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7/31/2019 Ngo Duy Kien _Thesis Version 10_29_Final
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Error! Reference source not found.VIETNAM NATIONAL UNIVERSITY, HA NOI
UNIVERSITY OF ENGINEERING AND TECHNOLOGY--------
Ngo Duy Kien
FALL DETECTION BASED ON
ACCELEROMETER SENSOR
Major: Computer Science
Ha Noi 2012
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VIETNAM NATIONAL UNIVERSITY, HA NOI
UNIVERSITY OF ENGINEERING AND TECHNOLOGY
--------
Ngo Duy Kien
FALL DETECTION BASED ON
ACCELEROMETER SENSOR
Major: Computer Science
Supervisor:Assoc. Prof. Bui The Duy
Co-Supervisor:Dr. Vu Thi Hong Nhan
Ha Noi 2012
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AUTHORSHIP
I hereby declare that the work contained in this thesis is of my own and has not been
previously submitted for a degree or diploma at this or any other higher education institution.
To the best of my knowledge and belief, the thesis contains no materials previously published
or written by another person except where due reference or acknowledgement is made.
Signature:
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SUPERVISORS APPROVAL
I hereby approve that the thesis in its current form is ready for committee examination as a
requirement for the Bachelor of Computer Science degree at the University of Engineering
and Technology.
Signature:
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ACKNOWLEDGEMENT
First of all, I wish to express my respect and my deepest thanks to my advisers
Assoc.Prof. Bui The Duy and Dr. Vu Thi Hong Nhan, University of Engineering and
Technology, Viet Nam National University, Ha Noi for their enthusiastic guidance, warm
encouragement and useful research experiences.
I would like to gratefully thank all the teachers of University of Engineering and
Technology, VNU for their invaluable knowledge which they gave to me during four
academic years.
I would also like to say my special thanks to my friends in K53CA class, University
of Engineering and Technology, VNU, especially Tran Nguyen Le in the Behavior
Recognition group for their helpful discussions.
Last, but not least, my family is really the biggest motivation behind me. My parents
and my brother always encourage me when I have stress and difficulty. I would like to
send them my gratefulness and love.
Ha Noi, May 24th
, 2012Ngo Duy Kien
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FALL DETECTION BASED ON ACCELEROMETER SENSOR
Ngo Duy Kien
Course: QH-2008-I/CQ, Computer Science
Abstract:
The percentage of elderly population is rapidly growing in recent years. Fall-relatedinjuries are a main issue for this population. Falls account for approximately half of all
admissions- related injuries. Falls cause not only broken bones and other health injuries, but also
cause psychological trauma which can reduce the independence and confidence in
communication. Therefore, techniques for detecting and tracking down the movement are
instrumental in dealing with this problem.
Elderly people often want to live at home, therefore, new technologies need to be able to
support automatic fall detection, which guarantee their living independence and security. Thisthesis meets the challenge of different types of motions as part of a system designed to fulfill the
demand for wearable device to collect data for fall and non-fall analysis. In this thesis, we present
a machine learning based approach for this problem which takes the data from accelerometers as
input. First, we have built a database of fall data from real people. We then propose a method to
extract features from raw accelerometer data, which can be used to differentiate between fall and
non-fall actions. Finally, a machine learning based classifier is used to detect fall events. Results
show that falls can be distinguished from non-fall with 94% accuracy, for a total data set of 300
movements.
