PACE UNIVERSITY; CAPSTONE PROJECT - BIOMETRIC AUTHENTICATION & ACCELEROMETER SENSOR
1
Biometric User Authentication on Smartphone
Accelerometer Sensor Data
Noufal Kunnathu,
Seidenberg School of CSIS, Pace University

Abstract — This study attempts to build a statistical model to
identify a user, based on how the user picks up the phone and how
he/she holds the phone to the ear. Phone-pickup and phoneholding methods are quantified using accelerometer readings on
the three axes. This study is based on the theory that every person
has a unique way of handling the phone during a phone call. The
minute variations in the angle of tilt and the micro movements
made during the phone call can be quite different between two
individuals. The Accelerometer gives a convenient method of
quantifying the phone movements and the change in orientation of
the phone on a three dimensional space. This study involves data
gathered from seven participants who made five test phone calls
each lasting for a little over 5 seconds. The study produced a model
that can predict the user with high accuracy using a smartphone
accelerometer.
Index Terms— Accelerometer, Biometrics, Authentication,
Weka.
I. INTRODUCTION
A
is a device that can detect the static
acceleration caused by the pull of gravity and the dynamic
acceleration caused by displacement or vibration of the device
[7]. The static acceleration gives useful insights into the
orientation of the device at different phases of the experiment.
The dynamic acceleration quantifies customer movements
while picking up the phone and gives insights into the micro
movements during the call. Most smartphones available in the
market today, have three-axis accelerometer included.
Biometric authentication methods often provide secure
alternatives to traditional authentication methods. In fact
several biometric authentication methods can be used in tandem
to build a secure and an easy-to-use system that provide
continuous authentication capabilities.
This paper describes a method of accelerometer-based
authentication that can be combined along with other means of
authentication to improve the accuracy of biometric-based
authentication systems.
CCLEROMETER
A. SMARTPHONE ACCELEROMETER
Fig-1 shows the working principle of a smartphone
accelerometer. Seismic mass is fixed on the housing of the
accelerometer unit that is attached to the phone circuit. The
gravitational pull or user movements will change the orientation
This study is conducted as part of Capstone Program for the completion of
Masters Degree in Information Systems at Pace University during the Fall
Semester of 2014.
of the seismic mass with in the sensor, generating changes in
the capacitance [27].
Fig. 1. Smartphone Accelerometer – picture credit Techulator.com[27]
B. WEKA
Weka [12] is a product of University of Waikato. It is among
the leading open-source tools used for building Data Mining
Systems. It is released under GNU General Public License
(GPL).
Weka requires data in a specific format called AttributeRelation File Format (ARFF). It provides a convenient utility
for converting Comma-Separated Values (CSV) format to
ARFF format. Weka allows preprocessing of the data before
applying the classifier algorithms to build the model. Weka also
supports a number of attribute evaluator algorithms to be used
for selecting or ranking attributes in the dataset. The evaluator
can be used for filtering noisy attributes or ranking the attributes
based on their relevance.
Weka supports a large collection of classifier algorithms to
be used for building statistical models in different contexts. In
this study Weka classifiers are used for achieving two things;
for finding a threshold value for splitting the data sessions into
three phases – picking-up, on-ear and placing-down. More
importantly, Weka is used for building the statistical models for
the four experiments conducted in this study.
After evaluating many of the available classifier algorithms,
the highest performance is observed for a particular classifier
algorithm called Multilayer Perceptron. This is a classifier
PACE UNIVERSITY; CAPSTONE PROJECT - BIOMETRIC AUTHENTICATION & ACCELEROMETER SENSOR
based or Artificial Neural Network (ANN) model. This
algorithm is discussed further in the next section.
C. Multilayer Perceptron Algorithm
Multilayer Perceptron is a feed-forward artificial neural
network model. It maps a set of input data to a set onto a set of
appropriate outputs [8].
Fig. 2. Multilayer Perceptron Model.
As shown in Fig. 2, MLP model contains three layers with
each layer fully connected to the others – the input layer, the
hidden layer and the output layer.
