Iaetsd habitual descend recognition

HABITUAL DESCEND RECOGNITION
SCHEME BY SMART PHONE APPLICATION
Dr.R.Dhaya,
Dept. of CSE,
Velammal Engineering College,
Chennai-66.,
dhayavel2005@gmail.com
Ambhika.C., PG Student,
Dept. of CSE,
Chennai-66.
ambhikac@gmail.com
Anjana Devi.J, PG Student,
Dept. of CSE,
Chennai-66
anjanajavar@gmail.com
.
ABSTRACT:
The mobile application being proposed
is capable of detecting possible falls, through the use
of special sensors and through a user friendly
interface that can be used to alert relatives, doctors or
other people who take care of the elderly. The alert
messages contain useful information about the people
in danger, such as his/her geo-location and also
corresponding directions on a map. In occasions of
false alerts, the supervised person is given the ability
to estimate the value of importance of a possible alert
and to stop it before being transmitted. The system is
capable of monitoring elderly people in real time.
The Accidental Fall Detection System will be able to
assist care takers as well as the elderly. This fall
detection system is designed to detect the accidental
fall of the elderly and to alert relatives, doctors or
other people who take care of the elderly via
Messaging Services (SMS) immediately. In this
application, the accelerometer is used as to detect the
sudden movement or fall and the Global System for
Mobile (GSM) modem, to send out SMS to the care
taker. The objective of this paper is to provide the
security for the people who may suffer a fall, by an
advanced android application with Android mobiles.
Key Words: Fall Detection, Sensors, Geo Location,
SMS, GSM
1. INTRODUCTION
Falls increase risk for serious injuries,
chronic pain, long-term disability, and loss of
independence, psychological and social limitations
due to institutionalization. Nearly 50% of older adults
hospitalized for fall- related injuries are discharged to
nursing homes or long-term care facilities. A fall can
cause psychological damage even if the person did
not suffer a physical injury. Those who fall often
experience decrease activities of daily living and self-
care due to fear of falling again. This behavior
decreases their mobility, balance and fitness and
leads to reduced social interactions and increased
depression. The mortality rate for falls increases
progressively with age. Falls caused 57% of deaths
due to injuries among females and 36% of deaths
among males, age 65 and older . Majority of falls
result from an interaction between multiple long-term
and short-term factors in person’s environment .
Common risk factors include problems with
balance and stability, arthritis, muscle weakness,
multiple medications therapy, depressive symptoms,
cardiac disorders, stroke, impairment in cognition
and vision Detection of a fall possibly leading to
injury in timely manner is crucial for providing
adequate medical response and care. Present fall
detection systems can be categorized under one of the
following groups:
• User activated alarm systems (wireless
tags),
• Floor vibration-based fall detection,
• Wearable sensors (contact sensors and
switches, sensors for heart rate and
temperature, accelerometers, gyroscopes),
• Acoustic fall detection,
• Visual fall detection.
The most common method for fall detection
is using a triaxial accelerometers or bi-axial
gyroscopes. Accelerometer is a device for measuring
acceleration, but is also used to detect free fall and
shock, movement, speed and vibration. Using the
threshold algorithms while measuring changes in
acceleration in each direction, it is possible do detect
falls with very high accuracy. Using two or more tri-
axial accelerometers and combining them with
gyroscopes at different body locations it is possible to
recognize several kinds of postures (sitting, standing,
etc.) and movements, thereby detecting falls with
much better accuracy. An easy and simple method to
detect fall detection of people is using accelerometer
together with ZigBee transceiver to communicate
with Monitoring System through wireless network,
and in this paper a system for monitoring and fall
detection of people using mobile MEMS
accelerometers will be presented.
The first three functions provide recording
in a database, and also a text message is sent to the
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ISBN:378-26-138420-0286

supervisor with latitude, longitude and other useful
data. Afterwards, you can detect the elder person
through Google maps. Additionally, an application
was implemented for the attending physician, which
is connected with the database, through which s/he
can obtain a complete picture of the patients’ status,
to draw useful conclusions and proceed to possible
change in medical treatment.
