities
throughout the day.

####  5.2.3. Post-Study.

During that period, dog owners answered the post-study questionnaire. They
placed all materials and apparatus in the pre-paid package, and shipped it
back to the return address. Upon successful completion of the study, dog
owners received a $25 Amazon gift voucher and a report summarizing their dog
activity profile over the seven days of the study.

###  5.3. Recruitment

Recruitment for in-the-wild human studies is typically difficult (Ellard-Gray
et al., 2015), and so it is for animal studies. We employed two techniques
that were proven (un)successful to varying degrees.

  * •

Social media and local communities (Twitter, Facebook, Instagram, and
NextDoor): Twitter and Facebook are used to advertise scientific studies
(Whitaker et al., 2017; Sibona and Walczak, 2012)—both channels were not very
successful in this study. Instead, Instagram posts on profiles dedicated to
dogs with 1000s of followers were successful to some extent. Finally,
Nextdoor666https://nextdoor.com/, a social media site for local communities,
was the most successful recruitment strategy (40% of the dogs were recruited
through it). A banner of the study was also shared within the communities of
Cambridge Dog Meetup.

  * •

Word of Mouth: One researcher from Nokia Bell Labs, who is not part of
conducting the study, participated in the study with his dog. He spoke to his
neighbors about the study, who also signed up. Shortly after, this created a
snowball effect (30% of the dogs were recruited through word of mouth).

Having a variety of recruitment techniques, we were able to reach out to 31
dog owners in Cambridge, United Kingdom. Of these, 22 signed up for the study
and received the package. 10 of them withdrew during the study for various
reasons: high temperature, including a heatwave, making it difficult for the
dog to wear the strap continuously (2/10), owners going away for summer
holidays (2/10), strap not holding to the body of the dog due to its curvy
shape (1/10), dogs not being in healthy conditions (i.e., leg injury after a
run, wound on the neck, bug bites) during the time of the experiment (3/10),
and dogs not appearing to feel happy about wearing the strap (2/10). This left
us with 12 healthy dogs that successfully completed the study. Note that these
12 dogs were all healthy (as reported by their owners), and every morning the
first author checked with the owners whether any of the dogs displayed
peculiar behavior (e.g., snagging on objects, appearing to feel uncomfortable)
due to the wearable. No such incident was reported. However, we had an
incident wherein a dog jumped into a body of water, destroying the device.
This dog continued the study later with a replacement device. The recruitment
took place during the summer period, with starting dates ranging from July to
August. The study was approved by Nokia Bell Labs, and the study protocol
stated that the collected data will be analyzed for research purposes only. In
accordance to GDPR, no researcher involved in the study could have tracked the
identities of the dog owners after the end of the study, and all responses
were analyzed after anonymization at an aggregated level.

##  6\. Dataset

Having successfully deployed Patchkeeper in an in-the-wild study and collected
more than 1300 hours of accelerometer and gyroscope data, we then applied a
processing pipeline to that data.

Figure 3. Comparison of dog#3 is monitored by activity levels generated from
Patchkeeper (top) and Pitpat (bottom) for 24 hours.

Figure 4. Example of four types of activity levels of dog#1 (i.e., sleep,
sedentary, light, and moderate-vigorous) generated from Patchkeeper.

###  6.1. Data Processing Pipeline

####  6.1.1. Activity-Level features.

This set of features describes dog behaviors derived from accelerometer data,
and is interpretable. To extract these features, we used a state-of-the-art
data processing pipeline to convert the triaxial data to acceleration (Doherty
et al., 2017; Willetts et al., 2018). The processing included four steps: _a)_
ten-second samples from static sections (no movement) of accelerometer data
were obtained to optimize the gain and offset for each of the X, Y, and Z axes
to fit a unit gravity sphere (Willetts et al., 2018); _b)_ data were re-
sampled at 100Hz using linear interpolation, and acceleration was calculated
using the euclidean norm of X, Y, and Z axis values; _c)_ a fourth-order
butterworth filter was used to remove noise; and _d)_ one gravity (1G) unit
was removed from the data, and the remaining negative values were truncated at
zero.

Next, using non-overlapping time windows of 60 seconds, 126 time and frequency
domain features such as mean, standard deviation, median, minimum, maximum,
25th and 75th percentiles of vector magnitude, kurtosis, and skewness were
generated (Willetts et al., 2018; Doherty et al., 2018; Walmsley et al.,
2021). Using these features, we used a pre-trained model based on Hidden
Markov Models and Balanced Random Forests (Walmsley et al., 2022) to classify
acceleration into four different activity levels: _sleep_ , _sedentary_ ,
_light_ , and _moderate-vigorous_. These activity levels are in line with
prior studies on dog activity-levels (Morrison et al., 2013; Ortmeyer et al.,
2018; Weiss et al., 2013).

As the data processing pipeline was initially developed for wrist wearables
worn by humans, we conducted a validation step to ensure transferability to
animals. To do that, we used a consumer-grade dog activity monitor called
PitPat777https://www.pitpat.com/ on two dogs (dog#3 and dog#8) for three days.
These two dogs also took part in the larger in-the-wild study. In total, we
collected over 120 hours of sensor data from both devices, and a total of 83
self-reports (e.g., the dog is sleeping, running) from dog owners. A
comparison of our 24-hour data processing pipeline and PitPat’s output is
shown in Figure 3. In terms of ground truth obtained from PitPat (in total, we
analyzed 100 data points), our model performed with an accuracy of: 98% in
detecting sleeping (sections where PitPat showed no activity); 92% in
detecting high intense moderate-vigorous activities (sections where PitPat
showed a peak in activity levels), and 96% in detecting sedentary or light
activity levels (sections where PitPat showed a medium level of activities).
In terms of self-reported ground truth (including pictures), our model was 91%
accurate in determining the 83 activity levels provided by dog owners. This
answered our RQ1, allowing us to conclude that activity levels can be
extracted with accuracies over 90%.