Keyword: fall detection, accelerometer, activity of daily living
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Table of Contents
List of Figures ........................................................................................................... ixList of Table ............................................................................................................... xChapter 1Introduction ............................................................................................ 1Chapter 2Related Work .......................................................................................... 3
2.1. Fall Detection using 3D Head Trajectory Extracted from a Signal CameraVideo Sequence ...................................................................................................... 42.2. Fall Detection using accelerometer sensor ........................................................ 6
2.2.1 Analytical and Threshold Method ............................................................. 72.2.1.1. Fall detection algorithm using bi-axial gyroscope sensor .................... 72.2.1.2. Fall detection algorithm using tri-axial accelerometer sensor ............ 102.2.1 Learning Methods for fall detection......................................................... 13
2.3. Summary ....................................................................................................... 14Chapter 3Fall Detection Process ........................................................................... 15
3.1. Accelerometer Data collection ....................................................................... 163.2. Algorithm for Fall Detection .......................................................................... 18
3.2.1. Pre-noise processing data ........................................................................ 193.2.2. Action Segmentation ............................................................................... 203.2.3. Feature Extraction ................................................................................... 21
3.2.3.1. Length of the acceleration vector ...................................................... 213.2.3.2. The Standard Deviation .................................................................... 223.2.3.3. The speed of change in acceleration along the z-axis ........................ 233.2.3.4. The acceleration vector changes (AVC) ............................................ 233.2.3.5. The accelerometer inclination angles ................................................ 24
3.2.4. Forming feature vector ............................................................................ 243.3. Action learning .............................................................................................. 25
3.3.1. Support Vector Machine (SVM) .............................................................. 253.3.2. Learning .................................................................................................. 28
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3.4. Summary ....................................................................................................... 28Chapter 4Experimental Setup and evaluation ..................................................... 30
4.1. Experiment Setup........................................................................................... 304.1.1. Background of fall and non-fall action .................................................... 30
4.1.1.1. Study of fall...................................................................................... 314.1.1.2. The simulated-fall study ................................................................... 334.1.1.3. The ADL study (Activity of Daily Living) ........................................ 33
4.1.2. Data Collection ....................................................................................... 354.2. Performance measures ................................................................................... 374.3. Experimental results of the fall detection system ............................................ 38
Chapter 5Conclusion............................................................................................. 40Bibliography ............................................................................................................ 42Appendix A .............................................................................................................. 45
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List of Figures
Figure 2-1: Reading trunk pitches rolocity V v and horizontal velocity Vh. These velocities
were obtained without markers from the 3D trajectory of a person who brutally sits down andfalls. The different action are: the person(a) stands up,(b)sits down, (c) is seated, (d)stands upagain, (e)remains stand up and (f) falls ................................................................................... 5
Figure 2-2: Acceleromter Sensor ............................................................................................ 6
Figure 2-3: Reading trunk pitches and roll gyroscope ............................................................. 8
Figure 2-4: Overlapping and non-overlapping sampe Fall and ADL data................................ 8
Figure 2-5: Bi-axial gyroscope fall-detection algorithm flow chart ......................................... 9
Figure 2-6: Fall detection algorithm operation example for upper and lower fall threshold,using an artificial example signal ......................................................................................... 10
Figure 3-1: The general model cycle .................................................................................... 16
Figure 3-2: Fall annotation for a single fall ........................................................................... 17
Figure 3-3: Fall Detection Flow Chart .................................................................................. 18
Figure 3-4: Low-pas and high-pass filter algorithm ............................................................. 19
Figure 3-5: (a) Sliding window technique. (b) Overlap sliding window technique ................ 20
Figure 3-6: RSS plot of a forward fall .................................................................................. 22
Figure 3-7: Accelerometer inclination angle ......................................................................... 24
Figure 3-8: Separating hyper plane ....................................................................................... 26
Figure 4-1: The stage of fall ................................................................................................. 31
Figure 4-2: The four phases of a fall event ........................................................................... 32
Figure 4-3: Two types of non-fall(ADL) .............................................................................. 34
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List of Table
Table 3.1: Sample data acquired from acceleromter ............................................................. 17 Table 4.1: Events Sequence in the test Scenario ................................................................... 35Table 4.2: The detail of ten young volunteers ....................................................................... 36 Table 4.3: The distribution of the samples ............................................................................ 38Table 4.4: The test result ...................................................................................................... 38
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Chapter 1
Introduction
In the modern society, the percentage of elderly people is increasing rapidly.
The proportion of the population aged more than 65 years old in the developed
countries is projected to increase from 7.5 % in 2009 to 19.6% in 2030 (United
Nations 2009).The ratio of elderly people living alone in the house is quite high. Fall-related injury is a main issue of elderly people, both home environment and hospital
which affects the overall quality of life either at home or hospital. Most of falls occur
when walking, standing or sitting down, or trying to find something.Peopleusuallythink that falling recognition is the most important in our lives. Fall detection has
become a major health concern recently. Falls account for approximately half of all
admissions which are related to injury. Falls cause not only disabling fractures and
other health injuries but also traumas which can reduce the independence and
confidence when communicating. Moreover, the elder people always want to live in a
place which ensures the independence and safety in life. Because of this, new
technologies to guarantee and assist their lives are absolutely necessary. This is a
factor to promote the development of technology.