II. LITERATURE REVIEW
Sensor-based, biometric analysis is a relatively recent field
of research. There is an increased interest in this area due to the
proliferation of mobile devices and the wide availability of
sensors. There were many early studies that used
accelerometer-based biometric analysis for gait recognition
[2][10][14][22]. The studies produced promising results in
identifying user gait based on accelerometer devices worn by
the users. There is an interesting study that proposed
authentication via electronic pets [4][26] that imprints the user
characteristics and continuously nurture the characteristics to be
used as an authoritative token for securely identifying a user. A
recent study focuses on predicting user traits such as height,
weight, sex etc. from the data collected with the accelerometer
sensor with the volunteers taking short walks carrying an
Android phone [29]. There have been interesting studies
proposing
gesture-based
user
authentication
using
accelerometer sensor [20][11].
There have been researches in mining complex activities and
performing activity recognition based on the data collected
from the Accelerometer [23][21][19]. There are studies for
building Accelerometer-based gestural text-entry systems [29].
Accelerometer and gyroscope is combined in many
experiments. The combined data can be used for motion gesture
recognition using classifiers with dimensionality constraints
[18]. There is a recent study investigating mobile device
picking-up motion [9]. Also, there is a study conducted to
transparently identify the user of a smartphone when he/she is
on a phone call [5]. These last two references are very relevant
to the scope of this study. Those studies and this paper look at
the same problem but in different perspectives.
2
III. EXPERIMENT DESCRIPTION
The aim of this study is to identify a user from the
accelerometer readings. For the purpose of this study, data was
collected from the accelerometer from volunteers who agreed
to participate in the study. There are four experiments
conducted in this study. All four experiments are based on
different aspects of the same dataset. The first two experiments
investigate the phone holding-to-ear pattern. The third
experiment investigates the phone picking up activity and the
last experiment combines all the features to derive a highperforming continuous authentication system.
A. Data Collection
The experiments required data corresponding to the phone
picking up action and the data for phone holding-to-ear activity.
Seven volunteers were selected for the data collection. The
participants included five males and one female with ages
ranging from 25 to 40, and a thirteen-year-old male. Each
participant is requested to repeat the following steps five times,
resulting in a total of 35 data samples.
 Pick up the phone from a table
 Hold it up to the ear for approximately 5 seconds
 Place the phone back on the table
The Accelerometer data is recorded continuously while the
above steps are executed. Users are asked to hold the phone
naturally as if making a phone call. It is observed that 5 of the
7 users held the phone to their left ear, one to the right ear, and
one to both ears - sometimes the left ear and sometimes the right
ear. The selection of the ear resulted in variation of the
accelerometer readings but it did not appear to be a significant
factor in identifying the user.
The accelerometer data gives a unique perspective on the
way phone moves on all three axes while the user makes the
phone call. Each experiment session lasted a little over 5
seconds. Sampling rate of 25Hz is used for the data collection.
A data vector contains following attributes.
 Time
 Session-id
 Ear-used
 X-acceleration
 Y-acceleration
 Z-acceleration and
 User-id
The Session-id, Ear-used and User-id are manually recorded
and added to the dataset. Other attributes are directly extracted
from the smartphone accelerometer sensor.
B. System Design
Android operating system was chosen for conducting the
study and a Nexus 5 device was used for all data collection.
An android application called “Sensor Kinetics Pro” [24] is
used for recording the accelerometer data. This app can record
readings from many sensors including accelerometer,
gyroscope, magnetometer, etc. However, the scope of this study
is limited to the accelerometer. So, the readings from other
sensors were not processed.
PACE UNIVERSITY; CAPSTONE PROJECT - BIOMETRIC AUTHENTICATION & ACCELEROMETER SENSOR
Fig. 1 is a screenshot from the app that shows the readings
for Accelerometer on X, Y and Z axes.
3
In this study, a semi-automatic procedure is used for tagging
the data into stages. The first part is to build a model to correctly
tag the three stages. For this purpose, a movement rate metric is
created based on the rate of movement on three axes.