Having established the reliability of our data processing pipeline, we first
obtained how long a particular dog had been engaging in activities at
different levels (i.e., percentage of time spent in sleep, sedentary, light,
and moderate-vigorous activity levels), resulting in four features. We then
used the acceleration time series to extract statistical features such as its
minimum, maximum, mean, median, and standard deviation, resulting in five
features. For simplicity, we call these nine features _activity-level_
features throughout the paper.

####  6.1.2. Statistical Features.

This set of features was derived from complex associations in the time series
of both accelerometer (x,y,z) and gyroscope (x,y,z) and, as such, is less
interpretable compared to the activity-level features but computationally less
expensive to obtain. To extract these statistical features, we used the tsfel
library (Barandas et al., 2020). The library allowed us to extract 56 features
(e.g., min, max, std, mean, median, kurtosis, skewness, absolute energy, zero
crossing rate, histogram, and empirical cumulative distribution function) that
describe temporal and statistical aspects of the time series nature of the
data888https://tsfel.readthedocs.io/en/latest/descriptions/feature_list.html.

####  6.1.3. Unit of Analysis.

A typical way of capturing temporal dynamics in HCI and UbiComp studies is to
use time windows at different times of day when calculating features (Obuchi
et al., 2020; Wang et al., 2020, 2017; Nepal et al., 2020; Wang et al., 2022;
Constantinides et al., 2018). A large time window of eight hours, dividing the
day into three periods, has been previously used in dog studies, and it has
been found, for example, that studying night sleep separately from day sleep
provided more meaningful insights about sleeping patterns (Schork et al.,
2022; Bódizs et al., 2020) than studying sleeping during the whole day.
Drawing from this prior line of work, we resorted to three time windows for
our analysis: _a)_ night (N): time from 12am to 5.59am; _b)_ morning (M): time
from 6am to 11.59am.; and _c)_ afternoon (A): time from 12pm to 5.59pm. For
example, at night, a dog could be showing activity levels as 60% sleeping, 20%
sedentary, 15% light, and 5% moderate-vigorous. Hence, for each time window,
we extracted a total of 65 features, including the nine activity-level and the
56 statistical features.

(a) DPQ

(b) MCPQ-R

Figure 5. Average personality trait scores for five personality factors
measured through DPQ (in a Likert scale 1-7) and MCPQ-R (in a Likert scale
1-6), respectively. Scores for MCPQ-R were higher (except Neuroticism),
whereas scores for DPQ were more spread out.

###  6.2. Descriptive Statistics of Personality Traits and Activity Levels

The distributions of personality traits are shown in Figure 5, and a summary
of statistics of the recruited dogs is in Table 1. Recruited dogs were over
one year old, with a mean age of three years and ten months, 65% of them were
female dogs, and were all medium to small dogs. In terms of dog personalities,
the DPQ factors: _Fearfulness_ , _Aggression Towards People_ , and _Aggression
Towards Animals_ had average scores below 3.2, whereas the _Excitability_ and
_Responsiveness to Training_ had high average scores above 4.8. For MCPQ-R,
_Neuroticism_ had an average score of 2.67, while the other four factors had
average scores on or above 4.38. Overall, the mean percentages across the
personality dimensions were comparable to previous studies (Ley et al., 2009;
Carrier et al., 2013). Further, Figure 6 shows the average activity level
across all dogs as a percentage of total time ($y$-axis) for different time
periods of the day ($x$-axis). The night was predominantly spent sleeping
(63.3%) whereas morning was predominantly spent at other types of activity
such as sedentary (36.5%), light (13.5%), or moderate-vigorous (9.3%).

Figure 6. Percentage of time spent by all dogs on average in each of the four
activity level for sleep, sedentary, light, moderate-vigorous at three
different time of day (Night, Morning, Afternoon). Dogs slept more at night,
engaged in sedentary activity in the morning and the afternoon, and engaged in
physical activity (light or moderate-vigorous) during the morning.

##  7\. Methodology

Using the extracted features and self-reported personality (Section 6), we set
out to understand which features are associated with dog personality (RQ2),
and, to what extent these features are predictive of personality (RQ3). In so
doing, we defined our dependent variables, conducted a series of statistical
analyses, and developed machine learning classifiers to predict dog
personality, which we describe next.

###  7.1. Dependent Variables

The ten personality traits of both DPQ and MCPQ-R (five each) served as our
dependent variables for both statistical analyses and classification tasks. We
binarized each personality trait (i.e., whether a dog scored high or low in a
given trait —for example, high or low in fearfulness) using the median value
across all dogs in our dataset. In other words, we computed the median value
across all dogs for each trait, and binarized each trait to be either high or
low. The choice of binary traits was reinforced by previous literature on
inferring human personality from mobile data (Khwaja et al., 2019; Vinciarelli
and Mohammadi, 2014).

###  7.2. Statistical Analyses

These analyses allowed us to identify statistically significant features that
help discriminate between high and low personality scores for each trait
(Table 2). We report the top five features for each personality trait with:
_a)_ the highest $t$-statistic999$p$-values (Greenland Sander et al., 2016)
are marked with an asterisk (*) after bonferroni correction (Weisstein, 2004).
(Kim, 2015b), and _b)_ the highest Cohen’s-d101010the 95% confidence intervals
(Lakens, 2013) overlapping with zero are marked with an asterisk (*). (Rice
and Harris, 2005). As a rule of thumb, a Cohen’s-d of 0.2 illustrates a small
effect size, 0.5 a medium effect size, while 0.8 a large effect size (Kim,
2015a). Results are presented in Section 8.1.