Today, majority of people are smart phone
users Falls increase risk for serious injuries, chronic
pain, long-term disability, and loss of independence,
psychological and social limitations due to
institutionalization. Nearly 50% of older adults
hospitalized for fall- related injuries are discharged to
nursing homes or long-term care facilities. A fall can
cause psychological damage even if the person did
not suffer a physical injury. The mobile application
being proposed is capable of detecting possible falls,
in case of an elderly person or an unhealthy person,
such that the delay in treatment may be avoided due
to inefficient communication in situations where the
person is unconscious. This can be done through the
use of special sensors and through a user friendly
interface that can be used to alert relatives, doctors or
other people who take care of the elderly.
2. OBJECTIVE OF THIS PAPER
The system is capable of monitoring elderly
people in real time. The accidental fall detection
system will be able to assist care takers as well as the
elderly. It is an application with the ability of
automatic fall detection, by using the mobile sensors,
warning signal by pressing a button in cases of
emergency, detection and automatic notification to
supervisors as well as visual display to passerbies.
The application uses basically two incorporated
mobile sensors, namely the accelerometer and the
gyroscope sensor. A counter starts counting loudly on
the screen from 30 to 0. If the counter reaches 0, then
an SMS message is sent to the caregiver or relative
and an entry is made to the database. The first service
detects the patient’s position and calculates whether
the patient is further away than a set distance. When
activated can give directions to the patient what route
to follow to return back to home. This fall detection
system is designed to detect the accidental fall of the
elderly and to alert relatives, doctors or other people
who take care of the elderly via messaging services
(SMS) immediately. In this application, the
accelerometer is used as to detect the sudden
movement or fall and the global system for mobile
(GSM) modem, to send out SMS to the care taker.
2.1 SCOPE
Falls in people happen due to reasons like
old age, dementia, Parkinson’s disease, learning
disabilities and poor motor control. Falls result in
broken or fractured bones, cuts, abrasions, soft tissue
damage, and even death. They have psychological
consequences, fear of falling leads to isolation,
worsening of mental health, and general degradation
of quality of living. If falls cannot be
prevented, the next best option is detecting them
accurately, and summoning the required help
immediately. An automatic fall detection system can
do this; however, to be usable, the system should not
be prohibitively expensive, should be convenient to
carry on one's person, and should be capable of
generating alerts even if the user is rendered
unconscious due to fall. Smartphone’s are thus
uniquely positioned; they can gather information
about their surroundings using built-in sensors,
process it in real-time, and generate alerts in various
forms. In this paper, we design and develop an
Android application that monitors device sensors to
calculate device acceleration and orientation, and
detects occurrence of fall using these inputs and real-
time pattern recognition techniques. We also describe
sensor data collection, including fall signature
patterns, and alternative approaches tried for fall
detection. On detecting a fall, the application
generates alerts like emails, text messages, and voice
calls. The specifics of alerts to be generated and
persons to be contacted are configured by user. We
use speech recognition to reduce false positives and
improve fall detection accuracy. We also test this
application against a number of daily activities and
falls, and present the results.
3. LITERATURE SURVEY
The authors [1] presented an application for
mobile phones which can monitor physical activities
of users and detect unexpected emergency situations
such as a sudden fall or accident. Upon detection of
such an event, the mobile phone can inform a
designated centre (by automatically calling or
sending message) about the incident and its location.
The application operates based on analysis of user
movements using data provided by accelerometers
integrated in mobile phones. The authors [3] used a
common Android-based smart phone with an
integrated tri-axial accelerometer. The threshold is
adaptive based on user provided parameters such as:
height, weight, and level of activity. These variables
also adapt to the unique movements that a cell phone
experiences as opposed to similar system which
require users to mount accelerometers to their chest
or trunk. If a fall is suspected a notification is raised
requiring the user’s response. If the user does not
respond, the system alerts prespecified, social
contacts with an informational message via SMS.
When a contact responds with an incoming call the
system commits an audible notification,
automatically answers the call, and enables
speakerphone. If a social contact confirms a fall, an
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ISBN:378-26-138420-0287

appropriate emergency service is alerted. Our system
provides a realizable, cost effective solution to fall
detection using a simple graphical interface while not
overwhelming the user with uncomfortable sensors.
The authors [5] proposed a fall detector that uses the
accelerometers available in smart phones and
incorporates different algorithms for robust fall
detection such as thresholding and wavelet
transforms. We implemented our fall detector on a
smart phone running the Android 2 operating system.
