=Paper=
{{Paper
|id=Vol-3688/paper14
|storemode=property
|title=Developing an Application with Sensors in Smart Phones
|pdfUrl=https://ceur-ws.org/Vol-3688/paper14.pdf
|volume=Vol-3688
|authors=Atahan Tufekci,Muhammet C. Colak,Anar Gurbanov,Pinar Kirci
|dblpUrl=https://dblp.org/rec/conf/colins/TufekciCGK24
}}
==Developing an Application with Sensors in Smart Phones==
https://ceur-ws.org/Vol-3688/paper14.pdf
Developing an Application with Sensors in Smart Phones
Atahan Tufekci, Muhammet C. Colak, Anar Gurbanov and Pinar Kirci
Bursa UludagUniversity, Gorukle, Bursa, Turkey
Abstract
In the study, the functions of sensors in smartphone hardware, how data can be collected from sensors during
the coding phase, and healthy driving, fainting detection applications will be developed and the results will
be discussed.
Keywords
sensors, smartphone, driving 1
1. Introduction
With the advances and developments in smartphones, various sensors such as accelerometers,
gyroscopes, magnetometers and similar sensors have been included in the hardware of these devices.
Thanks to these sensors, it is possible to obtain various data about the person using the device and its
environment. These data can be accessed with the libraries used in the mobile software development
phase. Applications were developed by processing these data accessed with mathematical methods
or machine learning algorithms. With these applications, it has been possible to obtain results in many
areas about people using smartphones.
In the age of technology we live in, smartphones are perhaps the devices that people use, spend time
with and keep with them the most. At first glance, smartphones are thought to be used only for
communication and communication, but with the advancement of technology, new methods and new
directions have been discovered in which these devices can be used. Over time, with the development
of the hardware of smartphones, sensors such as accelerometers and gyroscopes (1) have been
included in the hardware of these devices and new usage opportunities have emerged for
smartphones. With the help of these sensors, it is possible to obtain various information about the
person using the phone and the changes in their environment. By analyzing the data collected from
the activities and movements of the person during the day with mathematical calculations or machine
learning algorithms, results have emerged in the field of health, where people can detect and track
their own fainting, driving, stepping, running, stopping, sitting (2). It has been possible to develop
applications related to this subject.
2. Sensors used in the project
2.1. Accelerometer Sensor
The accelerometer sensor in the smartphone hardware is used to measure the acceleration applied
to the device. The data obtained from this sensor gives the acceleration value affecting the
smartphone in the x, y, z axes in plus and minus directions. These values are in g (1). Based on these
data, the movements made by the person using the smartphone and the shaking of the device analyzes
can be made regarding their states and vibrations (3). Based on these analyzes, certain inferences can
be made. In the coding phase, the variables and functions of the Accelerometer sensor type and
SensorEventListener interface from the SensorManager library (1) can be used in the Android system
to access the data collected by this sensor. In this way, accelerometer data can be obtained with
COLINS-2024: 8th International Conference on Computational Linguistics and Intelligent Systems, April 12–13, 2024, Lviv,
Ukraine
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0000-0002-0442-0235 (P. Kirci);
ISSN 1613-0073
Proceedings
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Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
smartphones that people carry with them all day long without the need to use an extra environment,
device or sensor (4).
2.2.Gyroscope Sensor
The gyroscope sensor in the smartphone hardware is used to measure the orientation angle of the
device in the x, y, z axes in the plus and minus directions (5). With the data obtained from this sensor,
the orientation state, rotation angle and angular velocity of the smartphone can be determined (3).
By using the data collected from the gyroscope sensor, certain inferences can be made from the
analysis of the physical activities of the person using the smartphone in daily life, as used in the
accelerometer sensor. In order to access the data collected by this sensor during the coding phase, the
variables and functions of the Gyroscope sensor type and SensorEventListener interface from the
SensorManager library (1) can be used in the Android system. In this way, gyroscope data can be
obtained from smartphones that people often carry with them without the need for an auxiliary
factor.