###  7.3. Classification and Cross-Validation Methods

In the next set of experiments, we used Python with Keras (Chollet et al.,
2015) and scikit-learn (Pedregosa et al., 2011) frameworks. For dimensionality
reduction, we used principle component analysis (PCA) (Abdi and Williams,
2010) and retained features with a variance of 95% (leaving us with 3-5
features to train models, depending on the set of features used—we discuss
these features in a subsequent section). All experiments were done with the
leave-k-dogs-out strategy (in a similar manner to the leave-one/K-out setting,
which is typical cross-validation for human subjects (Meegahapola et al.,
2021)) in which data in training and testing splits do not come from the same
dog. Hence, this is subject-independent (Meegahapola et al., 2022). We
conducted all experiments with five iterations and K = 4 — that is, in each
experiment, four dogs were left out for the testing set such that this set
contains instances from both high and low scores of each personality trait.
This allowed us to measure the mean and standard deviation of the models’
performance across iterations. Given the small sample size, the choice of four
dogs was a reasonable one. As for performance metric, we chose the area under
the receiver operating characteristic curve (AUC), which is a holistic measure
assessing how well a model performs for both classes (i.e., distinguishing
high and low traits) (Bradley, 1997). In total, we set up three experiments,
and tested four types of models (S1) using a combination of features (S2 and
S3).

  * •

S1—Model Types: Given the small dataset size (typical in animal studies
(Hirskyj-Douglas et al., 2021; Weiss et al., 2013; Ladha et al., 2013)), we
used four types of classifiers: _(a)_ Support Vector Machines (SVM) (Noble,
2006), _(b)_ Light Gradient Boosting Machine (L-GBM) (Ke et al., 2017), _(c)_
Naive Bayes (NB) (Webb et al., 2010), and _(d)_ Random Forest (RF) (Cutler et
al., 2011).

  * •

S2—Feature Types: As previously mentioned (Section 6.1), we generated two main
types of features from the inertial data: activity-level features (ACT) and
statistical features (STAT). The former set of features is typically more
interpretable but costly to obtain due to the power processing needed to
generate the features, while the latter set of features is not computationally
expensive but it is less interpretable. In addition, we used demographic
attributes (DEM) such as sex of the dog, weight, age, neutered, and training
rating as input to the model because prior work suggested connections between
these attributes and dog personality (Section 2). As the dog owner’s
personality has been previously linked to the dog’s personality and well-being
(Kubinyi et al., 2009; Höglin et al., 2021), we also used the dog owner’s
information (O-INFO) including their sex and personality traits captured from
the Personality Inventory (Gosling et al., 2003b).

  * •

S3—Time of Day: These features were computed for three time periods of the day
(i.e., night, morning, and afternoon). In S1 and S2, we used all the features.
As mentioned in Section 7, the same feature captured at different times of day
may have differences in statistical significance values. For example, sleeping
time in the morning (M) could be informative to discriminate high and low
levels of Extraversion, while sleeping time in the afternoon (A) might not be.
Hence, in this set of experiments, we incorporated the time period of the day,
and sought to understand whether developing separate models for different time
periods yield better performance. For example, if a model trained with data
from only morning features performs better, it would mean that we only need
six hours of data from a dog to perform the inference.

##  8\. Results

Table 2. t-statistic (p-value=<0.05:*; =<0.5:**; >0.5: ***) and Cohen’s-d (95%
confidence intervals do not overlapping with zero: *) of activity-level and
statistical features for personality traits in DPQ and MCPQ-R: Night
(12am-6am), Morning (6am-12pm), and Afternoon (12pm-6pm) are denoted by N, M,
and A, respectively; t-statistic and Cohen’s-d values are sorted in descending
order, with the highest value of each trait in boldface. Results for activity-
level features (left) and statistical features (right) are shown separately.
Standard notation min, max, mean, median, std were used for minimum, maximum,
mean, median, and standard deviation of the signal, respectively. Other
notations include: acceleration – acceleration value was calculated in Section
6.1, acc – accelerometer, gyro – gyroscope, x,y,z – axes of the accelerometer
and gyroscope, % – percentage of time spent doing a particular activity, ecdf
– empirical cumulative distribution function.

