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ORIGINAL RESEARCH
published: 19 May 2015
doi: 10.3389/fict.2015.00010
Edited by:
Bruce Hunter Thomas,
University of South Australia, Australia
Reviewed by:
Maki Sugimoto,
Keio University, Japan
Thuong Hoang,
The University of Melbourne, Australia
*Correspondence:
Kristof Van Laerhoven,
Embedded Systems, Department of
Computer Science, University of
Freiburg, Georges-Koehler-Allee 10,
Freiburg 79110, Germany
kristof@ese.uni-freiburg.de
Specialty section:
This article was submitted to Mobile
and Ubiquitous Computing, a section
of the journal Frontiers in ICT
Received: 12 March 2015
Accepted: 02 May 2015
Published: 19 May 2015
Citation:
Van Laerhoven K, Borazio M and
Burdinski JH (2015) Wear is your
mobile? Investigating phone
carrying and use habits with
a wearable device.
Front. ICT 2:10.
doi: 10.3389/fict.2015.00010
Wear is your mobile? Investigating
phone carrying and use habits with
a wearable device
Kristof Van Laerhoven
1
*
, Marko Borazio
2
and Jan Hendrik Burdinski
2
1
Embedded Systems, Department of Computer Science, University of Freiburg, Freiburg, Germany,
2
Embedded Sensing
Systems, Department of Computer Science, TU Darmstadt, Darmstadt, Germany
This article explores properties and suitability of mobile and wearable platforms for
continuous activity recognition and monitoring. Mobile phones have become generic
computing platforms, and even though they might not always be with the user, they are
increasingly easy to develop for and have an unmatched variety of on-board sensors.
Wearable units in contrast tend to be purpose-built, and require a certain degree of
user adaptation, but they are increasingly used to do continuous sensing. We explore
the trade-offs for both device types in a study that compares their sensor data and
that explicitly examines how often these devices are being worn by the user. To this
end, we have recorded a dataset from 51 participants, who were given a wrist-worn
sensor and an app to be used on their smartphone for 2 weeks continuously, totaling
638 days (or over 15,300 h) of wearable and mobile data. Results confirm findings of
previous studies from North-America and show that smartphones are on average being
on their user <23% of the time, mostly during working hours. Just as noteworthy is the
high variance in smartphone use (in carrying, interacting with, and charging the phone)
among participants.
Keywords: wearable computing, mobile computing, smartphone use, activity sensing, wrist-worn inertial sensing
1. Introduction
Mobile phones have become increasingly general purpose and personal computers that fit in the
user’s pocket, being used by a growing number of people around the world. The usage of these
general-purpose platforms is experiencing an unprecedented uptake: in 2013, almost 1 billion
devices have been sold worldwide
1
; in the year before, a recent publication by the United Nations
Organization claimed that more people worldwide had access to a mobile phone than to a clean
toilet. Additionally, approximately 6 billion people in the world have access to a mobile phone.
Statistics show a steady increase in the number of smartphone owners all over the world, indicating
also that user behavior has been gradually changing over the past years, with smartphone usage
topping that of desktop computers. At the same time, smartphones are being used more frequently
by the user. We tend to use them to manage our schedules and appointments, as navigation systems
to find our way in unknown environments, as flashlights, music players, or to obtain updates on
the daily news. Along with their variety of uses, their computing capacity is rising steadily, enabling
current generations of smartphones to be used for various application scenarios, as, for instance, in
1
Statistica: Global Smartphone Sales to End Users 2007–2013. http://goo.gl/WEkCJO
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Van Laerhoven et al. Wear is your mobile?
Farrahi and Gatica-Perez (2008), Berchtold et al. (2010), Kwapisz
et al. (2011), and Sahami Shirazi et al. (2013). Mobile devices
have as such been deployed for capturing and tracking the user’s
immediate surroundings and to recognize physical activities of
the owner in early research by Ashbrook and Starner (2002) and
Brezmes et al. (2009).
Since the acceptance of these mobile devices has reached such
a high number, they have been targeted increasingly to be used
as devices for self-monitoring and activity capture in more recent
research such as Altakouri et al. (2010), Oresko et al. (2010),
Bardram et al. (2012), and Bielik et al. (2012). These devices not
only sense the user’s frequent whereabouts as described in Mazilu
et al. (2013) but also what the user is doing in terms of physical
activities as in Sun et al. (2010), be it as part of the quantified-
self movement (Swan, 2013), in health care scenarios (Mosa et al.,
2012) or to track fitness trends (Seeger et al., 2011). These trends
caused researchers to study to what degree these mobiles have
become suitable for activity recognition in several studies over the
past years, most notably Dey et al. (2011) and Patel et al. (2006).
The results of these studies suggest that the phone is within the
user’s arms reach about half of the time during the day, indicating
that the use of a mobile platform may not be suitable for all user
monitoring applications.
We present in this article a novel approach to estimate how
frequently the mobile phone is being carried by the user. For
this purpose, we use a wrist-worn unit, which registers the user’s
physical movements through a 3D accelerometer sensor. The data
are compared to those from a custom-built Android application
that enables us to log the inertial sensing modalities that almost
every smartphone has embedded. Both recording methods, the
wrist-worn device and the android App, were designed so that
they would minimally impact their user’s phone use and main-
tenance behavior (e.g., recharging or otherwise interacting with
both devices), and this for continuous deployments with the user
over up to 2 weeks. The aim is to study smartphone carrying
behavior by asking users to continuously wear the wristwatch unit
and installing the App on their personal smartphones.