3. Areas of Use of Sensors
3.1. Step Detector
One of the most important aspects of personal health is to stay active during the day. It is necessary
for a healthy life for people to get out of situations where they remain stationary and move, walk and
run. Thanks to the studies on the step detector, people have had the opportunity to track how many
steps they take during the day. Although different tools can be used for step detection and step
counting, accelerometer sensors are generally used. For this process, basically 4 stages can be
mentioned as data acquisition from the accelerometer sensor, noise reduction, detection of stepping
and whether to count it as a step or not (6). Although the most common method is to calculate the
magnitude of the x, y, z data from the accelerometer sensor and compare this value with a threshold
value, many different methods have also been used. In some studies, the x, y, z data from the
accelerometer sensor were sent into a mathematical function and the output value was used together
with the zero crossing method (6) (7). In some studies, the user's walking frequency was also included
in the calculations (8). In addition to the different methods used, studies have also taken into account
the activity in which the data from the accelerometer was obtained while the person was performing
the activity or the position in which the smartphone was in during the determination of stepping. In
a previous example study on this topic, the values of the three axes obtained from the accelerometer
sensor were processed with various formulas and a net magnitude value was calculated. In the next
step, a dynamically running peak detection algorithm and an algorithm for detecting false peaks and
real peaks were used using this calculated magnitude value. In the following steps, step detection was
performed using the start vector and end vector values obtained by these algorithms. Using the data
obtained with the algorithms and methods used, the step length was determined and the distance
traveled was estimated accordingly (9).
In another study on step detector and step detection, values such as sampling rate, stepping rate
and average of orthogonal accelerations are taken into account while determining the threshold
value.
The algorithm was designed and tested at different walking speeds and different phone positions.
As a result of the tests, very high success percentages were obtained (10).
3.2. Vehicle Use Behaviors
Although it is not directly related to human health, both physical and psychological health of a
person can be injured in a traffic accident that may occur. The majority of these traffic accidents are
caused by human errors and the effects of these errors. In order to prevent or minimize the
negativities that may occur as a result of traffic accidents, it has been possible to make applications
using the sensors in the smartphone hardware. In these applications, two issues related to driving
behavior can be examined: the behavior of the driver using the vehicle and the way the vehicle is used
(11). The driver's behavior can include whether the driver is focused on the road or whether the
driver is on the phone while driving. The manner in which the vehicle is driven can include
aggressively pressing the gas and brakes or making very sharp turns on bends. In a study on this topic,
the data obtained from the accelerometer and gyroscope sensors on the smartphone in the vehicle
were used, and the driver's behaviors such as aggressive cornering, aggressive brake pedal use and
aggressive accelerator pedal use were detected by comparing the sensor data using the DTW
algorithm. With a certain number of signals, the driver's maneuvers were identified (12). When
another study was examined, it was seen that classes such as aggressive driving, normal driving,
aggressive braking and normal braking were determined in order to detect driving behaviors with
the accelerometer sensor and datasets of the data obtained from the accelerometer sensor related to
these classes were created. In the following stages, classification processes were performed and
tested with the help of MLP (Multilayer Perceptron), RF (Random Forest), KNN (K-Nearest
Neighbors) and GNB (Gaussian Naïve Bayes) algorithms and methods.
After the tests, F-scores for the classification of different event types were observed (13). Various
studies have been conducted with different classifications, methodologies and methods, such as using
the ANN (Artificial Neural Network) algorithm (14). It has been observed that various success
percentages have been observed with different classification algorithms and different scenario
classes.
3.3. Activity Detection
Another topic related to the field of smartphone sensors is activity detection. With the
accelerometer and gyroscope sensors detecting information such as vibration, rotation, acceleration,
deceleration, tilt, etc., the orientation of smartphone users' activities such as running, walking and
climbing can be determined. Thanks to the sensors of systems such as smartphones, it has been
possible to receive data, process the data and perform motion detection without the need to integrate
another external sensor (15). For activity detection, data is first collected from mobile sensors such
as accelerometers and gyroscopes during different activities. After collecting the data, certain features
such as speed and orientation are extracted for each activity. After these features are extracted, tests
are performed and the activity is detected according to the result of the test. For activity detection,
machine learning and classification algorithms such as kNN (2), Logistic Regression (2), SVM, Random
Forest, Naive Bayes, Bayesian Networks, Multilayer Neural Network, Ameva, K-Means Clustering (16)
are important. In a study on this subject, activity detection was performed by classifying with SVM,
LR and J48 decision tree algorithms. Activities were used as standing, walking, running, running, lying
down, getting up, and getting down.