| Activity-Level | Statistical
---|---|---
| DPQ
|  | t-statistic |  | Cohen’s-d |  | t-statistic |  | Cohen’s-d
Fearfulness | sedentary % (A) | (-) 3.55* | sedentary % (A) | 0.88* | gyro z histogram_5 (N) | (+) 17.92* | gyro z histogram_5 (N) | 4.46*
light % (N) | (-) 3.12* | light % (N) | 0.78* | gyro z histogram_5 (M) | (+) 13.07* | gyro z histogram_5 (M) | 3.25*
acceleration std (M) | (+) 2.46* | acceleration std (M) | 0.61* | gyro z histogram_6 (N) | (+) 12.86* | gyro z histogram_6 (N) | 3.21*
sedentary % (M) | (-) 2.43* | sedentary % (M) | 0.60* | gyro z zero_crossing_rate (N) | (+) 9.94* | gyro z zero_crossing_rate (N) | 2.47*
sleep % (A) | (+) 2.37* | acceleration std (A) | 0.59* | gyro z histogram_6 (M) | (+) 9.56* | gyro z histogram_6 (M) | 2.38*
Aggression Towards People | light % (M) | (+) 1.74** | light % (M) | 0.43 | gyro y histogram_8 (N) | (-) 4.44* | gyro y histogram_8 (N) | 1.11*
light % (A) | (+) 1.31** | light % (A) | 0.32 | gyro y histogram_4 (N) | (+) 4.40* | gyro y histogram_4 (N) | 1.09*
sleep % (M) | (-) 1.16*** | sleep % (M) | 0.29 | acc x histogram_5 (M) | (-) 4.38* | gyro y histogram_8 (M) | 1.09*
moderate-vigorous % (N) | (-) 1.08*** | moderate-vigorous % (N) | 0.27 | gyro y histogram_8 (M) | (-) 4.35* | acc x histogram_5 (M) | 1.09*
light % (N) | (+) 0.72*** | light % (N) | 0.17* | gyro y histogram_7 (M) | (-) 4.33* | gyro y histogram_7 (M) | 1.08*
Excitability | sedentary % (A) | (+) 2.05* | sedentary % (A) | 0.51* | gyro z histogram_5 (M) | (-)v5.86* | gyro z histogram_5 (M) | 1.45*
acceleration std (A) | (-) 1.50** | acceleration std (A) | 0.37 | gyro z histogram_5 (N) | (-) 5.85* | gyro z histogram_5 (N) | 1.45*
light % (M) | (-) 1.44** | light % (M) | 0.36 | gyro z zero_crossing_rate (M) | (-) 5.67* | gyro z zero_crossing_rate (M) | 1.41*
sedentary % (M) | (+) 1.32** | sedentary % (M) | 0.33 | gyro z zero_crossing_rate (N) | (-) 5.63* | gyro z zero_crossing_rate (N) | 1.40*
acceleration max (A) | (-) 1.27* | acceleration max (A) | 0.32 | gyro y zero_crossing_rate (N) | (-) 5.54* | gyro y zero_crossing_rate (N) | 1.37*
Responsiveness to Training | light % (M) | (-) 2.17* | light % (M) | 0.52* | acc x mean (M) | (+) 4.06* | acc x mean (M) | 0.99*
light % (A) | (-) 1.76** | light % (A) | 0.42 | gyro x ecdf__perc_0 (M) | (+) 3.87* | gyro x histogram_8 (A) | 0.97*
sleep % (M) | (+) 1.52** | sleep % (M) | 0.38 | gyro z ecdf__perc_0 (M) | (+) 3.81* | gyro x histogram_7 (A) | 0.95*
light % (N) | (-) 1.08** | acceleration std (A) | 0.27 | gyro z median (M) | (+) 3.73* | gyro z histogram_8 (M) | 0.95*
acceleration std (A) | (+) 1.06** | light % (N) | 0.26 | gyro z median (N) | (+) 3.71** | gyro x ecdf__perc_0 (N) | 0.94*
Aggression Towards Animals | sedentary % (M) | (+) 2.80* | sedentary % (M) | 0.69* | acc y neighbourhood_peaks (M) | (+) 5.43* | acc y neighbourhood_peaks (M) | 1.35*
sleep % (M) | (-) 2.61* | sleep % (M) | 0.65* | acc z neighbourhood_peaks (M) | (+) 5.20* | acc z neighbourhood_peaks (M) | 1.29*
acceleration mean (M) | (+) 2.33* | acceleration mean (M) | 0.58* | gyro y zero_crossing_rate (M) | (+) 5.13* | gyro y zero_crossing_rate (M) | 1.27*
acceleration median (M) | (+) 2.32* | acceleration median (M) | 0.58* | gyro x neighbourhood_peaks (M) | (+) 5.06** | gyro x neighbourhood_peaks (M) | 1.25*
acceleration median (N) | (+) 2.32* | acceleration median (N) | 0.58* | gyro x ecdf_0 (M) | (-) 4.99* | gyro z ecdf_percentile_count_0 (M) | 1.24*
| MCPQ-R
|  | t-statistic |  | Cohen’s-d |  | t-statistic |  | Cohen’s-d
Extraversion | acceleration min (M) | (+) 2.34* | acceleration min (M) | 0.60* | gyro x histogram_4 (N) | (-) 4.86* | gyro x histogram_4 (N) | 1.24*
acceleration max (A) | (+) 2.19* | acceleration max (A) | 0.56* | gyro x zero_crossing_rate (N) | (-) 4.23* | gyro x zero_crossing_rate (N) | 1.08*
acceleration min (A) | (+) 2.09* | acceleration min (A) | 0.54* | acc y histogram_8 (M) | (-) 3.71* | acc y histogram_1 (A) | 0.94*
acceleration min (N) | (+) 1.96** | acceleration min (N) | 0.50* | acc y histogram_1 (A) | (+) 3.68* | acc y histogram_8 (M) | 0.92*
acceleration mean (N) | (+) 1.95** | acceleration mean (N) | 0.50* | gyro y auc (M) | (-) 3.45* | gyro y auc (M) (N) | 0.86*
Motivation | sedentary % (M) | (+) 3.22* | sedentary % (M) | 0.80* | gyro z histogram_7 (M) | (-) 4.45* | gyro z histogram_7 (M) | 1.10*
acceleration std (M) | (-) 3.11* | acceleration std (M) | 0.76* | gyro z histogram_6 (M) | (-) 4.25** | gyro z histogram_6 (M) | 1.06*
sleep % (A) | (-) 2.58* | acceleration min (M) | 0.65* | gyro z histogram_9 (N) | (-) 4.18** | gyro z median (M) | 1.05*
acceleration min (M) | (+) 2.57* | sleep % (A) | 0.64* | gyro z median (M) | (-) 4.18* | gyro z median (N) | 1.05*
sleep % (M) | (-) 2.40* | sleep % (M) | 0.60* | gyro z histogram_7 (A) | (-) 4.18* | gyro z median (A) | 1.04*
Training Focus | light % (A) | (-) 1.88** | light % (A) | 0.46 | gyro x histogram_4 (M) | (+) 4.74* | gyro x histogram_4 (M) | 1.17*
acceleration std (M) | (-) 1.80** | acceleration std (M) | 0.44 | acc x histogram_5 (M) | (+) 4.71* | acc x histogram_5 (M) | 1.17*
moderate-vigorous % (M) | (-) 1.69** | moderate-vigorous % (M) | 0.41 | acc x mean (M) | (+) 4.56* | acc x mean (M) | 1.12*
sedentary % (A) | (-) 1.59** | sedentary % (A) | 0.40 | acc x median (M) | (+) 3.98* | acc x median (M) | 0.98*
light % (M) | (-) 1.59** | light % (M) | 0.39 | gyro y negative_turning_points (M) | (+) 3.88* | gyro y negative_turning_points (M) | 0.96*
Amicability | sleep % (N) | (-) 3.80* | sleep % (N) | 0.97* | gyro y zero_crossing_rate (N) | (-) 9.79* | gyro y zero_crossing_rate (N) | 2.42*
sleep % (M) | (-) 3.44* | sleep % (M) | 0.87* | gyro y zero_crossing_rate (M) | (-) 9.15* | gyro y zero_crossing_rate (M) | 2.30*
sedentary % (M) | (+) 3.29* | light % (M) | 0.84* | gyro y histogram_4 (M) | (-) 9.03* | gyro y histogram_4 (M) | 2.22*
light % (M) | (+) 3.20* | sedentary % (M) | 0.83* | gyro y histogram_4 (N) | (-) 8.89* | gyro y histogram_4 (N) | 2.21*
accleration min (M) | (+) 3.17* | accleration min (M) | 0.83* | gyro y histogram_5 (M) | (-) 7.82* | gyro y histogram_5 (M) | 1.94*
Neuroticism | acceleration std (M) | (+) 3.02* | acceleration std (M) | 0.75* | gyro z histogram_8 (N) | (+) 6.19* | gyro z histogram_8 (N) | 1.53*
moderate-vigorous % (M) | (+) 2.72* | acceleration min (M) | 0.67* | gyro z histogram_7 (N) | (+) 5.80* | gyro z histogram_7 (N) | 1.44*
acceleration min (M) | (-) 2.69* | moderate-vigorous % (M) | 0.67* | gyro z histogram_9 (N) | (+) 5.78* | gyro z histogram_9 (N) | 1.43*
acceleration max (A) | (-) 2.49* | acceleration max (A) | 0.62* | gyro z histogram_9 (M) | (+) 5.53* | gyro z histogram_9 (M) | 1.37*
acceleration min (A) | (-) 2.44* | acceleration min % (A) | 0.61* | gyro z ecdf_percentile_1 (M) | (+) 5.43* | gyro z ecdf_percentile_1 (M) | 1.35*