The training data set is built manually for a few
representative sessions. The training data and the computed
value for metric were loaded into Weka. J48 decision tree is
used to build a simple model to derive a threshold value for the
movement metric for the stages. The classifier returned a
threshold value for the movement as 0.776. Any vectors that
have a lower than the threshold value is tagged as “on-ear”
stage. The rest of the data is split into either “picking up” or
“placing down” based on the time of recording.
After the automated tagging is applied, the data is manually
inspected to make sure that the three phases are continuous. For
example, any vector tagged as “picking up” in the middle of an
“on-ear” phase is manually overridden to “on-ear”.
Fig. 1. Screenshot from Sensor Kinetic Pro App.
Weka is used to analyze the sensor data. A number of other
tools were evaluated as part of the study. Weka was selected for
its intuitiveness, ease of use, and the rich feature set.
C. Data Pre-processing
As stated earlier, there are three phases for each experiment
session – picking phone up, holding up to the ear and placing
the phone down. Initial pre-processing task is to tag the data to
separate the data vectors for these three stages. This can be done
by plotting each session in Excel and visually inspecting the
graph. For example, see fig-2 for the line chart of a typical
session.
15
10
5
-5
time
0.313
0.63
0.948
1.265
1.582
1.9
2.216
2.533
2.851
3.316
3.862
4.406
4.953
5.498
5.993
6.538
7.083
7.628
0
-10
-15
X_value
Y_value
D. Experiments
Four experiments were conducted to build statistical models
for identifying users based on the Accelerometer data
associated with the phone call sessions.
1) EXPERIMENT-1: PHONE ON-EAR.
This experiment uses the average values for the X, Y and Z
readings from the accelerometer sensor. The aim is to build a
statistical model that can correctly predict the user from the
average values. The model will be built with the help of a
classifier algorithm available in Weka.
For this experiment, only the data vectors tagged as “on-ear”
is used. The average values are computed for X, Y and Z
readings for each session. The averages were computed by
building a pivot table in Microsoft Excel.
We are left with 35 feature vectors with following attributes.
 Session-id
 Ear-used
 Average-X-acceleration
 Average-Y- acceleration
 Average-Z-acceleration and
 User-id
In order to select the optimum attributes, an Attribute
evaluator called Chi-squared Attribute Evaluator [25] was used.
The evaluator ranked the attributes as shown in Table-1
Z_value
Fig. 2. Line Diagram for a typical experiment session.
Large variances can be observed at the beginning and end of
each session with relatively stable values for the middle part of
the experiment. The beginning part with large variances
corresponds to the “picking up” phase, the middle part
corresponds to the “on-ear” phase and the end part with
variances corresponds to the “placing down” phase. There can
also be 0 values very early and end of each session. These zero
values will be trimmed out as these correspond to the stationary
phone on the table.
Table 1. Attribute Ranking
It appears that averages on Z and X axes have higher
significance over other attributes in predicting the user id. The
attributes that have low significance can be eliminated one-byone as long as the removal doesn’t reduce the performance of
the system.
PACE UNIVERSITY; CAPSTONE PROJECT - BIOMETRIC AUTHENTICATION & ACCELEROMETER SENSOR
Figure-3 shows the clusters of instances when the values for
the two most significant attributes are plotted on X and Y-axes.
4
User-5 was confused as User-7. This can indicate some
similarity in the data for these two users.
Table 3. Confusion matrix for Experiment-1
Fig. 3. Data visualization – Average Z value on horizontal and X value on
vertical axes
Each point in the graph represents a feature subset containing
average values for Z and X accelerometer readings. Different
colors are used for the feature vectors corresponding to distinct
user. The rectangles show the clustering. As observed from the
diagram, there is minimal overlap between the rectangles. Only
one user has the data spread-out across the graph. This user is
the one who used both ears for the experiments.
As discussed earlier, in this study Weka is used with the
Multilayer perceptron algorithm as the primary classifier for
building the model. The entire dataset was used as both training
set and the test set with a 35-fold cross-validation. Since there
were 35 sessions altogether, the 35-fold cross-validation will
test each session individually with the remaining 34 sessions
used for training.