As a feature, mean, energy, entropy, standard deviation and correlation were used. Accelerometer
and gyroscope sensors were used to measure and collect the data. The highest percentage results for
all three algorithms were obtained for the lying down activity. The lowest result was recorded in the
detection of sitting activity with the SVM algorithm. In general, when looking at the average of all, the
LR algorithm gave the highest result (17). (18) compared the same six activities with SVM and HF-
SVM (Hardware Friendly SVM). 789 test instances were evaluated with approximately equal
instances per class and it was observed that although the percentage of detection of up and down
activities was low in both algorithms, the overall detection percentage was high. In the study by (19),
XGB, SVM, NN, Soft voting, Hard voting algorithms and gyroscope sensor were used. 2 different
numbers of features (195 and 304) were tested. Normal walking, fast walking, going down, going up,
going down, going up and sitting were used as activities. Testing was done for three different
situations. In the first case, bag walking and normal walking were combined as a single activity. In the
second case, normal, fast and bag walking were combined as a single activity. The results showed that
the Soft Voiting approach had the highest detection results.
3.4. Condition Monitoring in Parkinson's Disease
Among the many studies conducted in the field of health with integrated sensors used in
smartphones, one of the most important ones is the monitoring of the condition of Parkinson's
patients. Using accelerometer and gyroscope sensors in smartphone hardware, measurements such
as tremor, slow movement and loss of balance can be used to evaluate motor functions and diagnose
as well as severity (20). Neural networks including back-propagation algorithms are frequently used
in the problem of classifying data about patients and in solving this problem. In addition, Levenberg -
Markard algorithm and conjugate gradient algorithms have also been used (21). In a study on this
subject, during the data analysis and preprocessing process using Python, the sensor data of each
participant were organized by side (right or left), sensor mode, activity and session, and then
organized according to the timestamp with the BioStampRC application. Left (right) side sensor data
were matched to the corresponding side clinical scores for bradykinesia, tremor and dyskinesia, and
all sensor data obtained from the accelerometer and gyroscope were segmented into 5-second clips
with 50% overlap. To remove the limb orientation effect and to detect bradykinesia, high or low pass
filters were applied to the accelerometer and gyroscope data at specific frequencies. These filters
were found to be helpful in symptom detection. Each of the accelerometer and gyroscope data from a
total of 41,802 clips obtained in this process was matched with the corresponding patient ID, side,
activity, session and clinical score data (22).
4. Project Presented
4.1. Healthy Driving Practice
Within the scope of healthy driving, our application aims to focus the driver on the road and driving
in situations where the driver is talking on the phone or interested in the phone. In this application, 3
classes have been determined and these classes include the state of the phone in the holder / in front
of the user while driving, the state of the phone in the pocket while driving, and the state of talking on
the phone while driving (23). In order to perform the classification process, KNN (K-Nearest
Neighbors) algorithm (13) was used with a dataset and classification was performed with different K
values such as 3, 5, 7, 9, 11. The success of the classification process performed with different K values
in the KNN algorithm.
The classified scenarios were realized with the application and our testing processes were carried
out in order to determine its status. For the data collection process to be used in the classification
process of our application, sample scenarios belonging to 3 classes were realized through the
application we wrote with Android Studio and Java, and the x, y, z axis data obtained from the
accelerometer sensor with a frequency of 0.2 Hz were collected and saved in a file to create our data
set in the specified format. Figures 1, 2 and 3 below show the graphs of the accelerometer data
collected for the sample scenarios that constitute our dataset.
Figure 1: Accelerometer data collected with the driver's phone in his pocket
Figure 2: Accelerometer data collected while the driver was talking on the phone
Figure 3: Accelerometer data collected while the phone is in the holder
4.2. Fainting Detection App
Within the scope of fainting detection of the application made by us, it is aimed to send
notifications to the relevant people in cases where we detect the user fainting. In this application, 4
classes have been determined and these include the user's sitting state, the user's walking state, the
user's standing state and the user's fainting state (24). In order to perform the classification process,
KNN (K-Nearest Neighbors) algorithm (10) was used with a dataset and classification was performed
with different K values such as 3, 5, 7, 9, 11. In order to determine the success of the classification
process performed with different K values in the KNN algorithm, the classified scenarios were
realized with the application and our testing processes were carried out.