Our experimental results show that compared to a
simple thresholding algorithm, using wavelet
transforms achieve better true positive performance
while decreasing the rate of false positives
drastically. Besides the fall detection capability, our
implementation also provides location information
using Google Maps about the person experienced the
fall, using the available GPS interface on the smart
phone and a warning about the fall and the
location information are transmitted to the users,
such as the caregivers, via SMS, email and Twitter
messages. The authors [8] proposed a mechanism
which suit the Elderly persons with Alzheimer’s
disease and dementia have many behavior disorders
such as wandering, repeatedly questioning and being
uncooperative during the day. Their wandering
behavior is a major cause of death, so it is an
especially serious problem for caregivers. It is
therefore very important to monitor the wanderer’s
location. Numerous mobile phone-based location
detection systems have been developed. The location
is obtained by the caregivers accessing the mobile
company; however, the caregiver is not notified that
the wanderer has left home, which is a major problem
of these systems. In this study, the newly-developed
system immediately detects that the wanderer is away
from home and then automatically transmits
notification of the wandering elderly person’s
location to the caregiver once a minute. The
authors [9] explored the use of mobile social network
technology combined with modern mobile phone
hardware as a platform for programming applications
in the elder care area. An application that covers two
use cases for outdoors monitoring and detecting
disorientations of the elderly is introduced. The
system leverages on standard mobile terminals
(Android G1) equipped with GPS and compass
devices and on Liber Geo-Social, a mobile social
framework we are developing.
The data collected from the device is
evaluated using Bayesian network techniques which
estimate the probability of wandering behavior
[10].Upon evaluation several courses of action can be
taken based on the situation’s severity, dynamic
settings and probability. These actions include
issuing audible prompts to the patient, offering
directions to navigate them home, sending
notiﬁcations to the caregiver containing the location
of the patient, establishing a line of communication
between the patient-caregiver and performing a party
call between the caregiver-patient and patient’s local
911. As patients use this monitoring system more, it
will better learn and identify normal behavioural
patterns which increases the accuracy of the
Bayesian network for all patients. Normal behaviour
classiﬁcations are also used to alert the caregiver or
help patients navigate home if they begin to wander
while driving allowing for functional
independence.Wandering is a behavioural disorder,
which occurs in Alzheimer’s disease or other
dementia. People who wander are at risk of physical
harm and untimely death. Moreover, wandering
behaviour causes a lot of stress to the caregivers. In
the last few years, different geolocation devices have
been developed in order to minimize risk and manage
unsafe wandering[11]. These detection systems rarely
meet patients and caregivers’ needs because they are
not involved in the devices building process.
4. EXISTING SYSTEM
An application for Apple IOS by using an
accelerometer to detect falls. A possible drawback is
that the development platform Apple IOS is not
accessible to the average user. An application in
Symbian s60 using machine learning algorithm takes
64 samples every two seconds from the
accelerometer and decides whether there is a fall.
Disadvantage
o
Not secure and not preserving
privacy
o
The patient can’t be in observation
at all times.
o
In Emergency cases it causes
serious hospitalization for cardiac
Patient.
o
The health care system is not
available for the patients, aged
persons who are out of the facility.
5. PROPOSED SYSTEM
In this paper, we designed an application
with the ability of automatic fall detection, by using
the mobile sensors, warning signal by pressing a
button in cases of emergency, detection and
automatic notification to supervisors as well as visual
display to passerbies. The application uses basically
two incorporated mobile sensors, namely the
accelerometer and the gyroscope sensor. A counter
starts counting loudly on the screen from 30 to 0. If
the counter reaches 0, then an SMS message is sent to
the caregiver or relative and an entry is made to the
Database. The first service detects the patient’s
position and calculates whether the patient is further
away than a set distance. When activated can give
directions to the patient what route to follow to return
back to home.
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ISBN:378-26-138420-0288

Advantages:
• Fall detection along with the patient’s
location is sent to the care taker.
• Helpline video helps the on-goers and
passerby to take the immediate action.
5.1 ARCHITECTURE DIAGRAM
The FALL DETECTION is something that we have
developed at Alert1 so you can be safe at all times. Whether you
are a senior citizen and want to maintain your independence, a
concerned family member looking for peace of mind, or a
caregiver with patients, this tool has been developed for you.