However, there are many approaches that can be used to construct a fall
detection systems. The first one is based on image and video system and the second
one is based on accelerometer sensor. The later is our concern in this study because
with the rapid advances in wireless network and tiny sensors they can be embedded
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easily in the mobile devices which can be carried anywhere. Moreover, recently,
smart phone applications are being developed faster and faster. Therefore, with a
phone equipped sensors, researchers have developed applications for fall detection
based on accelerometer sensor. These applications are mainly developed based on the
threshold algorithms, some works involve the use of machine learning techniques for
the detection of fall and movement classification. This enables the elders status to be
monitored anytime and anywhere and aided when necessary.
In this thesis, we present our study on human fall detection. We present a
machine learning based approach for this problem which takes the data from
accelerometers as input. First, we have built a database of fall data from real people.
We then propose a method to extract features from raw accelerometer data, which can
be used to differentiate between fall and non-fall actions. Finally, a machine learningbased classifier is used to detect fall events.
The remaining of the thesis is organized as follows: Chapter 2 presents a
detailed overview of related work in the field of fall detection. Some approaches using
different types of sensor are discussed and some higher level comparison of the result
is presented. Chapter 3 describes the processing of the data. This chapter is divided
into several parts. First, the raw data of the sensors is explained. Then the process of
filtering and the process of attribute computation. Chapter 4 we give the evaluation of
the results from different experiments performed in this research. We present about
building a database of fall data and explain the experimental data.
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Chapter 2
Related Work
Accelerometer and gyroscopes are the two most popular methods in most of
the research on fall. Threshold for acceleration, the change of velocity and angles
were usually applied in some researches. By applying a simple threshold to the
acceleration and using a tri-axial accelerometer worn on the chest, Lee, Nguyen, Cho
(2009) detected falls with 98. Bourke and Lyons (2008) usedbiaxial gyroscope worn
on the chest, they applied threshold to the peaks in the angular velocity, angular
acceleration and the change of angle.
In addition to applying threshold for fall detection, machine-learning algorithm
seems to be a good method instead of threshold algorithm algorithm. One example for
using machine learning is Shan (2010) and Yuan and Zhang (2006), both studies
which used tri-axial accelerometer wornon the waist. They used SVM algorithm to
classify various features from accelerometer sensor data.
Especially, Particular interest to us is research of Lie (2009). Although their
result of fall detection is lower than previously research, it may be due to the more
experimental data. They apply threshold, angles and velocities. Based on this,
potential fall and the activity will be detected after fall. Their methods sometimes do
not lie down quickly. And these situations will be addressed by the classify
accelerometer sensor on this thesis.
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2.1. Fall Detection using 3D Head Trajectory Extracted from aSignal Camera Video Sequence
Falls are one of the greatest dangers for elderly people living alone. They may
be unconscious after suffering the fall. Computer vision system provides an automatic
solution to overcome the limitations of researchers. Some research has developed fall
detection system by using the image sensor. One example is the work of Lee and
Mihailidis who detect falls by using a camera mounted on the ceiling determine the
specific silhouette [14]. This is a new method using 3D data of the head trajectory for
monitoring the movement of the fall.
The method of this research will be based on three steps:
Head tracking: because of the fact that the head can be often seen in thepicture and there is a large movement during a fall
3D tracking: the head is monitored with a particle filter to extract a 3D
trajectory
Fall Detecting: a fall is detected by using 3D velocities which are computed
from the 3D trajectory of the head.
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Figure 2-1: Reading trunk pitches rolocity V v and horizontal velocity Vh. These
velocities were obtained without markers from the 3D trajectory of a person who
brutally sits down and falls. The different action are: the person(a) stands
up,(b)sits down, (c) is seated, (d)stands up again, (e)remains stand up and (f) falls
This method required a set of video in various situations such falls and normal
situations like sitting down or squatting. Based on the characteristics of the image
sequences, the result will be showed after analyzing with OpenCV library (Intel Open
Source Computer Vision Library)
The result obtained in this research is quite high. However, in some cases it
depends on the location and specific circumstance. Camera is not always everywhere.
Moreover, using camera usually makes the user feel unnatural and lose freedom in the
daily activities. It affects the privacy of individuals. Consequently, using accelerationsensor for fall detection is an effective approach to ensure personal information can be
kept secure and applied in every situation.
