Cat@Log: Sensing Device Attachable to Pet Cats for
Supporting Human-Pet Interaction
Kyoko Yonezawa

Takashi Miyaki

Jun Rekimoto

Interfaculty Initiative inInformation Studies, The
University of Tokyo
7-3-1 Hongo, Bunkyo
Tokyo, Japan

Interfaculty Initiative inInformation Studies, The
University of Tokyo
7-3-1 Hongo, Bunkyo
Tokyo, Japan

Interfaculty Initiative inInformation Studies, The
University of Tokyo
7-3-1 Hongo, Bunkyo
Tokyo, Japan

qq086413@iii.utokyo.ac.jp

miyaki@acm.org

rekimoto@acm.org

ABSTRACTIn spite of the development of technologies that support
human-computer or human-human interaction, few studies
have been conducted for improving interactions between humans and pets, pets and computers, or between two pets.
We propose a new area of research on entertainment using computers, called “human-pet interaction.” As an initial
step in this research, we have developed a series of sensing
devices that can be attached to pet cats, called Cat@Log(cat-a-log). These devices comprise various sensing units
such as a camera, a GPS, an accelerometer, and a Bluetoothmodule. Here, we attempted to determine an optimum design of the devices such that they can be attached to a pet
without causing discomfort to it; for determining this design, we considered parameters such as the device’s form
factor and way of attachment. These developed devices canrecognize the experiences and activities of cats; information
sensed by the devices is transmitted in real time by using
the Bluetooth wireless module. We used this platform anddeveloped a software system that automatically recognizes
a pet’s high-level behavior and posts it to Twitter.

Categories and Subject Descriptors
H.5.2 [User Interfaces]; I.3.6 [Methodology and Techniques]: Interaction techniques

Keywords
User Interface, Human-Pet Interaction, Life Log, Wearable
Computing, Activity Recognition

1. INTRODUCTION:
HUMAN-PET INTERACTION
Since ancient times, animals have been closely associatedwith people, in the form of pets that act as partners andcompanions. Around 63% of all U.S. households have pets.

Figure 1: Cat wearing Cat@Log sensing device.

Almost all owners wish to gain a better understanding of
the moods, feelings, and behavior of their pets. Unfortunately, it is diﬃcult to communicate with pets, because human and pets do not speak a common language. Recently,
various digital technologies have been developed for supporting human-to-human communication, such as emails, blogs,
chat rooms, and social networking sites (SNSs). However,these systems are limited to humans. If we could support
interactions between human and pets, our lives and thoseof our pets could be enriched. In spite of the developmentof human-human communication technologies, few studies
have been conducted to improve communication between
humans and pets. Thus, our goal is to establish such communication media; we term this research area “human-pet
interaction.”
The design space for pet interaction is shown in FigureFigure 3: Pet input and output. (Pet experience (pet input) is shown on the left. Here, each sensor
corresponds to one of the ﬁve senses. Pet reaction (pet output) is shown on the right. Here, the sensors
correspond to a cat’s reaction. The logging function is shown at the bottom.)this system is virtual, it indicates the possibility of a petpet interaction.
In this study, we focused on the human-pet interaction.
Here, the interaction between humans and pets is supported
by a computer. For this scenario, we could think of many
applications, as shown in Figure 2. By means of such aninteraction, humans can monitor the activities of their pet
even when the pet is not around. Thus, by using a personiﬁed interface, owners can understand their pets better.

Figure 2: Design space for pet interaction.

2. Currently, various pet-computer interaction tools forcats, that is, cat-friendly systems, are available on the market. Examples include electronic toys and automatic feeding
plates. Some researchers have developed ubiquitous computing systems for application to their pets. For example, a cat
door that opens with an RFID tag attached to a cat’s collar
has been developed[12]. This system ensures that cats apartfrom the owner’s pet cat do not enter the house; thus, thefood of the pet cat is protected. Further, a system called
Cat Cam[13] has been developed, which is a camera thatenables a cat to take photos outside of home.
We further thought of improving communication among pets.
An SNS service could be developed for investigating the interaction among pets. A Nintendo DS game called “Nintendogs” is available on the market, in which a player owes apet dog. With the help of the “Surechigai” crossing mode
available by an ad hoc Wi-Fi connection, even players who
do not know each other can exchange messages. Though

Owners feel the need to know what their pets are seeing,
hearing, doing, etc. Such pet experiences and reactions areshown in Figure 3, indicated as Pet Experience and Pet
Reaction, respectively. These two categories can also beclassiﬁed as input and output, respectively. On the input
side, data on the ﬁve senses, that is, sight, hearing, touch,smell, and taste, can be collected. On the output side, dataon the activities, location, and voice can be collected. The
sensors for collecting such data are also shown in Figure 3.