Prevention is Automatic process here. Use it to inspect and
detect hazardous areas in where your people who could result in
a fall. All Android mobiles have its own sensors like
Accelerometer and gyroscope which are having angle
monitoring process. Using that angle monitoring sensors we are
predict the fall detection of elderly people based on the angle
change of mobile and the help of timer. Fall detection conformed
by particular time limitation. The following figure 1
shows the system architecture
Figure 1: System Architecture
Location Tracking: Real-time locating systems (RTLS) are used
to automatically identify and track the location of objects or
people in real time, usually within a building or other contained
area. Wireless RTLS tags are attached to objects or worn by
people, and in most RTLS, fixed reference points receive
wireless signals from GPS to determine their location. The
physical layer of RTLS technology is usually some form of radio
frequency (RF) communication, but some systems use optical
(usually infrared) or acoustic (usually ultrasound) technology
instead of or in addition to RF. Gps and fixed reference points
can be transmitters, receivers, or both, resulting in numerous
possible technology combinations. RTLS are a form of global
positioning system, and usually refer to GPS, mobile phone
tracking, or systems that used only for Effective
Location tracking. Location information usually shows Elderly
people Fall down place.
Communication: SMS Communication that maintained between
the care taker and the elderly people which contain the fall down
location details of Elderly people. That message contain
Emergency alert of URL based Location link. When care taker
get alert message, He can find out the landmark location with the
help of Google map.
Helpline video: Helpline video containing elderly people’s
information’s like address, caretaker numbers, blood group and
the personal medical details. It will be play automatically when
the user getting fall down condition. These Information’s are
very useful when somebody trying to help that elderly people in
that situation.
Route map Integration: If care taker click that URL link from
the message, He will get Elderly people fall down exact location
which is Destination place for care taker. It then integrates this
information into Google Maps through Google Maps API which
displays the position on a map. Since the positional information
is retrieved every second and the maps updated at the same
frequency, a real time GPS tracking effect is achieved. Maybe he
wants to know about Destination place information like
Distance, Route map, etc from source.The integration of spatial
maps in mobile was investigated using a spatial analog to
sensory preconditioning. The GPS chip outputs the positioning
information which is transferred over a GPRS link to the mobile
operator’s GGSN (Gateway GPRS Support Node) and then to a
remote server over a TCP connection.
Figure 2: Activity Diagram
The figure 2 shows the activity diagram
which includes the following steps
• The fall is detected via in-built sensors and it
is detected by the Android application
• The Timer counts from 30-0 sec. and an
alarm is rang- if person responds, the
person is safe. Else the following step takes
place.
• The Timer again counts from 30-60 Sec.
• Two Actions Takes Place
Example: The current location is tracked via
G-Maps and it is sent to the care taker the
details and the current location.
83
ISBN:378-26-138420-0289

• The Pre-Recorded Video of the person is
played which includes person’s details
which can be used by the passerby to alert
the necessary authorities.
• The current location is updated for every
two minutes and is sent to caretaker
6. RESULTS AND DISCUSSION.
This paper has been implemented using
ASP.Net frame. ASP.NET is compiled common
language runtime code running on the server. Unlike
its interpreted predecessors, ASP.NET can take
advantage of early binding, just-in-time compilation,
native optimization, and caching services right out of
the box. This amounts to dramatically better
performance before you ever write a line of code.
The figure 3 shows the android based
system for eldered support system which has the
value of geographical parameters like longitude,
gratitude and login details for people for finding their
locations.
Figure 3: Android Based System For Eldered Support System
Figure 4: Details of the caretaker
The figure 4, 5 show the login details which
includes name, address and phone number. These
values are stored in the database and if anything
happened means the information has been passed to
the contact person.
Figure 5: Date base of caretaker
Figure 6 supports the database system which is
having , all details about the Caretaker .
Figure 6: Database
Figure 7: Updated Caretaker
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ISBN:378-26-138420-0290

Figure 8:SMS Format
Figure 7 shows the updated values of
caretaker. If you want to change any details about the
caretaker, you can edit and these values are added to
the database. The figure 8 shows the final view called
as SMS Format wich informs the happen of the
patient about the caretaker with the help of google
map.