With this system, we carried out a study in which 51 participants
were recruited. We installed our Android app on the participants’
personal phones and asked them to continuously (day and night)
wear a wrist-worn accelerometer logger over the course of the
study, typically up to 2 weeks. This resulted in a total of 638 days or
15,300 h worth of mobile and wearable accelerometer data, which
can be used to analyze how often the phone and the wrist-worn
unit were actively worn by the 51 users. The results are studied in
this paper for the amount of time the two device types were worn,
but our investigations also show more in-depth analysis on how
different users manage their mobile and wearable devices, and
how consistent (or variable) these different behaviors are between
users.
The remainder of this article is structured as followed: first,
in Related Work, we will highlight research related to this study.
Then, in When is the Phone on the User? we will describe the
sensing modality we used in this paper, showing in the Study
Methodology how we proceed with the data. In Evaluation of
Study Results, we depict the results of the study. Following that, a
discussion about the limitations and benefits from our approach is
given. We conclude with the main study results in the last section,
giving also an outlook into future work.
2. Related Work
The current trend for mobile phones is not to produce smaller
and lighter models, even though technical advancements might
support this, but rather to have more built-in features, espe-
cially embedded sensors (Oyvann, 2013). Newer generations of
smartphones can manage a growing variety of tasks, and these
devices are more and more replacing the typical functionalities
of desktop or laptop computers, while being expected to be with
the user most of the time. 3D MEMS (microelectromechanical
systems) accelerometers, in particular, have become one of the
most widespread sensor modules that are embedded in the mobile
phone. Recent research has investigated the possibility of using
these sensors within mobile phones to detect common and basic
physical activities such as walking, jogging, or climbing (Kwapisz
et al., 2011), where data gathered from 29 participants indicatethat
most activities can be recognized with over 90% accuracy.
Brezmes et al. (2009) describe their approach to classify activi-
ties of a mobile phone user based on accelerometer data recorded
by the phone itself in real-time. For a Nokia N95 phone, they
used the Python API aXYZ1 to obtain the accelerometer data
and a socket-connected Java program to classify activities. While
their attempt to identify activities proved difficult in practice, the
authors did manage to get more than 70% accuracy in pattern
recognition using a set of training records for each activity and a
k-nearest neighbors algorithm on the Euclidean distance between
a current record and the previously classified records.
Similar results had already been published in a 2006 paper by
Iso and Yamazaki. In Iso and Yamazaki (2006), they report an
accuracy of around 80% for walking, running, and walking stairs
using wavelets. To be able to cope with the computational effort
of their approach, all the classification work has been done on a
dedicated server, while the phone was primarily used to collect the
data. A recent approach described in Nam et al. (2013) combines
the accelerometer with a video capturing device. In combina-
tion with optical flow techniques, they were able to increase the
accuracy for the respective gaits to an overall average of 96%.
Researchers in Reinebold et al. (2011) investigated which fea-
tures can be used to detect inactivity in a mobile phone. For this
purpose, theycollected accelerometer and gyroscope data fromsix
different participants who were asked to follow a script to obtain
movement and non-movement data over a short period of time
(5 min). Different features were extracted and the data were used
in different classification techniques (e.g., k-Nearest-Neighbor –
kNN), which yield and accuracy for detecting the aforementioned
states of approximately 95%. Whether the approach shown in
Reinebold et al. (2011) holds true for most of the phone users
has still to be evaluated, as only six participants were included in
the experiment. Nevertheless, detecting motion or non-motion is
important, especially if the goal is to determine if a mobile phone
is a suitable platform for activity recognition.
A different study in Hausmann et al. (2012) depicts how
accelerometer data can be used to track physical activities on a
mobile phone. By annotating the recorded data directly on the
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Van Laerhoven et al. Wear is your mobile?
phone, a self-tracking mechanism is presented for walking activ-
ities (according to the environment, e.g., walking on a treadmill
or flat surface, etc.) that accurately shows how much the user is
moving during the day.
As all these studies illustrate, it is feasible to use the mobile
phone as a device to detect and log the user’s activities, provided
the users carry their phones along throughout the day. Whether
such a device is suitable as a continuous sensing platform, and
whether it stays in the user’s proximity, has been investigated
before by Patel et al. (2006) and followed up by Dey et al. (2011).
Both studies conclude that users are farther away from their
phones as one might expect, by making use of the received signal
strength of a neck-worn Bluetooth token to record its distance
to the mobile phone as within arms length, within room or no
signal, based on calibration data. The study’s findings suggest
that the phone is within arms length <50% of the day, within the
room for about 65% of the time and switched off for most of the
remaining time. Interestingly, the portion of the day for which
the phone is within arms length seems to decrease from 2006 to
2011 while the amount of time the phone is in the same room has
increased. In addition, both studies indicate that the proximity
of smartphones to their users has not changed significantly in
the meantime. Since both of these studies had a relatively small
user base focused on North-America, these findings may vary
elsewhere and may have changed in the past years. Additionally
to the proximity evaluation, both studies recorded a vast amount
of sensor data from the phones and users were interviewed in
order to produce a journal about their activities during the exper-
iment. With such a journal, the user behavior was structured
into 15–20 classes of activities each related to one of the three
smartphone distances. Using a decision tree, the recorded data
were matched to one of these classes. An accurate prediction was
reached by ranking the different features based on the ground
truth data using the Bluetooth tokens. Interestingly, the study did
not find a “one-fits-all” decision tree: the ranking of the single
features for an accurate decision differed from participant to
participant.