2.

CAT@LOG PROJECT

To achieve the abovementioned goals, we have developed asoftware system and a series of sensor devices that can beworn by cats. The sensor device has multiple sensors (e.g.,
accelerometers, cameras, global positioning systems (GPSs),etc.) and a storage unit in order to record activities of pets
continuously. A wireless communication system may also
be required to collect these data in real time. We mustselect these sensor devices carefully because they would be
used to collect diﬀerent information from the outside world
than that collected by humans (for example, the audible
frequency ranges of cats and humans are diﬀerent).

2.1 Pet Selection
We conducted this study with a female house cat as a pet. It
is known that cats are the most popular pets worldwide. In
addition to this reason, we selected a house cat in this study
because as reported by Driscoll[8], cats contribute virtuallynothing in the way of sustenance or work to human endeavor,
whereas other once-wild animals were domesticated for theirmilk, meat, wool or servile labor. Though cats do not serve
any such purpose, around 600 million cats are currently living among humans. Humans adopted cats as pets because
their features elicit nurturing from humans.
It is said that understanding the feelings of cats is more difﬁcult than understanding those of other pets such as dogs.
Dogs usually live in a pack with their relatives and form theirown society. Therefore, dogs are used to having a leader(leader of the pack); as a result, they quickly adjust to humans as their leaders. In contrast, cats almost always live inisolation and do not like external interference in their lives.
This is why many humans feel that cats are diﬃcult pets.
For these reasons, we focused on developing an interaction
system for pet cats.

2.2 Possible Ways of Attaching Sensor Devices
Because of the inability of cats to converse with humans,
they cannot communicate even when the device attached tothem causes them discomfort. Therefore, the device must
be designed with extra care. In the conceptual phase, we
looked at several possible ways of attaching the device, as
shown in Figure 4.

wear harnesses as well. However, this harness-type device
could be an obstacle when a cat wants to lick its body andgroom itself. Moreover, it is impossible to collect optical
data from the viewpoint of the cat’s sight because the front
side of the device is occluded by the cat’s head.
Data on the cat’s sight can be collected most accuratelyby the use of a head-mounted device (HMD) (Figure 4(b)).
This type of device oﬀers another advantage: receiving information from GPS satellites would be easy because the device
is attached to the upper part of the cat’s head. If this device
were to be attached to a human candidate’s head, the candidate would be instructed not to move his/head vigorously
in order for the device to remain stable; further, a humancandidate would also bear with the uncomfortable feeling of
having a device mounted on a body part. However, it would
be extremely diﬃcult to attach an HMD to a cat’s head.
Cats are highly temperamental, and they would dislike having any external object mounted on them, particularly on
their head. It is not diﬃcult to imagine that they would
shake their heads vigorously in an attempt to get rid of thedevice.
As a result, a collar-type device (Figure 4(c)) appears to
be most suitable for being attached to cats. Many pet cats
wear collars, and some collars even have charms on them.
The feeling of equipment attached to a collar may be quite
familiar to cats. If a camera is attached below the neck,it provides a pseudo-view of the cat’s sight. By this way
of attachment, most of the sensors would be located under
the cat’s chin. This could cause diﬃculty in collection ofdata from GPS satellites. Our solution for overcoming thisdiﬃculty is to attach an extending cable so that the GPS
antenna may be attached to the other side of the collar,
as is shown in Figure 5. This will provide easy access to
GPS satellites. Though the GPS-related problem is solved,
it would still be diﬃcult to accurately detect the cat’s lineof sight.

Figure 5: Antennas attached to back side of collartype device.

2.3
Figure 4: Possible ways of attaching sensor devices
to cats.
In the case of a harness-type device (Figure 4(a)), the loadof the device would be distributed, and the comfort level by
this way of attachment would be higher than that in thecase of other ways of attachment. Dogs, for example, wear
harnesses often when they are taken out for a walk. Filets

Sensor Selection

From the viewpoint of animal ethics, devices attached to an
animal must be less than 4 to 5% of the animal’s weight[16].
The average weight of an adult cat varies, but it is usually
between 2 and 7 kg. The weight of the participant cat inthis study is 3 kg, but we think that the developed systems
should be compatible with cats of diﬀerent weights. Fromthese considerations, we set the maximum weight of the device to 60 g, which is 3% of a cat weight of 2 kg.

