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FALL DETECTION
V I N AY- 1 8 0 7 1 A 0 4 K 0
C H AT U R YA - 1 8 0 7 1 A 0 4 K 3
MON ISH - 1 8 0 7 1A0 4 P7
S W AT H I - 1 9 0 7 5 A 0 4 2 2
R AJ U - 1 8 0 75 A0 44 4
G U I D E - R . R AV I K U M A R
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ABSTRACT
E ld e r ly p e o p le a r e p r o n e t o d if f e r e nt h e a lt h issu e s a s a
p a r t o f a gin g. Th e y a r e su sc e p t ib le t o f a lls t h a t ma y c a u se
se rious in jurie s. At t i me s, t he y a re le ss l ike ly t o be a ble t o
a c q u ir e e me r ge n c y me d ic a l c a r e wit h o u t a ssist a n c e if t h e y
c o lla p se a s a r e su lt o f a p a st he a lt h c o n d it io n o r t h e fa ll
it se lf. The ult ima t e ide a of t his proje c t is t o de ve lop a
we a ra ble de vic e t hat whe n worn c a n ide nt ify t he fa ll of t he
p e r so n a n d se n d a la r ms in o r d e r t o e na b le t ime ly me d ic a l
a ssist a nc e . The ke y c ompone nt of t hi s de vic e wou ld be
M P U6 0 5 0 , w h ic h i s a c o mb in a t io n o f a c c e le r o me t e r a n d
gy r o sc o p e t o me a su r e a c c e le r a t io n a n d a n gu la r o r ie n t a t io n
r e s p e c t i v e l y. T h e s t r e n g t h o f t h i s p r o j e c t l i e s i n a c c u r a c y
of t he de vic e a nd a ddit iona l fe a t ure s l ike st e p c ount w it h
g r a p h i c a l a n a l y s i s o f d a i l y a c t i v i t y, s c o p e t o t e r m i n a t e
f a l s e a l a r m m a n u a l l y, g r a p h i c a l u s e r i n t e r f a c e f o r t h e u s e r
t o int e ra c t wit h t he de vic e .
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LITERATURE
SURVEY
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style
TITLE
AUTHOR
OBSERAVTIONS
A fall detection system tested
with real data from daily life of
elderly people
Panagiotis Kostopoulos ,
Tiago Nunes , Kevin Salvi,
Michel Deriaz and Julien
Torrent.
The paper presents a practical real time fall detection system running on
a smartwatch. The ultimate choice on a fall event is designed depending
on the user's location. This research demonstrates an innovative
algorithm which enhances fall detection accuracy by taking into account
the residual movements and the person's positioning.
Real-Time Signal Processing
of Accelerometer Data for
Wearable Medical Patient
Monitoring Devices
Matt Van Wieringen, J.
Mikael Eklund.
The paper “explains the process of developing a wearable sensor device
that uses an accelerometer for monitoring the movement of the person to
detect falls after they have occurred in order to enable timely medical
assistance. The accelerometer data is analysed through a matching filter
to recognise the occurrence of a fall, and the data is compared to
benchmark analysis data to establish the circumstances that signify the
occurrence of a fall.
Optimal threshold selection for
threshold-based fall detection
algorithms with multiple
features
D. Razum, G. Seketa, J.
Vugrin and I. Lackovic.
The paper offers a basic overview of ways to assist older persons in
detecting falls. When analysed the dataset of accelerations obtained
during simulated falls and ADLs, it shows that algorithms with thresholds
adjusted according to the newly described technique are efficient.
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Fall detection system for elderly
people using IoT and Big Data
Diana Yacchiremaa, Jara Suárez
de Pugaa , Carlos Palaua and
Manuel Estevea.
The paper provides a broad overview of major
public health risk worldwide for the elderly
people. It presents a unique Internet-ofThings based system for detecting elder
people's falls in living areas, which makes use
of low-power wireless sensor networks, smart
devices, big data, and cloud computing.
Experiments have revealed that fall detection
has a high success rate in terms of accuracy,
precision, and gain.
IOT based fall detection system
with energy efficient sensor nodes
Tuan NguyeN Gia , Igor Tcarenko,
Victor K. Sarker, Hannu Tenhunen
An IoT-based system that takes use of
wireless sensor networks and wearable
devices is a feasible solution to this problem.
Sensor nodes based on delicate tailored
devices are more energy efficient than those
based on general purpose devices, even
when the microcontroller and memory
capacity specifications are same.
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AIM,MOTIVATION AND
SCOPE
A i m : To i m p l e m e n t a f a l l d e t e c t i o n m o d e l
that can be integrated on a smart device.
Motivation : As the population is increasing
rapidly everyday and the average mean age
is to the higher number a lot of old age
dependents are at a high risk of fall thus
there arises a need for a system through
which the help is delivered on time.
Scope : Seamlessly integrating on different
platforms and making the model the model
compatible with all the systems.
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RESEARCH GAPS
Finding the right hardware components that
are compatible with any device.
C o s t e f f i c i e n c y.
Accuracy of the obtained information
conversion to the response of the
system(Loss of efficiency with more no of
steps)
Mass production on a large scale.
Ability to detect an unknown situation and
the response to it.
Identifying right or wrong fall.
Implementing the right decision with high
precision.
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OBJECTIVES
To d e v e l o p a h a r d w a r e d e v i c e t h a t c a n
detect a fall.
To u s e t h e r i g h t h a r d w a r e c o m p o n e n t s t o
develop a quick response system.
To o b t a i n a c o m p a t i b l e c o m m u n i c a t i o n
between the hardware components and the
software response system.
To i n t e g r a t e a l l t h e f i n a l f u n c t i o n a l i t i e s o n a
w e a r a b l e d e v i c e w i t h m a xi m u m e f f i c i e n c y.
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REFERENCES
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h t t p s : / / w w w. s c i e n c e d i r e c t . c o m/ s c i e n c e / a r t i c l e / p i i / S 1
877050918304721
h t t p s : / / i e e e x p l o r e . i e e e . o r g / d o c u me n t / 8 4 0 0 2 7 2
h t t p s : / / i e e e x p l o r e . i e e e . o r g / d o c u me n t / 4 6 4 9 6 8 2
h t t p s : / / i e e e x p l o r e . i e e e . o r g / d o c u me n t / 7 4 5 4 5 3 3
ht t ps : / / w w w. f r o nt i e r s i n. o r g/ a r t i c le s / 1 0 . 3 3 8 9 / f r o bt . 2 0
2 0 .0 0 0 7 1 /full
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Thank You
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