Context-aware smart alarm application
for smart living
Harshith Allamsetti
School of Computing, Informatics and
Decision Systems Engineering
Arizona State University
Tempe, Arizona – 85281
Email: hallamse@asu.edu
Venkat Sanjay Thotapalli
School of Computing, Informatics and
Decision Systems Engineering
Arizona State University
Tempe, Arizona – 85281
Email: vthotapa@asu.edu
Raghavendra Kolloju
School of Computing, Informatics and
Decision Systems Engineering
Arizona State University
Tempe, Arizona – 85281
Email: rkolloju@asu.edu
Akshit Reddy Gudoor
School of Computing, Informatics and
Decision Systems Engineering
Arizona State University
Tempe, Arizona – 85281
Email: agudoor@asu.edu
Abstract—Traditionally we have used alarm clocks to set time
to help us wake up in the morning.However, some people are
still finding it difficult to wake up in the morning even with
the help of alarm clock as a result of which they are missing
their classes, meetings and even exams. The traditional snooze
option of the alarm clock provides the user more time to sleep
by snoozing the alarm sound for a fixed interval of time. We
plan to develop an intelligent alarm clock system for smart-
phones that delivers alarm sounds by taking into consideration
the user’s sleep patterns, context information such as weather
and social information and events on their calendar for the
following day. Our application aims to provide a smart life by
sensing data collected by multiple sensors on the smart-phone
and collaborating with other applications on the smart phone
to recommend a preferable time for the alarm sound.
1. Introduction
Traditional alarm clock’s have been in use for many
years, yet people are still finding it difficult to wake up in
the morning. Our aim is to provide a one step solution in the
form of an mobile application for people who find it difficult
to wake up to the alarm clock as a result of which they are
missing their meetings and classes. Mobile application de-
velopment in recent times has grown exponentially making
the mobile devices far more capable than just being able to
send text messages, or make phone calls. Mobile devices
now provide users with a wide range of functions such
as media players, GPS (Global Positioning System) with
advanced computing abilities etc. Many mobile application
are being developed based on context-awareness that would
provide the users with a wide variety of information about
the surroundings such as temperature, noise levels, location
and proximity detected by various environmental sensors.
There are two major problems that can be seen in the
current sleeping habits of people. First, the problem of over-
sleeping due to lack of proper sleep. Secondly, repeatedly
snoozing the alarm clock results in messing up and missing
out on the scheduled work. In our application we provide the
user only with the option of enabling or disabling the alarm
clock application. When enabled, the alarm clock would
automatically set an alarm based on the user’s sleeping
pattern data and the user’s next day schedule. It would
integrate with the calendar application of the mobile and
then sync the user’s events along with their schedule. The
snooze time is not fixed and would get dynamically adjusted
based on the time left for the event considering the location
of the event and the time it takes to reach to the event in
time. After a certain time threshold the application will no
longer provide the user with the snooze option leaving the
user with no other option but to get up from his bed. So”,
our system provides a smart solution for the users to have
an alarm application that sets up an alarm automatically
and provides dynamic snooze patterns depending on various
context-aware information.
2. Background
Context-aware computing is essentially a type of com-
puter operation that anticipates cases of use or, in other
words, works in customized ways based on the context of
user activities. This can apply either to a user’s activities
on the device, or the physical environment in which the
device is being used. By using context-aware computing in
the smart alarm systems, we can develop our system that
cooperates with many sources of information in providing
a harmonious and smart living environment.
In general, there are three main approaches of recom-
mendation systems: collaborative filtering based, content-
based and hybrid methods [1]. Collaborative filtering based
methods focuses on users’ previous activities and predilec-
tions, such as items’ ratings, rather than on content informa-
tion of users or products, while content-based methods uti-
lize item descriptions or user profiles for recommendation.
Hybrid methods, literally, combine collaborative filtering
based and content-based information. As for the smart alarm
system, not only content-based information, but the real-
time situations, such as the traffic or the weather would
affect the choice of snooze time for the alarm. For instance”,
when there is a lot of traffic on the route that the user
has to go from his current location to the destined location
where his event would take place, then the alarm time
and the snooze time also changes with the changing traffic
conditions. Recently, context-aware applications has become
popular in the research community for its good performance
by considering the contextual information[2].
Several researchers have identified and classified the dif-
ferent context types based on their perspective[3][4][5]. One
of the most popular classification was provided by Dey[6]”,
separating context into primary and secondary types. He
stated that with the primary contexts time, identity, location”,
and activity it is possible to fully capture any given situa-
tion. Furthermore, he highlighted that all other contexts are
secondary and can be derived from the primary ones. While
we agree with the importance of the primary contexts, we
do not consider this classification useful. One contradictory
example are machine conditions, such as the temperature
of a machine, which would be considered secondary even
though they cannot be derived from any of the stated primary
contexts. This shows that the distinction between more and
less important contexts is not always possible as it highly
depends on the use case.