To satisfy this weight condition, we must carefully select thesensors to be loaded onto the device. For this, we focusedon the point that sensors could be broadly divided into two
categories on the basis of the input and output Figure 3 ofthe cat, that is, information collection and cat’s reaction,
respectively. Since cats are dependent mostly on the senseof sight, an optical camera would be appropriate for collecting the same information that cats collect from the outerworld. Since a cat’s eye collects more light than human eyes
do, a highly sensitive camera may reproduce the cat’s view
with accuracy, but due to the weight limitation, it might bediﬃcult to mount such a camera.
Cats are also dependent on their sense of hearing; thus, we
performed an experiment in which noise from a microphone
was collected. However, cameras were more eﬀective for collecting surrounding data. As mentioned earlier, the audible
frequency range of a cat diﬀers from that of a human; thus,
the collected data may be quite diﬀerent from the data actually being received by the cat. Loading a microphone onto
the device would require power, and in consideration of thebattery endurance, we decided not to load a microphone
onto the device. However, we are considering the possibilityof loading a microphone in a future study.
Another necessary sensor is an accelerometer. Data from anaccelerometer could be analyzed to determine the activityof the cat. It is true that a cat’s activities can possibly bemonitored by a camera, but cats are small and sensitive, so
the device must be small and light so as not to be a burden
to the cat. A high-performance camera is required for analyzing and classifying cat’s activities, but it is diﬃcult tomount such a camera because of the abovementioned weight
limitation.
A location platform would also be a useful tool for determining the cat’s reaction. For cats living in urban areas, a
Wi-Fi-based location platform would yield accurate location
information, but the problems with using such a platform are
its size and weight. A GPS is smaller and lighter, and thus,
we decided to use a GPS module as a location platform.
A memory device is required to store the collected data. A
micro SD card is a popular memory device that can be readin many personal computers (PCs); therefore, we mounted
micro SD card slots on our devices.
To acquire real-time information on the cat, a wireless communication system is required. We selected a class 1 Bluetooth device to transmit information to the computer. An
XBee module was another candidate for this purpose because of its small size; however, its power consumption isgreater than that of Bluetooth modules, and a receiver mustbe attached to the computer if the XBee module is to be
used. For these reasons, computers containing Bluetoothmodules are becoming increasingly common.

one’s pet’s lifestyle; e.g., one should know when, where, andwhat one’s pet is doing. Checking the cat’s life log would
help the owner understand the cat. The owner would thenbe able to reward the cat for some diﬃcult task performed
successfully or to adjust the amount of food given to thecat in order to control its diet. An area of research, called“life log,” has been developed in the past. For example,
Clarkson [6] extracted his life patterns from wearable sensor
data for human. Application of a similar life log technology
can enable humans to determine a cat’s life log using a device
made especially for cats.
In order for owners to be able to feel more empathy for their
pet cats, the output of the system should be a personiﬁed
representation. Blogs, SNSs, emails, and chat rooms aresome of the possible outputs of the system. We now areconsidering blogs and Twitter (a mini-blog) as output media
for our Cat@Log project.

3. SYSTEM IMPLEMENTATION
3.1 Evolution of Cat@Log Devices
We have developed three kinds of devices as a part of our
Cat@Log project. Figure 6 shows the evolution of thesedevices. The latest device contains a camera module, an
accelerometer, a GPS module, a micro SD card logger, aBluetooth module, and a Li-Po Battery. Figure 1 shows thecat wearing the latest collar-type device.

3.2
3.2.1

Since the subject of this study was nonhuman, we did nothave to be concerned about privacy. However, since cats
and humans cannot communicate via a common language,
it is diﬃcult for one to determine the other’s thoughts andemotions. To solve this problem, it is important to knowVideo of Cat’s View and Stabilization of Video

Photos and videos will replace the cat’s vision. By attaching a camera near the cat’s face, we can collect informationthat cannot be obtained using an external camera. However,there are some problems associated with mounting a camera on a collar. Cats cannot hold the camera still, but they
do not generally move their heads while walking or running.
Therefore, applying video stabilization would yield accurate
data on the cat’s view. Then, we selected Takahashi’s realtime video stabilization method[15], since this method canbe used to stabilize jerky or blurred videos after they havebeen recorded. Figure 8 shows the result of video stabilization.