Table 3. Inference results for each personality trait in DPQ and MCPQ-R for
different types of models: Mean ($\bar{S}$) and Standard Deviation
($S_{\sigma}$) area under the receiver operating characteristic curve (AUC)
computed from five iterations. Results are presented as $\bar{S}(S_{\sigma})$,
where $S$ is AUC score, and the highest performing model is marked in bold
text. SVM: Support Vector Machines; L-GBM: Light Gradient Boosting Machine;
NB: Naive Bayes; RF: Random Forest.

| DPQ
---|---
| Fearfulness | Aggression Towards People | Excitability | Responsiveness to Training | Aggression Towards Animals
Baseline | .50 (.00) | .50 (.00) | .50 (.00) | .50 (.00) | .50 (.00)
SVM | .71 (.02) | .66 (.05) | .59 (.03) | .72 (.06) | .63 (.09)
L-GBM | .76 (.09) | .68 (.06) | .61 (.08) | .66 (.03) | .59 (.10)
NB | .71 (.05) | .63 (.08) | .62 (.02) | .65 (.03) | .59 (.04)
RF | .78 (.07) | .65 (.08) | .62 (.08) | .70 (.09f) | .68 (.06)
| MCPQ-R
| Extraversion | Motivation | Training Focus | Amicability | Neuroticism
Baseline | .50 (.00) | .50 (.00) | .50 (.00) | .50 (.00) | .50 (.00)
SVM | .64 (.01) | .70 (.02) | .73 (.06) | .67 (.03) | .62 (.09)
L-GBM | .62 (.07) | .69 (.11) | .70 (.08) | .71 (.05) | .70 (.08)
NB | .64 (.11) | .72 (.05) | .81 (.02) | .70 (.08) | .70 (.02)
RF | .59 (.07) | .76 (.07) | .89 (.13) | .74 (.08) | .73 (.11)

Table 4. Random Forest inference results for each personality trait in DPQ and
MCPQ-R for different types of features: Mean ($\bar{S}$) and Standard
Deviation ($S_{\sigma}$) Area Under the receiver operating characteristic
Curve (AUC) computed from five iterations. Results are presented as
$\bar{S}(S_{\sigma})$, where $S$ is AUC, and the highest performing model is
marked in bold text. ACT: activity level features; STAT: statistical features;
DEM: dog demographic attributes including its sex, weight, age, training
rating, and whether neutered; O-INFO: dog owner’s sex and personality traits.

| DPQ
---|---
| Fearfulness | Aggression Towards People | Excitability | Responsiveness to Training | Aggression Towards Animals
B1 | .50 (.00) | .50 (.00) | .50 (.00) | .50 (.00) | .50 (.00)
B2: O-INFO | .54 (.10) | .48 (.15) | .40 (.18) | .52 (.11) | .49 (.09)
B3: DEM | .57 (.08) | .49 (.06) | .48 (.08) | .55 (.07) | .50 (.12)
G1: ACT | .67 (.05) | .47 (.02) | .60 (.06) | .53 (.08) | .56 (.05)
G2: STAT | .55 (.05) | .47 (.13) | .25 (.09) | .58 (.07) | .53 (.04)
G3: ACT+DEM | .80 (.11) | .63 (.13) | .47 (.12) | .61 (.15) | .67 (.05)
G4: STAT+DEM | .63 (.08) | .57 (.11) | .33 (.11) | .70 (.04) | .59 (.04)
G5: ACT+STAT | .78 (.07) | .65 (.08) | .62 (.08) | .70 (.09) | .68 (.06)
G6: ACT+STAT+DEM | .61 (.03) | .65 (.04) | .51 (.09) | .61 (.09) | .51 (.06)
| MCPQ-R
| Extraversion | Motivation | Training Focus | Amicability | Neuroticism
B1 | .50 (.00) | .50 (.00) | .50 (.00) | .50 (.00) | .50 (.00)
B2: O-INFO | .43 (.02) | .52 (.04) | .56 (.03) | .47 (.10) | .51 (.11)
B3: DEM | .44 (.05) | .54 (.08) | .56 (.04) | .49 (.09) | .52 (.10)
G1: ACT | .41 (.03) | .63 (.06) | .47 (.06) | .41 (.04) | .57 (.02)
G2: STAT | .64 (.06) | .36 (.11) | .34 (.04) | .71 (.05) | .69 (.09)
G3: ACT+DEM | .40 (.09) | .77 (.09) | .89 (.08) | .38 (.07) | .47 (.02)
G4: STAT+DEM | .63 (.04) | .33 (.08) | .43 (.06) | .70 (.08) | .67 (.09)
G5: ACT+STAT | .59 (.07) | .76 (.07) | .89 (.13) | .74 (.08) | .73 (.11)
G6: ACT+STAT+DEM | .62 (.04) | .33 (.08) | .43 (.07) | .63 (.06) | .70 (.09)

###  8.1. Activity Level and Statistical Features Discriminating Dog
Personality

The statistical features showed high $t$-statistic, low $p$-values, and high
Cohen’s-$d$ values for a majority of dog personality traits on both
questionnaires (Table 2). Most of these features were derived from gyroscope,
and were captured during the morning. The activity-level features also showed
high Cohen’s-d values, but not as high as the statistical ones.