For the data collection process to be used in the classification process of our application, we
realized sample scenarios of 4 classes through the application we wrote with Android Studio and Java
and collected data from the accelerometer and gyroscope sensor.
With a frequency of 0.2 Hz, the x, y, z axis data were collected and saved in a file with xls extension
in the specified format to form our data set. Figures 4 and 5 below show the graphs of the
accelerometer and gyroscope data collected for the sample fainting scenario that constitutes our
dataset.
Figure 4: Accelerometer data for the sample fainting scenario
Figure 5: Gyroscope data for the example fainting scenario
4.3. Step Counter App
Within the scope of the step counter of the application made by us, it is aimed to detect the user's
stepping motion with step detection methods and display it with a counter and accordingly display
information such as distance, speed, energy expenditure.
Each time the data coming from the accelerometer with a certain frequency changes, we get a
result from the functions of formula (4.1) and formula (4.2) given below (7).
(4.1)
(4.2)
The step will be counted when we detect that the result obtained from the functions has
transitioned from negative to positive or from positive to negative using the zero crossing method. In
order to eliminate the problems that may arise from the detection sensitivity of the accelerometer
sensor (9), the validity of a step is determined by the requirement of a minimum of 0.25 seconds more
than the previous valid step (7).
On the one hand, while the number of steps is determined, the distance information is calculated
using the height information and the number of steps we receive from the user, the average speed
information is calculated using the distance and time change, and the amount of energy consumed is
calculated using the weight information we receive from the user (25). We calculate the average speed
information with the formula (4.3) given below.
(4.3)
5. Research Results
5.1. Step Counter App Results
In the step detection part, which is the basis of our step counter application, the x, y, z values
obtained from the accelerometer sensor were calculated with the formulas and zero crossing method
we have previously mentioned (7). In order to determine the accuracy of our operations, we tested
the success of the step detector application in cases where the user holds the phone steady in his/her
hand, the user shakes the phone in his/her hand, and the user's phone is in the pocket (10) and the
following results were obtained.
Success percentage of the step detection algorithm:
Phone held steady in hand, 99.0%
Phone shaken in hand, 87.0%
Phone in pocket, 93.0%
In addition to step detection, the user can track and monitor the values of time, distance, speed andenergy
expenditure with the information we receive from the user and other calculations we have made.
5.2. Fainting Detection Implementation Results
In our fainting detection application, we performed classification using the KNN (KNearest
Neighbor) algorithm (10) on the data we obtained. In order to find the appropriate K value to be used
in the KNN algorithm, the classification success of different values were tested and our specific results
were obtained. With the data obtained by the accelerometer sensor on the smartphone, the
classification process at a frequency of 1 Hz was performed with the algorithm we coded and analyzed
by us by viewing it on the application. Classification processes and classified scenarios of the user's
sitting state, the user's standing state, the user's walking state and the user's fainting state (24), which
are the 4 classes we have determined, were performed with the application and our testing processes
were carried out.
The results we have obtained are shown in our success percentage graph displayed in Figure 6
below with the data visualization methods we have made through Python.
Figure 6: Classification success percentages in our fainting detection application
As a result of our tests, in order to determine the K value to be used in the KNN algorithm, the
average classification success of each K value was examined by us. As a result of our examination, we
found that when K is 3, there is an average success of 71.25%, when K is 5, there is an average success
of 76.67%, when K is 7, there is an average success of 77.92%, when K is 9, there is an average success
of 72.92%, when K is 11, there is an average success of 65.42%, and based on the results of our
observations, the value to be used in the algorithm was determined.
As a result of the data we collected and the classification we made with the KNN algorithm, if the
user is detected to be unconscious, an application that sends an SMS notification to a specific phone
number was written by us using Android Studio and Java.
5.3. Healthy Driving Practice Results
In our healthy driving application, the classification process was performed using the KNN (K-
Nearest Neighbor) algorithm (13) on the data we obtained.
KNN in order to find the appropriate K value to be used in the algorithm, the classification success
of different values was tested and our specific results were obtained. With the data obtained by the
accelerometer sensor on the smartphone, the classification process at a frequency of 1 Hz was
performed with the algorithm we coded and analyzed by us by displaying it on the application.