3.2.2

Module for Detection of Cat’s Face

We prepared a database for detecting the cat’s face, by using
data collected by the camera. We used rapid object detection with a cascade of boosted classiﬁers based on Haar-likefeatures supported in OpenCV. This program can be used
to detect desired objects by changing the database. We canuse a face detection algorithm to detect cats’ faces, as shownin Figure 9. By detecting the face of a cat, we can know the
relationship of the cat both inside and outside home.

3.3
3.3.1

2.4 Output

Camera (and Microphone)

Accelerometer
Activity recognition

We could recognize the cat’s activity from daily logging dataobtained from the acceleration sensors. The possibility ofhuman activity recognition was veriﬁed by Clarkson[5]. We
applied this technique to cats for monitoring some of theiractivities. We collected acceleration data by using a tri-axis
analog accelerometer. Results of a previous study on activity

Figure 8: Video stabilization. (Left: raw image captured by mounted camera; right: image stabilizedby video stabilization)

Figure 9: Normal human face detection (left) andcat face detection (right).

recognition using ±2G acceleration data have been found to
be promising[7]. Data obtained from the accelerometer havethe following attributes: time, acceleration along the x axis,
along the y axis, and along the z axis. The accelerometer
was worn by the author’s cat, named Ann.

Figure 6: Evolution of Cat@Log devices: (a) Withcamera and microphone, (b) with accelerometer,and (c) with camera, accelerometer, GPS, and Bluetooth.

Data on nine activities were collected: sleeping, walking,running, being brushed, eating, climbing up stairs, climbing down stairs, jumping up, jumping down, and scratching.
These activities were performed and data on them were collected over several days. Figure 10 shows some examples of
the collected data.
Features were computed on 64 sample windows of acceleration data with a 20% overlap between consecutive windows.
The sampling frequency was 50 Hz; thus, the size of eachsampling window was 1.28 s. In a previous study on humanactivity recognition [3], desired outcomes were obtained witha window size of 6.7 s with 50% overlap. However, since catsmove more quickly and are more restless than humans, we
decided to narrow down the window size to 1.28 s. It has
been proved that for faster movements, it is preferable toreduce the sampling window size and the overlap[14]. Fromeach window, we computed the mean, variance, correlation
between each axis, DC component, and AC component. We
used the fast Fourier transform algorithm to compute thelast two features.

Figure 7: Videos recorded by camera attached tocollar.

Many methods are available for classifying the activity data.
In a previous study, it was found that the recognition accuracy was highest for C4.5 decision tree classiﬁers[2, 3]. C4.5
builds decision trees from a set of training data that has
already been classiﬁed, by using the concept of information
entropy. At each node of the tree, C4.5 selects one attributeof the data that most eﬀectively splits its set of samples into
subsets enriched in one class or the other. Its criterion isFigure 10: X-axis readings for various activities.
the normalized information gain that results from selectionof an attribute for splitting the data. The attribute with thehighest normalized information gain is selected for making
the decision. The C4.5 algorithm then recurses on smaller
sublists. Here, we used the Weka Machine Learning Algorithms Toolkit[17] to preform the feature.
As an example, we can estimate the amount of exercise performed by the cat by recognizing and logging the activity
data. We can quickly know if the cat is low on energy,
by observing the blog of her life log. Further, if the cat isobserved to suddenly start scratching herself vigorously, it
could be possible to detect ﬂeas in early stages.

3.3.2 Relationship Prediction
By recognizing activities of multiple cats, we can predictthe relationship among them. Figure 11 shows acceleration
data recorded when two cats were playing together. We canpredict the relationship between them by combining location
data with a higher activity recognition method[4].

munication module. Then, if the location data are logged,
it is possible to construct a territory map of the cat.

4.

INTERFACE

The abovementioned automatic event detection technologies
enable the creation of an automatic blog-posting module bymeans of which cats can transmit their activity information
automatically. When information is automatically posted toa personiﬁed interface like blogs, it would appear as if thecat itself is writing the post.

4.1

Twitter Module

After the acceleration data are collected, they are sent to a
PC in real time. After the raw accelerometer data are sent,
the features are computed and classiﬁed into one of the nine
activities. The activities are posted in Twitter as shown in
Figure 12.

Figure 11: Acceleration data on two cats playing
together.

3.4 GPS
3.4.1 Territory Map of Cat
Cats often go outdoors, but owners are mostly never aware of
the cat’s whereabouts. However, owners would certainly liketo know the whereabouts. To achieve this, owners could send
location data from the GPS satellite via the wireless com-

Figure 12: Cat tweets on Twitter.