This paper presents (1) an alternative method to those pre-
sented in Dey et al. (2011) and Patel et al. (2006) to research
user proximity to their phone, by requiring study participants to
wear an accelerometer-based logger on the wrist and install an
accelerometer-logging app on their Android phones. As a second
contribution, we present (2) a study that uses this system with
51 participants [almost double the size of Dey et al. (2011) and
Patel et al. (2006)]. By correlating the amount of motion present
in the data from their wearable unit and their mobile phone, we
estimate when the phone is being carried by the user (in the front
pocket, in the hand, in a bag, etc.). This effectively means that
the proximity measure for our method will be restricted to on the
user or elsewhere, yet we argue that this measure in itself is already
interesting for research, and that our method does have significant
advantages over the wearing of Bluetooth transceivers.
3. When is the Phone Carried by the User?
Our method depends on two sources of information that are
synchronized and matched: (1) a miniature wrist-worn sensor that
records the user’s motions, and (2) a sensor data recording app for
Android. In this approach, the data measured by the wrist-worn
unit serve as an indication on when the user was physically active,
while the data recorded by the mobile phone characterize when
the mobile phone was experiencing acceleration. A comparison
of both could therefore result in estimating when the phone is
experiencing the same acceleration as its user, and therefore when
the mobile phone was on the user. After the description of the
respective information sources, we will outline assumptions and
limitations of this method.
3.1. Wearable Sensor
The wrist-worn device used in this study has been designed to
record inertial data for long-term experiments. It uses an Analog
Devices ADXL345 accelerometer sensor has a battery lifetime of
up to 2 weeks for continuous (day and night) 100 Hz logging, and
stores the collected data to a micro-SD memory card. Optionally,
the device can be equipped with an additional OLED screen (see
Figure 1) and a Bluetooth module. The sensor unit was config-
ured for our experiments to record at a sensitivity of ±4g and a
frequency of 100 Hz (i.e., a 3D acceleration vector every 10 ms).
Once the data are uploaded after the study period (via USB) and
converted to acceleration values in g, the raw values can be plotted
over time to obtain an impression on when participants have been
moving or not. An example of such data is depicted in Figures 1
and 2 for a time period of 24 h. While sleeping, for instance (here
between 03:00 and 12:00), the inertial data exhibit significantly
less movement, with the data changing only whenever the user
is transitioning between sleep poses. On the other hand, it is
important to note that with these settings, inertial data when the
unit is worn on the wrist, even when sleeping or resting, exhibits
significantly more variation, compared to when the unit has been
taken off.
FIGURE 1 | Our study uses a continuously worn and watch-like unit
(left, top plot) and an Android App running on the user’s smartphone
(right, bottom plot), that both record 3D inertial data. Our method
compares these two sources to estimate when the phone was carried on the
user, over the course of several weeks.
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Van Laerhoven et al. Wear is your mobile?
FIGURE 2 | Raw 3D acceleration over a day from a wrist-worn sensor and the phone of a participant (male, age 27). The phone was put on the mattress
while sleeping: the bottom plot exhibits peaks in the phone data (6:00–11:45) that correlate with some sleep pose transitions in the wrist-worn’s data (top plot).
3.2. Android Sensor Recording App
The Android app has been developed based on the Android
framework to compare the inertial data from the wrist to those
from a phone, therefore recording similar data with a mobile
phone, using its built-in sensors. The app is compatible with
phones running Android 2.3.3 or higher (covering a majority of
Android phones, and smartphones in general) and can record
from all sensors available within the Android sensor framework.
The sensor data are directly put into an SQLite database as
provided by the Android framework.
The first challenge when developing an Android app is that
it has to support various Android versions and should operate
robustly. A first obstacle in the Android framework for this follows
from its policy for management of resources, having different pri-
orities for processes and their threads that will influence schedul-
ing. Additionally, Android distinguishes between processes in the
foreground and processes in the background, which is impor-
tant to consider for our app: in resource critical situations, the
processes will be handled differently by the framework, such as
being shut-down automatically. For this reason, we implemented
our recording software as a background service, so that it does
not impact the recording software according to the user’s phone
behavior by using a partial wake lock, which requires only the CPU
to stay awake.
When recording data in the background it is possible that
periods of time exist when the app is not recording any data.
This was especially the case with older phone models with lim-
ited processing resources (like single-core processors) and while
users were talking on the phone. Therefore, we implemented our
Android app to automatically restart itself after an unexpected
shut-down. This impacted only slightly the selection of partici-
pants in our study, as most participants owned newer Android
phone models.