Classification processes and classified scenarios for the 3 classes we have determined, which are
the state of having the phone in the holder/front while driving, the state of having the phone in the
pocket while driving, and talking on the phone while driving (23), were carried out together with the
application and our testing processes were carried out.
The results obtained are shown in our success percentage graph displayed in Figure 7 below with
the data visualization methods we made through Python.
Figure 7: Classification success percentages in healthy driving
As a result of our tests, in order to determine the K value to be used in the KNN algorithm, the
average classification success of each K value was examined by us. As a result of the examination, we
found that when K is 3, the average success rate is 54.44%, when K is 5, the average success rate is
68.89%, when K is 7, the average success rate is 65.56%, when K is 9, the average success rate is
60.55%, when K is 11, the average success rate is 59.44% and based on the results of our
observations, the value to be used in the algorithm was determined. As a result of our data set and the
classification process we performed with the KNN algorithm, the actions to be taken and the actions
to be taken depending on the user's use of the smartphone while driving are provided by the mobile
application written by us with Android Studio and Java.
5.4. Application Overview
Figure 8 below shows the interface of the step counter application, where the number of steps
taken by the users is determined and the users can observe various information as a result of the
other calculations we have made. When users specify their weight and height and open the
application, they will be able to access the relevant information on this interface.
Figure 8: Interface of our step detection application
Figure 9 below shows the KNN algorithm we used, the classes we determined and the data sets we
obtained as a result of the data collection process we carried out; There is an interface that allows us
to observe how much success percentage we have achieved in the classification process, which is the
basis of our application.
Figure 9: Fainting detection classification interface
An audible warning is given to indicate that people using our application are not focusing on the
road during driving activity. Again, when the fainting condition of the users of our application is
detected, a notification is sent to the mobile phone number specified in the application. Figures 10
and 11 below show the interfaces where information appears on the screen according to the
classifications made while the application is running.
Figure 10: Fainting detection application information interface
Figure 11: Healthy driving application information interface
6. Conclusion
In our study, different procedures in previous studies or information obtained from other sources
have produced different results. The zero-crossing method and formulas (7), which we used as valid,
obtained more successful and useful results than statically determining a limit value and comparing
the magnitudes of the axis values obtained from the accelerometer sensor with the limit value and in
the zero-crossing method, the measurement accuracy of the accelerometer sensor is not paid
attention.
The average success percentages of previous studies and our study are shown below for
comparison and evaluation purposes.
Average success percentages of step detector studies: Average Success in our study is 93.00%,
Average Success in the study of (7) is 97.09%, Average Success in the study of (10) is 97.97%.
Although the main purpose of our application is a step counter, we have created an application
where the user can observe the distance traveled, the average speed and the energy consumed, along
with the calculations we have made. Thus, it is possible to observe different features that users may
want to know.
In the general scope of our fainting detection application, we believe that detecting the fainting of
the person using the smartphone, sending a notification with this detection and tracking the other
activities of the user is useful for people who need or may need help in these matters. In the general
scope of our healthy driving application, we believe that it would be useful to detect and warn the
user if the person using the smartphone is interested in the phone or talking on the phone while
driving.
The KNN (K-Nearest Neighbor) algorithm was used for both applications in the classification
sections of our application. With the use of this algorithm, we tested the success of classification
scenarios with K values in a certain range (3, 5, 7, 9, 11). When we examined the success percentages
after our tests, we observed that some of the different K values and different classes had a high success
percentage, some had an average success percentage, and some had a low success percentage.
Below, the highest success percentages of the classifications made in our faint detection and
healthy driving applications in different studies and in our study are given for comparison and
evaluation purposes.
In our study, healthy driving had the highest classification success: Phone in Holder/Front 88.33%,
Phone in Pocket 68.33%, Talking on the Phone 56.67%.
Fainting detection application highest classification success:
In our study; 76.67% in sitting, 96.67% in walking, 98.33% in standing, 58.33% in fainting.
In the study of (17); 94.2% in the Sitting state, 97.2% in the Walking state, 94.8% in the Standing
state, - in the Fainting state.
In the study of (18); 96.4% in the Sitting state, 95.6% in the Walking state, 93.0% in the Standing
state, in the Fainting state.
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