  * •

DPQ: For _Fearfulness_ , the percentage of time spent in sedentary activity in
the afternoon had a Cohen’s-d of 0.88, and the amount time of time spent doing
light activity at night had a Cohen’s-d of 0.78; both of which have a large
effect size. This translates into saying that a dog’s sedentary activity in
the afternoon or light activity at night are both informative of high vs. low
levels of fearfulness. These were in fact the highest Cohen’s-d obtained for
any activity-level features. In general, the lowest Cohen’s-d values came for
_Aggression Towards People_. For that trait, light activity percentage at
night, morning, and afternoon had Cohen’s-d of 0.17, 0.43, and 0.32,
respectively, while moderate-vigorous activity level during night had a
Cohen’s-d of 0.27. These features had above small effect size with low
reliability (because the 95% confidence interval was crossing zero in many
cases), thus no association could have been drawn.

  * •

MCPQ-R: Across all traits, the statistical features had comparably similar
values for $t$-statistic and Cohen’s-$d$ to those previously obtained by DPQ.
However, activity-level features had Cohen’s-d for MCPQ-R higher compared to
those for DPQ, showing a better discriminative capability of high class vs.
low class for MCPQ-R traits compared to DPQ traits. In particular, statistical
features capturing acceleration (i.e., dog movements throughout the day) had
Cohen’s-d values above 0.5, suggesting a link between Extraversion and high
levels of activity; a finding in line with previous work (Carrier et al.,
2013).

These results suggest that both activity levels and the statistical features
have discriminative power to various degrees, allowing us to draw conclusions
on which features are associated with dog personality (RQ2).

Figure 7. Area Under the receiver operating characteristic Curve (AUC) score
comparison for DPQ traits with models that used features from: night (N);
morning (M); afternoon (A); and all time periods (N+M+A). Feature type
combinations (G1-G6 from Table 4) that provided the AUC score is marked in
white color at the bottom of each bar.

Figure 8. Area Under the receiver operating characteristic Curve (AUC) score
comparison for MCPQ-R traits with models that used features from: night (N);
morning (M); afternoon (A); and all time periods (N+M+A). Feature type
combinations (=G1-G6 from Table 4) that provided the AUC score is marked in
white color at the bottom of each bar.

###  8.2. Predicting Dog Personality

Table 3 shows the classification results for four model types predicting dog
personality from both activity levels and statistical features. The columns
show the performance for each personality trait, while the rows show model
types. The baseline for all experiments is 0.5 as the testing sets were
balanced (Meegahapola et al., 2022). For DPQ, the best AUCs were obtained with
Random Forest (in the range of 0.62-0.80), so for MCPQ-R (in the range of
0.63-0.89). AUC was highest for _Training Focus_ with a score of 0.89 using
Random Forest, and lowest for _Excitability_ with a score of 0.59 using SVM.
In general, the AUC scores of DPQ questionnaire were lower compared to those
of MCPQ-R, meaning that our sensed features are better at classifying MCPQ-R
traits (in line with the results in Section 8.1). Given that Random Forest
models showed the best performance for the majority of inferences, for
brevity, we only present that model’s results in the remainder of this
section.

Table 4 shows the performance for various feature type combinations, and two
additional baseline models using: _a)_ dog owner’s sex and personality (B2:
O-INFO), and _b)_ dog demographics (i.e., sex, weight, age, training rating,
and whether neutered) (B3: DEM). Models that used activity-level features
alone performed with AUC scores in the range of 0.47-0.67 for DPQ, and
0.41-0.63 for MCPQ-R. Statistical features performed worse than activity-level
features with AUC scores in the range of 0.25-0.58 for DPQ, and 0.34-0.71 for
MCPQ-R. This suggests that, while being more interpretable, activity-level
features offer higher predictive accuracies. When adding dog demographics to
the models, their performance increased by considerable margins. The best
performance for most dog personality traits was obtained when either one or
both sensed feature types (activity-level and statistical features) were
combined with demographic features. Overall, DPQ traits had AUC scores in the
range of 0.62-0.80, with two traits above 0.70, while MCPQ-R traits had scores
in the range of 0.63-0.89, with four traits having scores above 0.70. These
results show that a combination of sensed features is predictive of dog
personality traits with reasonable AUC scores above 0.70.

When the same features were computed at different times of day, they
contributed differently to the predictive power. Figure 7 (DPQ) and Figure 8
(MCPQ-R) show varying performances for different time period-specific models
(night, morning, or afternoon) compared to generic models that used the
features computed throughout the whole day. For DPQ, models that used morning
features were the best for predicting _Fearfulness_ and _Excitability_ ,
afternoon features were the best for _Responsiveness to Training_ and
_Aggressiveness Towards Animals_ , and finally, night features were the best
for _Aggressiveness Towards People_. For MCPQ-R, models that used period-
specific features did not yield better results—with the exception of
_Motivation_ that yielded an AUC of 0.90 with night features. These results
suggest that it would be better to use period-specific models for DPQ, and
generic models for MCPQ-R.

Overall, these results suggest that a specific combination of features works
best (i.e., using activity levels, statistical features, and demographics
together), that the same feature computed at different times of day
contributes differently to prediction power, yielding higher accuracy when
predicting DPQ traits (RQ3).