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2.2. Fall Detection using accelerometer sensorAccelerometer usually provides the acceleration readings in direction of x, y, z
axis. Accelerations are these directions are presented by xA , yA , zA respectively. As
illustrated inFigure 2-2
Figure 2-2: Acceleromter Sensor
X-axis has positive direction toward the right side of the device
Y-axis has positive direction toward the top of the device
Z-axis has positive direction toward the front of the device
Noury and Rumeau indicate two approaches to detect fall in their research [4].
The first place, which is the more common approach, is an analytical method and the
second place is with machine learning techniques. An example for machine learning
approach is the research ofMitja and Bostjan [3].This system is a visual basic system.
By using markers, they tagged different point in body of users and used it as reference
points. They found that the angle and the reference points between them is very
reliable data source for extracting features. With other machine-learning algorithm,
Support Vector machine (SVM) was the best choice for research and followed by
Random Forest.
In our research for fall detection is mainly based on the machine learning
techniques and some use analytical models.
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2.2.1 Analytical and Threshold Method
Analytical method is based on the manual or research experience about data
sets and set-up our own parameters (thresholds). There is a need for a thorough study
of falls and ego stage for better understanding and results. What this study focuses onis presented in this section.
With fall detection - principles and methods [4], many falls end lying on the
ground, the simplest approach is to detect the lying position, from a horizontal
inclination sensor. This method is very suitable for monitoring an "isolated a person",
but less suitable for the detection of falls of an elder person such as the irregular
sleeping hours. Therefore this method is prone to many "false positives", i.e. detection
as falls of situations that are not falls.In fall detection system, we can use fall velocity, body orientation, body
posture, angular acceleration, angular velocity and fall acceleration in the critical
phases as the determining factors for distinguishing fall and non-falls activities (ADL
and ADLS).
The simplest approach to detect a fall is by detecting the lying position (GPS).
This is based on the fact that most falls, but eventually not all of them, end up falling
in this lying positing. Both researches of StanKovic, Noury and Fleury indicate thatindirect detection of the lying posture during post-fall can be called [1], [2]. That when
the foot is no longer in contact with the ground also can be another method to detect
fall.
2.2.1.1. Fall detection algorithm using bi-axial gyroscope sensorA threshold-based algorithm to distinguish between Activities of Daily Living
and falls is described. A threshold-based fall detection algorithm using a bi-axial
gyroscope sensor is used[13]. With trunk pitch and roll gyroscope are read during
separate simulatedfall and Activities of Daily Living (ADL) studies, the result will be
compared through a threshold-based algorithm
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.
Figure 2-3: Reading trunk pitches and roll gyroscope
This research focus on studying about the ADL study, Data acquisition set-up,
sensor location and signal conditioning. In signal conditioning, low-pass filter would
be used a second-order low-pass Butterworth 2-pass digital filter, with a cut-offfrequency of 100Hz for each pitch and roll angular velocity signal. The resultant
vector was derived by from taking the root sum of square of roll and pitch angular
velocities.
Fall detection will apply a threshold to the Peak value of lowest fall and highest
ADL from the resultant angular velocity signals. The result of recording data will
result in one of two scenarios:
Figure 2-4: Overlapping and non-overlapping sampe Fall and ADL data
InFigure 2-4(A) the peak values recorded ADL will not overlap with the
recorded fall peak. Falls from ADL which may be distinguished by a
single threshold, level of which would be placed at the lowest fall peak
value.
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In Figure 2-4(B) the peak values recorded ADL will overlap with the
recorded fall values. In this case applying one threshold is not sufficient
to distinguish falls from ADL, so continuing to investigate additional
aspects of the signals is required.
There are three thresholds to determine for a fall which can be distinguished
from an ADL. If the resultant angular velocity is greater than 3.1 rads/s (Fall
threshold 1), the resultant angular acceleration is greater than 0.05 rads/s (Fall
threshold 2) and change the result in changing trunk angle is greater than 0.59 rads
(Fall threshold 3), a fall will be detected. The results indicate that falls can be
distinguished from ADL with 100% accuracy for a total data set of 480 movements.