Table-2 shows the results from the classifier. Out of the 35
sessions, 28 were correctly classified with an 80% success rate.
Each row corresponds to a user. All the sessions from a user
indicated by the row label is placed on that row based on how
the session was classified with the column label indicating the
classification.
The classifier was executed multiple times to reduce the
feature set to the lowest number of attributes that produce the
best results. Eliminating Session-Id and subsequently the EarUsed attributes, improved the system performance. Removing
any other attributes deteriorated the performance. The
remaining attributes constitute the feature set for this
Experiment. Following are the attributes that are identified as
part of the feature set for this Experiment.
Table 4. Feature-set for Experiment-1 with the chi-squared ranks
2) EXPERIMENT-2: PHONE ON-EAR (VARIANCES
ADDED)
This experiment is very similar to Experiment-1. Addition of
variances into the list of attributes is the only change from
Experiment-1.
Similar to the earlier experiment, attributes are evaluated
using Chi-squared Attribute Evaluator. Table-5 shows the
attributes with chi-squared ranks. From the rankings, it appears
that the variances are not as significant as the averages in
identifying the user. The Z and X averages are still the most
significant attributes.
Table 2. Classifier Results Summary – Experiment-1
Kappa statistic [28] with a value 0.7667 shows a moderate
agreement with in the experiment data. Root-mean-squarederror, root-absolute-error and root-relative-squared-error
provide metrics for comparing the rate of error in making the
correct prediction. These metrics can be used for comparison
between the experiments described in this paper in terms of
probability of errors with in the models.
Table-3 shows the confusion matrix that helps to visualize
how the measurement from each user was classified. The
diagonal shows the matches and the numbers marked outside of
the diagonal are the “confusion”. For example, two sessions
from User-7 was confused as User-5 and one session from
Table 5. Attribute Ranking
The experiment is performed multiple times by eliminating
lowest significant attribute each time. It was observed that the
following set of attributes provided the best performance.
PACE UNIVERSITY; CAPSTONE PROJECT - BIOMETRIC AUTHENTICATION & ACCELEROMETER SENSOR
5
Where
- gravity on X, Y or Z-axis for ith measurement.
- threshold which can be found by the following
formula. For this experiment a constant threshold of 0.8 is
assumed.
Table 6. Feature-set for Experiment-2 with the chi-squared ranks
In terms of number of instances classified correctly, both
Experiment-1 and Experiment-2 performed equally, by
correctly identifying 28 of the 35 instances. However,
Experiment-2 had lower error values compared to Experiment1 signifying an improvement in the model performance. With
the 35-fold cross-validation, the model produced 80% success
rate. Table-6 shows the result summary from Weka.
is the time-constant.
is the sampling rate.
- ith reading from the accelerometer.
As done earlier, the attributes are initially evaluated using
chi-squared attribute evaluator resulting in following rankings.
Table 7. Classifier Results Summary for Experiment-2
Table 9. Attribute Ranking
As observed on Table-9, the gravity acceleration on Z-axis
and session id have the lowest ranks in identifying the user
instance.
As discussed earlier, the feature set need to be reduced by
eliminating low ranking attributes with out reducing the system
performance. By following this reduction, following attributes
are determined as the feature set for this Experiment.
Table 8. Confusion matrix for Experiment-2
Confusion matrix has a slight difference from Experiment-1.
An instance belonging to User-5 was earlier classified as User1; now it is classified as User-7. Either way, both models got
that particular instance wrongly classified.
3) EXPERIMENT 3: PHONE PICKING-UP
Phone picking-up phase is relatively more dynamic. There
are high variances in accelerometer readings during this phase.
There are two components that contribute to the acceleration –
gravity and the user movement.
The first task in this experiment is to separate the two
components. Even though it is impossible to completely
separate the two components, a reasonable approximation can
be made using a Low-pass Filter [17].
Following formula is used for finding the gravity component
with the low-pass filter [1].
Table 10. Feature-set for Experiment-2 with the chi-squared ranks
Only "Session Id” was removed for this experiment. All other
attributes including Z_gvty contribute to the system
performance.