When dealing with context, three entities can be distin-
guished : places (rooms, buildings etc.), people (individuals”,
groups) and things(physical objects, computer components
etc.). Each of these entities may be described by vari-
ous attributes which can be classified into four categories:
identity (each entity has a unique identifier), location (an
entitys position, co-location, proximity etc.), status (or ac-
tivity, meaning the intrinsic properties of an entity, e.g.”,
temperature and lightning for a room, processes running
currently on a device etc.) and time(used for time-stamps
to accurately define situation, ordering events etc.).
3. Related Work
Both industry and academia have produced an impres-
sive amount of research work dedicated to providing new
ideas towards building smart applications for a smart living.
While we cannot describe all the applications that exist in
the literature, we summarize prior art that closely relates to
context-aware applications and smart alarm systems.
Many smart applications have been built on context-
based criteria where the applications does some actuation
based on the information sensed from the sensors. Mfundo
Masango et al.[7] proposed an application that provides user
authentication methods that are set up according to the auto-
detection pf areas designated as safe zones by the user. De-
Vaul et al.”,[8] proposed a wearable context-aware reminder
system that delivers reminders based on time, location and
user activity. It focuses on personal context and uses body-
worn sensors to determine in which activity the wearer
is engaged. Cheverest et al.[9], proposes an application
that integrates the use of personal computing technology”,
wireless communication, context-awareness, and adaptive
hypermedia to support the information and navigation needs
of visitors to the city of Lancaster. The context such as
the visitor’s interests, current location, and any refreshment
preferences dynamically affects the interface of the system.
A context-awareness infrastructure in the hospital described
by Bardram and Hansen[10] is aware of the location of
nureses, patients, the trolley and the bed; a bed is equipped
with various RFID sensors that can oidentify a patient
lying in the bed and be aware of patient’s conditions and
treatment.
With extensive research being done in context-aware
applications, the context based models have been integrated
into alarm systems on smart mobiles to provide better
user interaction and smart solutions. Jiaqi Wang et al.[11]
proposed a smart alarm sound recommendation system by
considering not only specific information, such as sleep
patterns, but also context information such as weather, and
social information. Shahreen Kasim et al.”,[12] developed an
Android application that can force the user to wake up by
using the SMS service and the pedometer. The application
needs the user to wake up and walk 10 steps for the alarm
to be disabled and it also sends SMS to the users parents
or friends to wake them up.
4. System Design
Our applications will have only one toggle button to
enable or disable alarm. Once enabled and required permis-
sions are granted (such as calendar access, sleep patterns)”,
the application automatically suggests the alarm time, sound
and snooze frequency. The key components of our system
are raw data collection, feature extraction, data integration”,
context aware model, user history and smart alarm sug-
gestion system. Following subsections explain briefly about
each component.
4.1. Raw Data Collection
To recommend a proper alarm time, sound and snooze
time, it is very important to understand the user. Each
human being has different preferences for these aspects. We
approximate these preferences by analysing his behaviour
such as his recent alarm patterns, selected sound patterns”,
sleep patterns (average sleep timings, etc). Here we also
integrate with other applications such as calendar, weather”,
and location to get the scheduled events for the user and the
current context the user is in. From this raw data, we extract
a set of features as described in the next sub section.
4.2. Feature Extraction
The alarm application is made context aware by ex-
tracting relevant features from the raw data and using these
features to predict what might be the right settings for the
alarm for the current context. Below are the set of features
we propose.
4.2.1. Event Features. For the raw event data that is
collected from calendar app, we extract event features.
Event features consists of next event on the calendar”,
event timings, event location. Suppose if the user has
to attend a conference which needs him to travel 3
hours, it is ideal to set alarm at least 4 hours before
the event. So along with event timings, event location
also is an important feature. Real-time weather also
plays an important role in deciding the alarm time
so we include that as one of the event features.
Figure 1:Proposed System Architecture
4.2.2. User Features. Alarm settings depend on user’s cur-
rent context such as his emotional state, if he is sleeping or
if he is already headed to the event. There are models that
are designed to understand the user emotional state such
as Arousal-Valence model[13]. Using this model, we can
predict if the user is angry, excited, calm or bored. This
prediction can be used to suggest the proper alarm sound.
Each sound in the alarm sound library can be mapped to a
vector to indicate which mood it suits.
4.2.3. User Info. User’s social information such as age”,
gender, occupation, nationality and education level play an
important role in deciding the alarm sound and loudness. If
the user’s occupation requires him to work in a noisy place”,
louder alarms are proper.
4.3. Context Aware Model
Based on the features extracted, user history, our smart
alarm system predicts the alarm time, tone and snooze time.
Each alarm tone in the library is mapped to a feature vector
that can be used to decide which alarm suits for which
environment. These features depend on the loudness, mood”,
tempo, etc of the sound. Context aware model predicts users
current mood, environment (noisy/sleeping/event priority)
and decides which sound to play.