Figure 2-5: Bi-axial gyroscope fall-detection algorithm flow chart
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2.2.1.2.Fall detection algorithm using tri-axial accelerometer sensorMost previous researchers and others combined the devices total acceleration
from the X, Y and Z axis [10]. This is called Root Sum of Squares or RSS which is
presented by the formal:2 2 2
RSS x y z (2.4)
This is sometimes referred to as the dynamic total acceleration2
9.81 / dRSS RSS m s
Checking the acceleration in early part of the critical phased is the simplest
algorithm for fall detection. When standing upright, we will have a total acceleration
(RSS) of 1G. This value will drop to 0G in free fall until finally reaching impact.2
1 9.81 / g m s
This method will use a threshold which is set based on training session. There
are two thresholds to compare with data from ADLs or other non-fall activities. There
are called Lower Fall Peak (LFP) and Upper Fall Peak (UFP). In training period, LFP
and UFP will give the Lower Fall Threshold (LFT) and Upper Fall Threshold (UFT),
respectively.
Figure 2-6: Fall detection algorithm operation example for upper and lower fall
threshold, using an artificial example signal
Normally, in some case, UFT usually have a better result than LFT.
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Profiling algorithm in [11] is also to measure the falling edge time and rising
edge time. With the falling edge time ( FEt ) is the time when the RSS signal last goes
below the LFT until it reach the UFT. The rising edge time ( REt ) is always a subset
and smaller than the falling edge time ( FEt ).A specify example, we can imagine thatwith t0 the value of RSS first will go below the Lower Fall Threshold (LFT) and keeps
under it until0 2 00ms
t . The time from 0 20 0mst to 0 5 00 mst the RSS value will increase
until the Upper Fall Threshold (UFT) reached. UFT will reach to peak in 0 5 00mst .
0 500 0mst t is the galling edge time ( FEt ) and 0 500 0 200ms mst t is the rising edge time. In
addition we also check for the profile LFT + the falling edge time ( FEt ) and + the rising
edge time ( REt ).Both LFT and UFT are a prerequisite for using the falling edge time
and the rising edge time. However, profiling algorithms only use LFT and UFT can be
further expanded to also use the falling and rising edge time.
There are some algorithms that is similar with profiling algorithm such as in
[12].this algorithm use the time window to find the UFP and LFP and take the
difference between UFP and LFP ( UFP LFPRSS ). If both of them reach to their respective
threshold and LFP happens before the UFP. Fall will be flagged.
UFP LFP UFP LFPRSS t t
with t = time of occurrences (2.5)Improving the result of a fall detection system is extreme important so many
algorithms check the body posture to improve on the specificity. Most of these
algorithms assume with many case of posture such as making a threshold similar to
UFT and LFT when the body is sitting or lying or running. We will compute the
Lower Sitting threshold and the Upper Sitting threshold. After achieving both LFT and
UFT 10 or 15 seconds, if both Lower Sitting threshold and Upper Sitting threshold are
achieved, fall will be flagged.
Another way for falling detection is measuring the velocity which is computed
by the formula.
dRSS dt (2.6)
The velocity will be added in the profiling algorithm and based on the integral
from the time of the start of a fall until impact.
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In addition, we can also use the fall index such as in 2.7 or make fall thresholds.
Especially, we can also make use both in three different types of algorithm.
First algorithm checks posture and impact. With impact, it will be based on Z2,
UFT, dUFT or UFP LFPRSS .The second algorithm uses LFT + UFT (within frame of 1 second) or threshold
based on Z2 + monitoring.
The third algorithm uses LFT + threshold with the velocity and UFT (within
time frame of 1 second) or threshold based on Z2 + monitoring of posture.
Postured will be detected 2 seconds after the impact using the vertical axis
acceleration. The sitting and lying posture are usually lower than 0.4G. The
result of the first algorithm when using Z2 based threshold + posture or UFT isusually 97%. The fall index will be calculated by the formula:
2
1
, , 19
(( ) ( ) )i
i k i k i
k x y z i
FI A A (2.7)
Where x, y, z = acceleration from the X, Y and Z axis
2 2 2RSS RSS G
22G
dZ (2.8)
The scalar product of two acceleration vectors can also measured for Posture to
fin the angle between them. It maybe is between the current gravity vector and the
reference gravity vector or the gravity and the vertical axis (Ay).
arccos|| || || ||
referemcegravity currentgravity
referencgravity x currentgravity (2.9)
arccos|| || || ||
d
y
A gravityA x gravity
(2.10)
Some devices have orientation sensors or more accurate gyroscopes which then
can be used instead [10]. Both formula 2.5 and 2.11 are used.
| sin sin cos cos |x z y y z y z
Orientation A A A (2.11)
Where x , y , z = the data from the orientation sensor
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X is the front-back axis, y is the horizontal axis (left and right) and z is the
vertical axis (up and down).