Figure 9. Response distribution from dog owners about features they would like
to see in future dog wearables. Most of them mentioned measurements of aspects
that are hard to quantify (e.g., psychological and behavioral states as
proxies to a dog’s health, feelings, mood, stress, and personality).

###  8.3. Follow-up Survey with Dog Owners

Dog owners answered a series of open-ended questions (in a free-text form)
about the usability of Patchkeeper and a series of Likert-scale questions
about the desirability of features in future dog monitoring wearables (Figure
9). We structured their responses into two sections: _Usability of
PatchKeeper_ and _Future dog monitoring wearables._ As a convention, for dog
owners’ quotes, we use the letter P (indicating a human participant) followed
by the dog ID from Table 1 (e.g., P1 is the owner of Dog#1).

##### Usability of the PatchKeeper device.

A majority of dog owners had a good experience using the device. P5 mentioned
that _“ it [PatchKeeper] was easy to use and charge”_ , and P7 stated that
_“dog didn’t mind wearing it”_. While the device was overall easy to use,
owners had mixed feelings about its battery life. One said that _“battery life
is too short […] charging it every day was a hassle”_ (P9), while another
stated that “battery lasted a full day without an issue” (P2). The split
opinions about battery life, however, are very subjective and might be driven
by the charging patterns of other owner’s devices. For example, for a person
who uses a smartwatch (e.g., Apple or Samsung Galaxy watches) that needs to be
charged daily, the Patchkeeper’s battery life might seem fair. However, for
people who are accustomed to devices with longer battery lives (e.g., AmazFit,
Fitbit or Garmin), PatchKeeper’s battery life would be perceived to be much
shorter. In terms of developing tools for dog monitoring, owners provided a
range of answers, including health monitoring and understanding their pet
better. For health monitoring, P12 mentioned that _“such devices can be useful
to catch dog problems early […] like maybe breathing too fast constantly, or
cardiac problems”_. For understanding their pet better, P3 put it nicely that
“as dogs cannot speak […] a device that allows my dog to ‘speak’ and ‘express
her feelings’ is worth everything”.

##### Future dog monitoring wearables.

Dog owners expressed their opinion about a set of features for future dog
wearable devices. They provided answers on a Likert scale of 1 to 7 (1 –
strongly not preferred; 3 – not preferred; 5 – preferred; 7 – strongly
preferred), and their responses are summarized in Figure 9. Most dog owners
(66.67%) strongly preferred features that would allow them to monitor their
dog’s health. The second most desirable feature was to understand how dogs
feel when they meet other dogs. Moreover, even though it was sixth in terms of
the mean score, 58.33% of dog owners (7 out of 12) gave a rating of seven out
of seven for a feature that measured the personality of their dogs. This shows
that dog owners were polarized regarding knowing dog personality. Another
feature that received a very high rating was the ability of a wearable to
track the mood and stress of dogs. In fact, most of the preferred features
were about measures that are hard to quantify (e.g., psychological and
behavioral states as proxies for a dog’s health, feelings, mood, stress, and
personality).

##  9\. Discussion

###  9.1. Summary of the Results

In summary, our results showed that features captured from the inertial
measurement unit along with dog demographic features are predictive of dog
personality traits with reasonable AUC scores in the range of 0.62-0.89 (Table
4) in leave-K-dogs-out setting, with K = 4 (around 33% of data was used for
testing). In addition, as shown in Table 3, we found that random forest
classifiers performed best for the majority of inferences. However, results in
Figure 7 and Figure 8 showed that, by using separate models for a particular
time of day (i.e., night, morning, or afternoon), and by using different
feature groups for each model (i.e., different combinations of activity level,
statistical, and demographic features), led to increased performance. In
particular, we observed an increased performance across all five personality
traits under DPQ (AUC scores of 0.89 for _Fearfulness_ ; 0.75 for _Aggression
towards People_ ; 0.65 for _Excitability_ ; 0.78 for _Responsiveness to
Training_ ; and 0.75 for _Aggression towards Animals_), while the performance
gains for MCPQ-R (AUC scores of 0.63 for _Extraversion_ ; 0.90 for
_Motivation_ ; 0.89 for _Training Focus_ ; 0.74 for _Amicability_ ; and 0.73
for _Neuroticism_) was only visible for one personality trait, that is,
Motivation. This highlights that capturing data from a certain period of the
day provides better predictive power for certain personality traits, while for
other traits, using all available features was a better option.

###  9.2. Implications

Our work has both theoretical and practical implications. From a theoretical
standpoint, our work adds empirical evidence to the growing body of research
on animal personality (Gosling, 2008). We corroborated the previous findings
suggesting that more extraverted dogs are associated with higher levels of
activity (measured through our device’s accelerometer sensor) (Carrier et al.,
2013), while amicable dogs engage in light activity (as previous work found
(Carrier et al., 2013)). When it comes to aggression towards other animals, we
found a moderate association with light activity. While previous work
associated aggression with higher activity levels, they did so by studying
Siberian Husky dogs (Wan et al., 2013); a breed not well represented in our
sample. Our models also showed high performance for inferring fearfulness.
Even though activity levels and fearfulness were not directly linked in
previous literature, a possible explanation could be attributed to the
relationship between activity levels and negative emotions (or stress), and
negative emotions and stress have been associated with fearfulness (Beaudet et
al., 1994; Rayment et al., 2015; Jones et al., 2014). Overall, these results
corroborate previous findings in the literature. At the same time, our study
provides a fine-grained empirical analysis of the relationship between
personality traits and activity levels, extending our theoretical
understanding. Beyond animal personality research, our work contributes to the
Animal-Computer Interaction literature, including recruitment techniques for
in-the-wild studies and the use of pre-trained models for animal activity
monitoring. In this work, we argue that dog monitoring could go beyond the
typical activity level recognition to capture hard-to-quantify psychological
aspects such as dog personality. Turning into our data processing pipeline, we
showed that state-of-the-art wearable processing pipelines tailored to humans
transferred, to a great extent, to our animal study. In a way, this might seem
obvious because accelerometer data capture motion. However, only when data
from dogs were properly scaled and processed (using a 1G scale) the pipeline
and the inferred activities started to work (§6.1.1). This finding holds great
promise for future research in dog activity recognition. As for recruitment
techniques, it was evident that dog recruitment required the element of trust.
Word of mouth and recruiting in physical proximity through NextDoor turned out
to be the best technique. Traditional techniques such as mailing lists,
distributing leaflets, and posting on social media (Twitter, Facebook,
Instagram) worked to a lesser extent. Another challenge was to retain
participants. Given the battery life of 24 hours (even though it is similar to
consumer-grade wearables such as Apple Watches111111around 18 hours of
battery: https://www.apple.com/uk/apple-watch-series-7/), dog owners found it
time-consuming and cumbersome to charge the device daily.