The Android sensor framework uses the International System
of Units (SI,
m
s
2
) instead of g for the inertial data, which is stored
directly in the database to ensure that no accuracy is lost. Pre-
liminary tests showed that a reliable sampling rate, like the one
of the wrist-worn sensor, is difficult to obtain in the Android
framework (100 Hz being unobtainable). Some exemplary data
recorded with the Android app are shown in Figure 1 bottom,
along with the previously discussed wrist data. The comparison
of both plots in Figure 1 already allows to make a coarse-grained
inspection about when the phone might have been with the user or
not. Immediately noticeable is that the phone data exhibit far more
“flat,” motionless segments than the wrist data. This particular
user was carrying the phone in the front pocket [as approximately
57% of males tend to do (Ichikawa et al., 2005; Steinhoff and
Schiele, 2010)] during the day and on the mattress while sleeping,
but this is certainly not representative for most phone users.
3.3. Data Comparison
For this study, we rely on motion from the smartphone and the
participant’s wrist. However, the data recorded by both platforms
cannot be compared directly for the following reasons: (1) both
devices are carried in different positions and will therefore experi-
ence different motion patterns and force of acceleration. (2) With
such, the axes of the sensor coordinate systems will unlikely be
aligned to each other most of the time. (3) Data are recorded by
both systems independently, since intervals between smartphone
sensor readings (readings were time-stamped on the phone as they
were obtained) tend to vary substantially, with the most robust
rates obtained for 10 Hz, while the wrist-worn sensor records
accurate equidistant 3D acceleration samples.
In order to compare the two datasets from both wearable and
phone over longer stretches of time, we calculate the variance
of the magnitude of the 3D acceleration vector over a 1-min
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Van Laerhoven et al. Wear is your mobile?
time interval. Using the magnitude of the 3D acceleration, we
have to consider only one rotation-invariant scalar value. The
variance has the advantage that it does not require calibration
with respect to gravity, as the mean would have to. To estimate
whether the wearable and mobile devices moved in conjunc-
tion, we defined different thresholds on the variances to detect
movement segments.
4. Study Methodology and Setup
In order to illustrate our method, a study was held with 51 volun-
teers, in which these were asked to install our logging App on their
Android phone and wear the wrist-worn sensor during a period
of 2 weeks. This section will describe further details of the setup
for our experiment and will give an overview of the collected data.
4.1. Participants Recruitment
The 51 participants were recruited through a local poster adver-
tisement campaign. As this was done in a university town, about
half of them were in some way affiliated with the university (being
staff or students), though of various ages. The authors declare that
the user study of this article was conducted with knowledge, guid-
ance, and approval of the university’s Ethics board and confirm
that all experiments conform to the relevant regulatory standards.
The number of 51 persons was initially much larger but only 1
out of 5 persons that responded to the advertisement participated
in the study and delivered a full dataset. Reasons mentioned for
not being part of the experiment were either because people did
not respond after a first contact or they decided not to participate
after a detailed explanation of it. Although the data in our method
were stored locally on the device, especially students with an
engineering background cited privacy concerns as a main reason
for not participating. Interestingly, comfort concerns for wearing
the wrist sensor 24/7 were rarely mentioned as a reason not to take
part: during the study, five participants had to take off the sensor
for a few nights due to being uncomfortable sleeping with it.
The participants’ ages range from 14 to 62 years. In total, 10
female and 41 male participants participated in the study. All
participants were asked to partake in this study with their personal
Android phone. The study was advertised with the purpose of
obtaining inertial data to detect daily activities afterwards, the
participants were not told that we investigated the user’s phone
carrying habits to avoid bias. We met with the participants three
times during the study: an initial meeting explained the purpose
of this study, showing the participants our privacy policy ensuring
anonymization of the data after the trial would be completed.
Additionally, the wrist-sensor functionality was explained and the
sensor handed out to the participants. In addition to wearing
the sensor, we asked the participants to keep a journal of their
sleeping times. A second meeting was held after 1 week to ensure
that data had been properly recorded, followed a week later by a
third meeting for returning the sensor, downloading all data from
the smartphone. The participants’ data were evaluated directly to
show and explain the real purpose of the study. In addition, we
conducted a post-study interview concerning wearing comfort of
the sensor and their perception of how often participants estimate
to carry their phone on the body, as well as the perceived power
consumption of the Android app.
Table 1 summarizes the demographic information on all the
51 participants that took part throughout the study, additionally
showing the amount of data obtained from the wrist-worn sen-
sor and the smartphone, as well as how often the user charged
the smartphone during the study, plus the total number of days
recorded. As expected, we gathered almost a continuous recording
TABLE 1 | Study participant details: information coverage from wrist-worn
sensor and smartphone, number of phone charges during the study (the
wearable sensor did not need charging), and days recorded.
User Age Gender % Wrist % Phone No of
charges
No of
days
1 32 Male 97.62 92.76 11 21
2 29 Female 54.13 43.04 5 11
3 26 Male 98.64 98.89 10 6
4 32 Male 87.50 90.63 5 9
5 36 Female 85.04 99.62 10 11
6 28 Male 72.86 100.00 11 17
7 27 Female 99.14 65.23 4 7
8 33 Male 89.62 96.19 18 11
9 31 Male 88.37 99.70 19 14
10 27 Male 99.42 99.23 10 11
11 27 Female 99.33 99.66 6 6
12 27 Male 80.84 34.36 3 9
13 20 Female 98.95 98.95 17 10
14 33 Male 87.85 98.21 11 10
15 25 Male 97.00 95.71 9 10
16 23 Male 98.42 92.53 6 9
17 26 Male 54.01 93.88 13 10
18 27 Female 93.57 95.48 8 9
19 27 Male 82.19 73.80 8 14
20 25 Male 93.19 99.28 11 11
21 26 Male 98.92 98.39 5 4
22 24 Male 99.63 98.88 9 11
23 30 Male 70.85 98.52 13 11
24 31 Male 98.93 98.72 11 10
25 28 Male 89.09 90.88 14 15
26 38 Male 30.70 99.53 9 9
27
20 Female 80.38 100
.