5. Challenges
There will be a lot of complications associated with the
data used in our application. During raw Data collection, the
collected data has to be extremely specific to that user. Our
application requires getting event data from the calendar, it
has to be in sync with the calendar data, any changes in the
calendar application such as creation of an event”,deletion or
updating of an event should reflect the data we have in our
application.
An other form of data source for our application is
pattern data such as alarm”,sleep and selected sound pattern”,
to obtain such data we need to study the user for a long
period of time and use this data and several machine learning
techniques to identify patterns and interests.
We also need to decide what feature extraction models
have to be implemented to come up with features that
give out the maximum information about a user and his
forthcoming events. In the event features, we need to find
how our application will understand the event attributes
such as location, time and make intelligent decisions for
the user to wake him up keeping real time data such as
navigation, scheduling in mind.Our application also has
dependencies with navigation and calendar applications.In
the case of User Features”,Arousal-Valence model has to be
implemented to get his current emotional state. Intelligence
has to be implemented to change the alarm settings based
on user info.
The biggest challenge will be to integrate or keep in
mind user’s calendar data, user’s sleep and sound patterns
and user’s general information and then make a decision.So”,
there will be multiple cases where conflicts might arise.
6. Conclusion
With the implementation of the idea, we proceed towards
developing an application that detects user sleep patterns”,
and the events in the calendar to set the alarm time and
the snoozing pattern of the alarm. With this, the user need
not set up an alarm manually and the application sets up
the alarm for him and also the snoozing patterns are set up
dynamically according to the events in the calendar
Acknowledgments
We would like to thank Dr.Ayan Banerjee for giving
us this opportunity to develop an Idea Paper on Mobile
Computing. We would like to acknowledge the previous au-
thors mentioned in the references for providing a foundation
towards our idea. Lastly, we would like to thank all the team-
members for equally contributing towards this idea.
References
[1] J. Bobadilla, F. Ortega, A. Hernando, A. Gutirrez, Recommender
systems survey, Knowledge-Based Syst. 46 (2013) 109132.
[2] G. Adomavicius, A. Tuzhilin, Context-aware recommender systems”,
in: Recommender Systems Handbook, Springer, 2015, pp. 191226.
[3] Yanwei, S., Guangzhou, Z., & Haitao, P. (2011). Research on the
context model of intelligent interaction system in the internet of
things. In IT in Medicine and Education (ITME), 2011 International
Symposium on (Vol. 2, pp. 379-382). IEEE.
[4] Chen, G., & Kotz, D. (2000). A survey of context-aware mobile
computing research. Technical Report TR2000-381, Dept. of Computer
Science, Dartmouth College.
[5] Chong, S. K., McCauley, I., Loke, S. W., & Krishnaswamy, S. (2007).
Context-aware sensors and data muffling. Context awareness for self-
managing systems (devices, applications and networks) proceeding”,
103-117.
[6] Dey, A. K. (2001). Understanding and using context. Personal and
ubiquitous computing, 5(1), 4-7.
[7] Mfundo Masango, Francois Mouton, Alastair Nottingham and Jabu
Mtsweni(2007). Context Aware Mobile Application for Mobile De-
vices. DOI: 10.1109/ISSA.2016.7802933. Information Security for
South Africa, At Johannesburg, South Africa
[8] DeVaul, R. W., B. Clarkson, and A. Pentland. (2000). The Memory
Glasses: Towards a Wearable Context Aware, Situation-Appropriate
Reminder System, CHI 2000 Workshop on Situated Interaction in
Ubiquitous Computing, Hague, Netherlands.
[9] Cheverst, K., K. Mitchell, and N. Davies. (1998). Design of an Object
Model for a Context Sensitive Tourist GUIDE. Workshop on Interactive
Applications of Mobile Computing (IMC’98), Rostock, Germany, pp.
883-891.
[10] Bardram, J. E. and T. R. Hansen. (2004). The AWARE Architecture:
Supporting Context-Mediated Social Awareness in Mobile Coopera-
tion, Proceedings of the 2004 ACM Conference on Computer Sup-
ported Cooperative Work, Chicago, Illinois, USA, pp. 192-201.
[11] Jiaqi Wang, Yanxiang Guo, Wenhan Han, Jianbo Zheng, Jun Cheng.
Mobile crowdsourcing based context-aware smart alarm sound for
smart living. Pervasive and Mobile Computing – Vol 55 (32-44)
[12] Shahreen Kasim, Hanayanti Hafit, Tan Hua Leong, Rathiah Hashim”,
Husni Ruslai, Kamaruzzaman Jahidin, Mohammad Syafwan Arshad.
SRC: Smart Reminder Clock. IOP COnference Series: Material Sci-
ences and Engineering – 2006.
[13] P. Bustamante, N.L. Celani, M. Perez, O.Q. Montoya, Recognition
and regionalization of emotions in the arousal-valence plane, in: Engi-
neering in Medicine and Biology Society (EMBC), 2015 37th Annual
International Conference of the IEEE, IEEE, 2015, pp. 60426045.
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