From a practical perspective, our findings speak to both dog owners and
shelters. A practical application would be a ‘dog health app’ that tracks a
dog’s behavior patterns over time and detects its personality or psychological
aspects, such as the pet’s valence, arousal, and stress levels. Such an app
could increase dog owners’ awareness of their pet’s health and allow them to
take proactive actions (e.g., walk the pet to reduce its stress levels). In
addition to dog health monitoring, our work can be used for dog socializing.
Future platforms could offer owners the ability to receive personalized
recommendations for their pets. For example, an owner could subscribe to a
service wherein tailored pet social activities are recommended, or their pet
is matched with another ‘like-minded’ pet. Similar to how dating apps allow
like-minded individuals to match, such a platform could offer the same
experiences for a dog-dog social matching. Finally, dog shelters could benefit
by developing platforms for matching dogs with prospective owners. Currently,
matching dogs to owners based on personality is difficult for both small and
large shelters because of the sheer amount of effort needed to characterize
dog personality. On the one hand, using experts to do personality assessments
needs specialized facilities and money. On the other hand, psychological
scales are time-consuming, prone to biases, and require someone who knows the
dog very well. In contrast, letting dogs wear a device like Patchkeeper for a
week (or a few days) and obtaining a data-driven personality assessment could
be immensely useful.

###  9.3. Limitations and Future Work

Our work has several limitations that call for future research. First, we
modeled dog personality by considering time-level (i.e., features were
extracted by considering the signal for three time periods: night, morning,
afternoon) and day-level (i.e., features were extracted by combining the
signal of the three time periods) data. This allowed us to obtain several data
points of the same dog on different days. In this way, we ensured robustness
but also increased the relatively small (N=12 dogs) dataset size for
inferences (up to 72 dog days). Second, while we obtained reasonable results
with a small sample size, future studies could replicate our methodology with
larger sample sizes. However, it is also worth noting that recruiting pet dogs
for an in-the-wild study that runs for several days is challenging, and
previous work had to resort to similar/lower sample sizes (Hirskyj-Douglas et
al., 2021; Weiss et al., 2013; Ladha et al., 2013). Additionally, while we
enforced the same time schedule for the data collection to obtain comparable
results, we acknowledge that different dogs might have different routines.
Dogs with similar psychological readings (i.e., personality), but different
routines might end up with different physiological readings (i.e., activity
levels). Thus future studies could account for dog routines.

Our findings are based on the assumption that dog activity levels serve as a
good proxy for activity types. Given that the range of activity types in dogs
is narrower (e.g., walking, eating, sleeping, running (Hussain et al., 2022))
compared to that of humans, that was a reasonable assumption. While we
prompted dog owners to share activity-type labels with supporting pictures and
videos with us (e.g., eating, drinking, running, playing), not all owners were
compliant, preventing any further analysis. Future studies could attempt to
disentangle dog activity types from dog activity levels by building upon our
results. Third, while we resorted to previous literature to control for
factors that might have influenced our results (e.g., dog demographics such as
sex, age, and neutering (Kubinyi et al., 2009; Lofgren et al., 2014)), future
studies may well incorporate additional factors such as the size of the dog’s
living environment, or even the presence of other pets in that environment.
Fourth, all the dogs in the study, are from the same city in the United
Kingdom. Thus, whether these results replicate in other cities or countries
remains a subject of future work, specially given that the generalization of
mobile sensing-based models across countries is an important topic of interest
(Meegahapola et al., 2023; meegahapola2021smartphone). Fifth, data collection
occurred during the summer period and might not be generalizable. Therefore,
future studies could explore whether our findings generalize to other seasons
(i.e., winter, fall, spring), when dog behaviors vary (especially in countries
closer to hemispheres, where weather drastically changes in different
seasons121212https://www.pdsa.org.uk/pet-help-and-advice/pet-health-
hub/conditions/seasons-in-dogs). Sixth, capturing personality could be done in
many ways, and, in this study, we only focused on two commonly used
personality measurement questionnaires that are filled by dog owners, which
were specifically designed for shelter rehoming. Future work should evaluate
these scales in the context of other facilities in the USA, and could also
explore other dog personality measurement techniques (i.e., test batteries,
experts).

##  10\. Conclusion

We built a device called “Patchkeeper” that can be strapped on a dog’s chest
and that measures its activity through an accelerometer and a gyroscope. We
experimented with the device on 12 dogs and collected sensor activity data for
a week, along with dog personality test results. By matching these two
datasets, we trained machine learning classifiers that predicted dog
personality from activity data. We found that a combination of activity-level
features (describing the activity as sleeping, sedentary, light, or moderate-
vigorous) and statistical features (describing temporal and statistical
aspects of the time-series accelerometer and gyroscope data) extracted from
sensor data, together with dog demographics worked the best. We also found
that the same feature computed at different times of day contributed
differently to prediction power, with morning features being predictive of
fearfulness and excitability, afternoon features being predictive of
responsiveness to training and aggressiveness towards animals, and night
features being predictive of aggressiveness towards people and motivation.

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