4.2 Blog Module
The blog is shown in Figure 13. The cat’s activity is posted
in the form of an article on the blog. The cat’s current lo-

Figure 13: Blog written by a cat using territory map widget. (The territory of the cat is indicated by yellow
markers and its present location is indicated by blue markers.)
cation and estimated territory are shown on a Google-maptype blog widget. For privacy, a static map is used. As
compared to normal Google maps, in static maps, one cannot zoom in and out or move areas. This will protect theprivacy of the map’s author. The amount of exercise performed by the cat is shown on another blog widget. RSS istransmitted to the blog’s audience. Photos and videos areposted as an article on the blog. They are sharpened andstabilized by Takahashi’s method[15].

is built using a tiny camera that periodically captures auser’s viewpoint. Clarkson[5] has used an accelerometer torecognize human activities from sensors. The methodology
of lifelog research can be applied to human-pet interaction
for understanding pet activities. However, the diﬀerence
in sensing objects usable with humans and pets should beborne in mind. In the present study, we applied veterinary
and observational knowledge for determining the optimum
design of sensor devices for cats.

5. DISCUSSIONS
5.1 Related Work

Currently, many blogs about the life of pets are active, as
typiﬁed by “BloggingCat.com[10].” In these blogs, the ownerand not the pet writes the pet’s diary. It is not written fromthe pet’s viewpoint, and thus, it is diﬃcult to get an accurate
overview of the pet’s actual life. In the service “BlogPet[1],”which is a blog widget, the pet itself can write a post. Thiskind of personiﬁed representation is eﬀective for enablingowners to communicate with their pets. However, in thiscase, the pet is just a virtual persona that is not linked to
the real world.

Various digital technologies have been developed for supporting human-to-human communication, such as emails,
blogs, chat rooms, and SNSs. However, these systems arelimited to communication among humans. Some researchers
have developed one-way communication systems for pets.
For example, “CatCam[13]” is a device that collects images
from a cat’s point of view by means of a camera mounted
on a cat’s collar. “ZooGraph[11]” is a system for extrapolating pets’ activity from an acceleration sensor. Both thesesystems do not have features to determine what pets want
and to help owners decide what actions to take with respect
to their pets. In order to make progress in our proposed
research area of human-pet interaction, two-way communication channels should be enhanced by means of various ubicomp technologies.
A research area that has recently gained popularity is called“lifelog computing.” The main purpose of this research is thearchiving of entire lives of humans and activity recognitionon the basis of the archives. A “MyLifeBits[9]” database

A service called “PigeonBlog[6]” provides an alternative way
to collect data on environmental air pollution by means of
sensors placed around a pigeon’s neck. This service appears
similar to Cat@Log at ﬁrst; however, the biggest diﬀerence
between them is that the PigeonBlog project is not actuallyfor the beneﬁt of pigeons but for improving the quality ofhuman life (e.g., for an air pollution survey). As explained
earlier in the paper, cats are ideal animals for investigating
possible ways of human-pet interaction by various communication methods; this is because the living environment ofcats is rather similar to that of humans.

5.2 Future WorkOne of the main purposes of developing the Cat@Log system was entertainment. However, if the interaction between
humans and pets is to be fruitful, pets should also beneﬁt
from the developed system. With this in mind, we are now
planning to develop a healthcare module for pets. This module can be used to log the change in the amount of exercise;thus, it would be possible to detect diseases in pets in earlystages.
We also intend to develop a platform for enhancing pet-pet
interaction. By this platform, pets can record their meetingsby ad hoc communication and exchange letters from theirowners. This may give rise to a new type of interaction
between pet owners.

6. CONCLUSION
In this paper, we have proposed a new research area termed“human-pet interaction” and developed a device that is wearable by pet cats. Pet owners can use this system to monitor
the life and feelings of their pets; in addition, owners can understand, empathize with, and love their pets better. This
device is useful even in the case that the cat and its ownerare positioned remotely. We extracted a cat’s interaction
data from a video recorded from the cat’s viewpoint, activity pattern from the acceleration data, and explored thepossibility of determining relationships between cats.

7. ACKNOWLEDGMENTS
We would like to thank Rena and Ann, my adorable cats,
for being obedient participants. Further, we are grateful to
Mr. Naruoka for providing valuable advice and assistancewith the development of the device.

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