7. CONCLUSION AND FUTURE WORK
This fall detection technique can be used for
detecting fall of any ailing people. They offer low
cost solution, and together with wireless connectivity
solutions such as ZigBee provide efficient solution
for both patients and medical personnels. In this
paper,we have presented an intelligent mobile
multimedia application that can be incorporated into
mobile smartphones in order to be used for the needs
of any people who may suffer a fall. It is in our future
plans to evaluate this system in order to test its
efficiency in actually helping these people
sufficiently.It is also in our future plans to extend the
system’s capabilities by incorporating new services.
These services include the following:
• Embed a belt measuring heart rate as an
external sensor
• Integrate a gyroscope sensor instead of an
orientation sensor, for more accurate results
• Integration of social networks to alert
senders
• Integrate public agency to alert senders
• Add a system administrator feature.
REFERENCES
[1] Hamed Ketabdar,Tim Polzehl: Fall and
Emergency Detection with Mobile Phones,
Proceedings of the Eleventh International ACM
SIGACCESS Conference on Computers and
Accessibility, pp. 241-242, Pittsburgh, USA, 2009
[2] Zhongtang Zhao,Yiqiang Chen,Junfa
Liu: Fall Detecting and Alarming Based on Mobile
Phone, Ubiquitous Intelligence & Computing and 7th
International Conference on Autonomic & Trusted
Computing (UIC/ATC), 2010.
[3] Frank Sposaro, Gary Tyson: iFall: An
Android Application for Fall Monitoring and
Response, Engineering in Medicine and Biology
Society, 2009. EMBC 2009. Annual International
Conference of the IEEE.
[4] Jiangpeng Dai, Xiaole Bai, Zhimin
Yang, Zhaohui Shen and Dong Xuan: PerFallD: A
Pervasive Fall Detection System Using Mobile
Phone, Pervasive Computing and Communications
Workshops (PERCOM Workshops), 8th IEEE
International Conference, 2010.
[5] Gokhan Remzi Yavuz, Mustafa Eray
Kocak, Gokberk Ergun, Hande Alemdar, Hulya
Yalcin, Ozlem Durmaz Incel, Lale Akarun, Cem
Ersoy: A Smartphone Based Fall Detector with
Online Location Support, In Proceedings of
PhoneSense, November 2010.
[6] Global Positioning System:
http://el.wikipedia.org/wiki/Global_Positioning_Syst
em
[7] Koichi Shimizu, Kuniaki Kawamura and
Katsuyuki Yamamoto: Location System for
Dementia Wandering, Proceedings of the 22nd
Annual EMBS International Conference, July 23-28,
Chicago IL, 2000.
[8] Hidekuni Ogawa, Yoshiharu Yonezawa,
Hiromichi Maki, Haruhiko Sato and W. Morton
Caldwell: A mobile phone-based Safety Support
System for wandering elderly persons, Proceedings
of the 26th Annual International Conference of the
IEEE EMBS, San Francisco, CA, USA, September 1-
5, 2004.
[9] Roberto Calvo-Palomino, Pedro de las
Heras-Quiros,Jose Antonio Santos-Cadenas, Raul
Roman-Lopez and Daniel Izquierdo-Cortazar:
Outdoors Monitoring of Elderly People Assisted by
Compass, GPS and Mobile Social Network, 10th
international work-conference on artificial neural
networks, IWANN 2009 workshops, Salamanca,
Spain, June 10-12, 2009. Proceedings, Part II. Berlin:
Springer (ISBN 978-3-642-02480-1/pbk). Lecture
Notes in Computer Science 5518, 808-811, 2009.
[10] Frank Sposaro, Justin Danielson, Gary
Tyson: iWander: An Android Application for
Dementia Patients, 32nd Annual International
Conference of the IEEE EMBS, Buenos Aires,
Argentina, August 31 -September 4, 2010.
[11] V. Faucounaua, M. Rigueta, G.
Orvoena, A. Lacombeb, V. Riallec, J. Extrac, A.-S.
Rigauda: Electronic tracking system and wandering
in Alzheimer’s disease: A case study, Annals of
Physical and Rehabilitation Medicine ,pp.579–587,
2009.
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