00
13
11
28 58 Male 43.69 98.63 12 12
29 26 Male 99.11 99.70 16 14
30 38 Male 99.57 99.45 31 34
31 33 Male 96.94 97.28 27 37
32 25 Male 89.97 97.16 28 14
33 25 Male 57.78 97.01 14 14
34 21 Male 87.36 98.10 25 15
35 25 Male 76.32 99.86 16 15
36 25 Male 90.15 95.82 26 14
37 26 Male 96.63 93.25 5 7
38 27 Male 77.78 52.36 ? 15
39 33 Male 99.05 100.00 6 5
40 33 Male 87.46 93.05 12 14
41 31 Male 98.80 96.54 17 14
42 23 Male 74.67 100.00 9 14
43 25 Male 93.91 99.84 15 13
44 62 Male 92.56 94.35 6 7
45 55 Female 94.92 99.58 10 10
46 28 Female 95.73 00.00 ? 14
47 35 Male 96.87 92.24 13 14
48 35 Male 94.51 96.59 12 14
49 14 Female 97.87 100.00 9 7
50 50 Male 85.76 99.70 15 14
51 33 Male 99.04 99.84 6 13
On average, we obtained 12.5 days and 12 charges per participant.
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Van Laerhoven et al. Wear is your mobile?
of smartphone data for all the participants. A few outliers (users
2, 7, 12, 19, 38, and 46) are visible, because either the app stopped
recording due to the power saving mode of the phone (which is
always switched on when battery power is low enough) or the
phone running out of battery power. In these cases, participants
switched off the app by themselves, unfortunately sometimes also
forgetting to switch it back on again. Participant 46 represents
an exceptional case: during the study, she was cleaning up her
phone storage and by accident deinstalled our Android app, which
resulted in deletion of the database entries. Nevertheless, we could
obtain the wrist-sensor data, as shown in the table. Due to a hard-
ware problem with his smartphone, participant 38 had problems
using his smartphone which is why it was regularly switched off
during the study (almost 50% of the time). This probably also
explains why for this individual the charging status could not be
logged by the app.
Recording data with the wrist-worn sensor suffered set-backs
for other reasons: obtaining almost 100% of the data over the
recording time is almost impossible, since whenever the sensor
is taken off long enough, mostly for showering or swimming,
the recording is interrupted and data for these times is missing.
For 27 participants, we nevertheless obtained almost 100% of
recording over the study period. Five participants (users 2, 17,
26, 28, and 33) found it uncomfortable to wear the wrist sensor
during most nights, which is why we obtained such a significantly
smaller portion of inertial data from their wrist-worn sensors.
Additionally, most of the participants tended to take off the sensor
on weekends for leisure activities or family celebrations (wearing
the sensor with a shirt seemed too uncomfortable). Participant
26 was also on holidays while wearing the sensor, which resulted
in data from the wrist-sensor of 30.7% for the whole recording
period of 9 days, showing again that wearing devices on the body
is hard to accomplish when traveling for private reasons. Two of
the study participants were willing to wear the wrist-sensor and
log phone data for over 5 weeks, resulting in a recording time of
34 and 37 days for users 30 and 31, respectively. The coverage
of obtained data from both modalities for these two participants
was also remarkably high: for participant 30, we gathered over
99% of wrist and smartphone data, and for participant 31, around
97% from both recording platforms. On average, we obtained
87.31% of wrist data and 91.22% of smartphone data from all
participants. Figure 3 shows examples of the raw acceleration data
from two participants (left: female, age 20 and right: male, age 33).
The top plots depict the wrist-sensor data and the bottom plots
represent the smartphone’s inertial data. We observe here two
different phone carrying behaviors: the average female participant
carries her phone mostly during the day from approximately 7–19
O’clock while the male participants carried it mostly from the
morning until the early afternoon and in the evening. Note here
that the smartphone was used shortly before going to bed and
again immediately after waking up by both participants: many
participants used their phones regularly as an alarm clock.
4.2. Variety in Mobile Phones
The fact that participants were using their own phones throughout
the study led to a high variety of different models, for which not
all of the built-in accelerometer modules were previously known.
Additionally, it was not guaranteed that the Android app would
be running properly on all devices, since manufacturers tend to
modify the Android OS according to their needs, leading to pos-
sible problems for recording continuously without interruption.
Fortunately, we obtained most of the data with the Android app,
as indicated in Table 1 by the amount of data recorded by the
smartphone. The majority of the phones were from Samsung (23),
followed by HTC (11), Sony (8), LG (6), Motorola (2), and Huawei
(1). The Android OS installed on the smartphones varied from
10 to 19, with platform version 10 corresponding to Android OS
2.3.x (9 participants), platform versions 14 and above to Android
4.x.x (OS 15 = 5, OS 16 = 9, OS 17 = 8, OS 18 = 7, and OS 19 = 13
participants). Note here that Android OS versions 11–13 are only
installed on Android Tablet models and therefore are not present
in this study. None of the above mentioned Android phones or OS
versions caused problems in the app, which is why recording data
over at least 2 weeks was feasible.