Table-10 shows the summary of results for experiment-3.
PACE UNIVERSITY; CAPSTONE PROJECT - BIOMETRIC AUTHENTICATION & ACCELEROMETER SENSOR
6
Table 11. Classifier Results Summary
The experiment identified 29 of the 35 users correctly, with a
success rate of 82.86%. It can be deduced that phone pickingup method is slightly better in identifying the user compared to
the phone holding position.
Table-12 shows the details of how each session was
classified.
Table 13. Attribute Ranking
Attribute reduction logic is performed again and found that
the following subset provided the best performance.
Table 12. Confusion matrix for Experiment-3
It can be noted from the confusion matrix that this experiment
identified some users (for example, User-7) more reliably
compared to the previous experiments and vice versa.
4) EXPERIMENT 4: COMBINATION OF PHONE ON-EAR
AND PICKING-UP PHASES
In this experiment, the feature sets extracted from
experiment-2 and experiment-3 are combined. Following
shows a quick visualization of all the available attributes. Each
color indicates a distinct user. For example, the Ear-Used
attribute is same for all users except for User-3 and for a portion
of samples for User-4.
Table 14. Feature-set for Experiment-4 with the chi-squared ranks
Table-14 shows a summary of results from the classifier.
Table 15. Classifier Results Summary
Fig. 3. Data visualization of how individual attributes stack-up for each
user represented by a distinct color.
The combined feature set is evaluated using the chi-squared
attribute evaluator. The results are provided in Table-13
The combined experiment has the highest performance
compared all previous experiments. This experiment identified
32 of 35 samples correctly with a success rate of 91.43%.
Table-16 is shows the classification. The darker diagonal
indicates the improvement in performance. 4 of the 7 users are
PACE UNIVERSITY; CAPSTONE PROJECT - BIOMETRIC AUTHENTICATION & ACCELEROMETER SENSOR
identified with 100% accuracy. The rest of the users were
identified with 80% accuracy.
Table 16. Confusion matrix for Experiment-4
[4]
P. Briggs and P. L. Olivier. Biometric daemons: authentication via
electronic pets. In CHI '08 Extended Abstracts on Human Factors in
Computing Systems (CHI EA '08). ACM. 2008
[5]
M Conti, I. Zachia-Zlatea, and B. Crispo. “Mind how you answer me!:
transparently authenticating the user of a smartphone when answering or
placing a call,” In Proceedings of the 6th ACM Symposium on
Information, Computer and Communications Security (ASIACCS '11).
ACM, 2011.
[6]
J. Demsar et al. “Orange: Data Mining Toolbox in Python,” University
of Ljubljana, 2013.
[7]
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[8]
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[9]
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IV. CONCLUSIONS AND FUTURE WORK
The result of this study is promising. The final model built
by combining feature sets has a high performance. It shows
91.4% success rate in correctly predicting the user. It is exciting
to see that this soft biometric analysis can be used in identifying
a user with such accuracy.
This experiment could be improved in a number of ways. The
sample size selected is relatively small. The experiment could
be repeated with a larger sample size to confirm the findings.
The data collection method can be improved. Instead of
conducting all 5 sessions in a single stretch, the data collection
could be done in phases with different sessions from each user
collected on different days. The direction were the user was
facing was neither random nor supervised. Hence some of the
users were choosing a random direction and recording all
samples facing that same direction. There were four users who
turned towards approximately the same direction. Also, the
participants were standing still during the phone call. It would
be more realistic to have the users walk or make other
movements while making the call. Making such changes can
potentially bring down the performance of the model; but such
changes will result in a robust model that can be used in realworld applications.
This study focused purely on Accelerometer sensor.
Combining this sensor data with other available sensors like
Gyroscope, Magnetometer, etc. can improve the performance
of the model further. This will be another area to explore in the
future.
This model can be used in combination with other biometric
systems like voice recognition, face recognitions, etc. Such a
system that combines multiple biometric techniques for
continuous authentication could become a foundation for high
security systems in future.
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