FIGURE 3 | Raw acceleration data from a female (age 20, left plot) and
male (age 33, right plot) participant for both wrist-sensor data (top) and
smartphone data (bottom). Both plots show the diversity in behaviors: the
female participant carries her phone intermittently throughout the working day
but less in the morning and evening; the male participant carries his phone
mostly throughout the morning and in the evening.
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Van Laerhoven et al. Wear is your mobile?
5. Evaluation of Study Results
To estimate whether the phone is carried by the user, our method
compares each of the detected motion segments from the wrist
sensor to the motion segments present in the phone data. For
this purpose, we first needed to obtain a proper threshold for
both datasets to detect these motion segments. We first discuss
the chosen parameters and their effect, and then present the
correlations between the wearable platform and the mobile phone
as obtained in the inertial data.
5.1. Threshold Selection
Key to our method is the choice of a proper variance threshold
for the detection of motion in the 1-min windows. We believe
that there should be one best threshold that detects accurately
all motion segments over all devices and OS Android versions.
Therefore, we aimed to set a threshold that applies for all models,
with which we primarily filter out noise and artifacts due to
the unstable logging frequency which the Android framework
delivers
2
.
Essentially, we made the following assumptions to determine
the thresholds for the entire study: (1) A phone never moves
without its user. We assume this to be true most of the time
when using large and long-term datasets. Nevertheless, a user
might lend his phone to someone else or leave the phone where
it experiences motion (e.g., a stationary phone that vibrates due
to a received message or email might generate phone motion
without the user moving). This might, however, lead to a bias in
small studies, though we have not found such occurrences in our
dataset. (2) The phone does not move while charging or during
the user’s sleep. Although users could sleep in means of transport
(e.g., bus or plane) and could be charging their phone in transit
(e.g., in the car or in a train), none of our study participants was
2
Android only takes a desired delay between sensor readings as a configuration
parameter.
found to have done so. We found by experiment that a threshold
of 0.000012 g delivered the best results among all participating
devices: higher thresholds do not increase the precision but lead to
a further drop in recall, meaning that motion of the phone might
have been missed. Additionally, lower thresholds tend to detect
phone motion where there is assumed to be no motion (e.g., when
sleeping).
5.2. Mobile Phone vs. Wearable Data
We applied the threshold on the variances of the inertial data using
a window size of 1 min. A first glimpse at estimated movements
on both the wrist and the phone data is given in Figure 4. It
shows for two male participants in each plot from top to bottom:
the battery charging status, the sleep segments as annotated by
the participant, the raw inertial data from the wrist sensor, the
detected movement segments from this sensor, and the detected
movement intervals of the smartphone data for 1 day. In the left
plot, several motion segments are immediately visible during the
night, although one might expect that the phone should not be
moving. During the study, this participant put the phone on the
mattress while sleeping, which is why we observe motion during
sleep in the smartphone data. Additionally, we see for both partic-
ipants that the wrist-sensor recorded far more motion segments
during the day in contrast to while sleeping, which makes sense
since we move mostly during the night when we change postures.
Note here that two different charging behaviors can be observed:
on the left, the participant was charging his phone during the
night, while on the right the participant charged only during the
day while at home. In total, over 46% of the charging events from
all 50 participants happened during the night. Thirty-one percent
of the charging occurred during the day between 9:00 and 18:00,
mostly while people are at work or in the university. The other 23%
happen in the morning or when people are at home in the evening.
On average, participants carried their phone with them for
22.19% of the time throughout the whole study (note: this figure
holds for day-and-night monitoring, as opposed to previous
FIGURE 4 | Results for two male subjects for 1 day showing from top
to bottom: battery charge status, sleep as annotated by the
participant, raw acceleration data from the wrist-worn sensor,
motion as detected by the wrist-worn sensor, and the thresholded
detection of movement according to the smartphone’s acceleration
data. The left plot shows a participant who put his phone on the mattress
while sleeping as depicted by the black bars that represent the movement
per minute.
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Van Laerhoven et al. Wear is your mobile?
studies investigating phone usage during the day). Per partici-
pant the results vary a lot, however, from 3.87% up to 52.91%,
highlighting also that the results depend on the individual user’s
habits. The dataset includes participants who excessively used
their phone, most noticeably for playing Ingress
3
(3 users: 13, 21,
and 22), an augmented-reality game, which correlates with high
smartphone usage. For these users, the carrying phone results are
substantially higher than the average (up to 52%). Some partici-
pants forgot to charge their phone on a few occasions during the
study, which led to a shut-down of the recording application and
therefore to data loss. Five participants found it uncomfortable to
wear the wrist-worn sensor during the night, which is why they
took off the sensor on most nights.
Smartphones are on the user at different times of the day. The
results listed in Table 2 depict when the users had the smartphone
on them at defined times of the day [using the divisions as in
Dey et al. (2011)]: in the morning (7:00–9:00), during the day
(9:00–18:00), in the evening (18:00–23:00), and during the night
(23:00–7:00). Additionally, we calculated the amount of wearing
time during the whole day (from 9:00 to 23:00, last column). As
expected, users carried their phone mostly during the day. In
contrast to the overall results of 22%, participants, on average,
carry their phone for 35.59% of the time from 9:00 to 23:00.
Depending on the participants time schedule (especially between
students and office employees), the individual results vary highly.
Participant 1, for example, uses his smartphone almost only while
being at the office (9:00–18:00), while user 49 seems to be carrying
his phone almost all day long. These findings suggest that not
only the type of user monitoring application but also the habits
on phone usage of the targeted user need to be known in advance.
Note here that user 46 was excluded from the evaluation step,
since the phone data were deleted by accident. In the following
paragraphs, we will discuss the benefits and drawbacks of this
study and especially the results.
5.2.1. Demographic Specificity
In contrast to the work of Dey et al. (2011) and Patel et al.
(2006), which evaluated the proximity to the phone in the North-
American region, we investigated the carrying behavior in a Euro-
pean country. We believe that cultural differences exist, which is
being reflected in the behavior of a countries’ population as well.
To the best of our knowledge, such an experiment as presented in
this section has not been conducted yet outside North-America.
5.2.2. Acceptance of the Wrist-Worn Unit
The wrist sensor used for this study was perceived as comfortable
to use by most of the participants and most reported that they
quickly forgot about it while wearing it. The fact that the device’s
battery charge lasts more than 14 days, meant that participants did
not have to charge or manage the device themselves. Although for
five participants the device was not always comfortable enough to
wear during their sleep, these users did remember to wear their
unit again after waking up and the data were reliably recorded
for the day-time periods. Nevertheless, the fact that 10% of par-
ticipants had chosen not to wear the wrist-worn unit during
night-time, is a limitation of our method.
3
https://www.ingress.com/, [last access, 11/2014]
TABLE 2 | Results for all users showing in percent if they were wearing
their smartphone at specific segments of the day with maximum values
highlighted.
User 7–9 9–18 18–23 23–7 9–23
1 3.82 25.43 11.26 1.18 20.98
2 6.67 11.30 3.78 0.83 9.57
3 3.02 28.00 30.62 4.60 29.43
4 34.86 30.92 55.22 7.64 44.68
5 5.31 24.98 18.42 1.04 23.45
6 5.19 36.48 28.80 7.40 34.54
7 22.38 15.95 23.19 0.30 21.79
8 18.71 27.91 25.70 5.83 29.87
9 10.77 50.23 40.38 6.07 48.35
10 9.77 30.88 31.39 6.50 32.52
11 25.42 16.42 13.94 2.50 19.25
12 15.83 19.26 12.57 7.71 19.15
13 24.58 37.33 21.23 6.64 35.23
14 24.33 29.94 7.87 8.15 25.61
15 0.28 18.64 23.15 4.00 20.29
16 31.20 32.82 33.30 9.93 37.55
17 5.50 48.22 66.20 19.00 55.55
18 10.65 19.11 16.56 2.29 19.75
19 14.38 46.10 45.00 7.54 47.84
20 5.14 35.08 37.86 3.23 36.85
21 35.42 36.11 38.00 9.74 41.90
22 30.83 70.89 68.61 27.88 74.58
23 7.00 51.63 52.00 7.15 52.86
24 15.95 55.11 33.05 13.21 49.62
25 13.97 19.97 15.69 0.71 20.51
26 39.06 25.90 4.17 16.74 23.75
27 3.67 35.96 32.03 6.25 35.13
28 24.92 36.18 15.57 0.19 32.44
29 11.25 28.89 35.36 12.96 32.84
30 25.15 40.42 31.16 1.11 40.83
31 18.03 41.14 39.58 4.16 43.28
32 5.12 33.93 16.55 7.69 28.50
33 6.73 28.94 56.08 21.64 39.68
34 16.89 43.25 24.00 7.19 38.86
35 13.00 35.79 37.40 9.30 38.30
36 17.08 52.22 53.93 16.12 55.40
37 22.78 28.06 26.50 1.95 30.89
38 3.43 27.28 14.37 5.42 23.17
39 20.00 30.00 52.08 17.66 40.89
40 21.79 28.52 40.05 7.01 35.89
41 13.99 38.85 33.07 8.12 38.88
42 5.75 22.46 36.47 21.85 28.37
43 52.14 27.42 55.55 6.44 45.02
44 6.46 15.56 28.58 0.57 21.19
45 4.25 15.83 16.04 4.06 16.52
47 30.89 50.71 43.55 8.93 52.67
48 33.27 47.36 11.44 3.35 39.42
49 40.00 73.07 58.14 7.65 73.62
50 48.01 52.82 36.13 23.25 53.82
51 6.67 17.26 17.13 5.98 18.22
Average 17.43 33.93 31.37 7.93 35.59
On average, participants carry their phone during the day (9–23) for 35.59% of the time.
5.2.3. Platform Differences
We encountered a difficulty in the evaluation process that has to
be considered in future studies that make use of the accelerometer
on the smartphone: due to the vast number of different mobile
phones that participated in this study, the algorithm for detecting
motion segments had to be insusceptible to noise, jitter, or other
sources of disturbance. Additionally, different OS versions led
to an unbalanced priority for our application to obtain sensor
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Van Laerhoven et al. Wear is your mobile?
data, since it is handled differently by the OS. Such scenarios
should not occur, especially if continuous data are needed to
detect, for example, activities with a smartphone. A benefit of
using the accelerometer though, is the fact that it does not require
any security permissions, which is why most of the applications
available in the Google Play store use it.
5.2.4. Power Consumption of the Android App
According to the participants, their phone-charging routine did
not change significantly, since most of the users chargetheir phone
overnight (almost 47%), as depicted in Table 1. The smartphone’s
battery lasts for typically a minimum of 24 h under normal usage
while having the app running. This was an early design constraint
for the application since an application that drains too much
power will quickly be deinstalled by the user. An exception was
the two elderly participants in our study: since both of them
are using their smartphones primarily to make phone calls, our
application forced them to charge the phone every day instead
of every three days. In total, two users contacted us during the
trial to discuss the higher power consumption. Power consump-
tion also depends highly on the CPU-type and the Android OS
version.
5.2.5. Proximity to the Phone
Participants have theirphone on them for 36% during theday time
on average, and 22% over the whole studyon average. This is over a
half of the time reported by Dey et al. (2011) and Patel et al. (2006)
that the phone is within arm’s length of the user (58 and 53%,
respectively). Interviews showed that indeed many participants
put their phone on the desk or at a table nearby, especially while
working and during the night. Additionally, some participants put
their phone on the mattress while sleeping, either because they
used it before falling asleep or to have it immediately on hand
when waking up. An exception was, for instance, a 14-year-old
participant that was supposed to switch off the smartphone while
attending class, but rather muted it during the whole day. During
that time, the phone was always in the front pocket and therefore
being carried on the body most of the time. We argue that the on
the user proximity information could give extra insight in future
studies, especially those that explore the use of smartphones as
wearable devices.
5.2.6. The User’s Perception
Especially when we interviewedthe participantsafter the study, we
asked them about their perceived smartphone behavior. Interest-
ingly, many participants underestimated their smartphone usage.
The most common answer was “I almost never use the phone
while being at work.” This was proved wrong for most of the
participants. Even though the smartphone is lying on the table
while the user is at his desk, whenever the user leaves his desk
the smartphone is put in the pocket. Many participants became
aware of that fact after the study. For context-based systems such
knowledge is crucial, since it shows that the smartphone is a
suitableplatform for sensing the environment. In their conclusion,
Dey et al. (2011) and Patel et al. (2006) stated that the mobile
phone is farther away from its user than expected. In contrast to
that, we conclude that the mobile phone is being carried on the
body more often than assumed by the users.
5.2.7. Long-Term Dataset and Future Uses
The recorded dataset consists of almost 638 days of sensor data
from two modalities that can be used in future studies: (1) the
smartphone data imply not only acceleration data and battery
status information but also light intensity values and the proximity
sensor values. (2) The wrist-worn sensor logged the inertial data
and, at the same time, the light intensity. Additionally, the dataset
contains a sleep diary from almost every participant. Such infor-
mation enables future studies that aim at detecting, for example,
sleep segments with a mobile phone only.
6. Conclusion
We presented an approach that estimates how often the user’s
smartphone is carried by the user, by relying on an additional
wrist-worn sensor that can be continuously worn for several weeks
and a smartphone App. By exploiting the fact that inertial sensors
are present in almost all smartphones, the data from both devices
can be matched to estimate carrying behavior. This approach can
be combined with the previously suggested methods by Dey et al.
(2011) and Patel et al. (2006), and allows for a characterization of
when the phone’s built-in sensors could be expected to monitor
the user, most notably to detect the user’s physical activities (such
as being sedentary, walking, and running).
We performed a 51 participants study using this method over
2 weeks per users, resulting in a dataset of over 638 days (or more
than 15,300 h) of recorded data from both modalities used. The
analysis of these data indicates that the users’ smartphones are on
the user on average 23% of the time (day and night). This figure is
considerably higher for some users (up to 52%) and considerably
less for others (4%). During day time (9:00–23:00), our results
show that users have their phone on them on average 36% of the
time. The study also suggests that users have very different habits
in phone-charging behaviorand usage, stressing the importance of
knowing the target users when designing activity monitoring apps
for smartphones that require the user to be carrying their phone.
We argue that the method of investigating phone use through
the comparison of inertial data from phone with a wrist-worn
sensor is particularly interesting for long-term activity studies.
Future work includes the deployment of the app and wrist-worn
sensors on a largerscaleand for a time-span of several months, and
additionally seeks to use the inertial data taken during the study
for activity recognition.
Both the anonymized dataset and the Android app are publicly
available at http://www.ess.tu-darmstadt.de to encourage others
to perform similar studies.
Acknowledgments
We thank all study participants for their commitment in wearing
the wrist-worn activity logger and using the Android app for at
least 2 weeks. Funding: This work was sponsored by the Emmy-
Noether project Long-Term Activity Recognition with Wearable
Sensors (LA 2758/1-1) from the German Research Foundation
(DFG).
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Van Laerhoven et al. Wear is your mobile?
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Conflict of Interest Statement: The authors declare that the research was con-
ducted in the absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Copyright © 2015 Van Laerhoven, Borazio and Burdinski. This is an open-access
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original author(s) or licensor are credited and that the original publication in this
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