2.2.1 Learning Methods for fall detectionMachine learning can be broadly classified into two fields, supervised learning
and unsupervised learning. In front, the machine will attempt to identify the groups of
similar data from a larger dataset. Besides, it also tries to from clusters of data based
on some criteria such as cost function. It has not prior knowledge of the data layers. It
only tries to identify natural clusters or groups of data. With supervised learning, it
learns from the test containing classified data and predicts data layers invisible. With
Fall-detection system, it will be more natural when using supervised learning
techniques.
Without any analytical algorithm, we can still carry out an intuitive approach
the development of machine learning based fall detection systems from a training
period and then classification. However, It may be is necessary to establish criteria for
classification.
The most important in supervised learning is the quality of accurate and
exhaustive of training data. Because of these reasons, in the period of training, it is
important to be able to simulate how falls as close and how the real falls for othergroups of users who will use the fall detection system.
With some machine-learning algorithm out there, it may be cause confusing
about selecting the right one for certain applications. Even thought, some research also
studies about comparing the result of them to try to choose the best algorithm [18],[19]
To sum up, a developer can be chosen an application method that is suitable with his
(her) own discretion. An evident with fall detection system is a sensitive application. It
is advised to compare and test between a numbers of algorithms to try picking the bestchoice.
Ralhan in University of Saskachewan compared five different supervised
learning methods for fall detection system [6]. Naive Bayesian classifier, Radial basis
Function (RBF), C4.5 and Ripple down Rule Learner, Support Vector machine are
used. Eight scenarios (four falls, three ADLS and one near fall) are used to getting data
from test the algorithms against. With Nave Bayes classifier, the highest result is 97%
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and taking the least time for building model and then it was chosen as the best choice
for research.
In addition, Fall detection by embedding an accelerometer a method that of
Ralhan in University of Saskachewan used with machine learning algorithm [5]. Theyused the group integrated a device with tri-axial accelerometer, a MCU device and
some other peripherals with a mobile phone. One class SVM for preprocessing the
signals and KFD (Kernel Fisher Discriminant) and K-NN (Nearest neighbor) was used
with fall detection for precise classification. The result of research is 92.3% for a
limited number of cases.
2.3.SummaryStudying and evaluating that system give us the overview about fall detection
approaches. In this few years, researches in this field are developing more and more.
There are many approaches for fall detection such as based on image, video etc.
Besides, the rapid advances in wireless network and tiny sensor promote the
development of smart phones application. However, these approaches seem to only
focus on using threshold of the length of acceleration. Therefore, we propose to
develop a new fall detection system with machine learning based approach for this
problem.
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Chapter 3
Fall Detection Process
In the period of studying for fall detection and the installation of experiment,
we found that the fall detection algorithms based on threshold still have the following
limitations:
They have not been built a specific training data on typical actions falls.
The techniques of experimental process based on the characteristic time
domain and or frequency domain were not mentioned. They have not
been proper interest in these methods.
In addition as mentioned in the previous chapter about the theory of fall
detection. In the data collection, human undesired movement can cause
large changes in intensity dramatically at that moment. It affects directly
on the result obtained when comparing with the threshold.
To solve the above disadvantages, we propose the method to extract feature
combined building layer model for fall detection system. In particular, the model will
be proposed for using extracted techniques based on the information on the time
domain and the characteristic value in accelerometer sensor. The following thesis will
present general model for classification
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Figure 3-1: The general model cycle
The components constitute the layer model using in this thesis. This model isbased on the criteria that the thesis proposed to decrease limitations of the above
methods. Accelerometers data will be collected directly from the user through specific
cases which described. Next part is to expand intensity and assign label for datasets. In
this part, some other information of accelerometer data on the time domain will be
extracted which are used in the classifying phase.
3.1.Accelerometer Data collectionIn this section, we introduce first how the data collected. We collected data of
falls from different directions (x, y, z axes) and different environment (Bedroom,
kitchen, outdoor garden, living room) with other cases of fall. The data from activities
of daily living (ADL) such as walking, jogging, sitting or standing are also be
collected. This below sample data acquired from accelerometer and fall annotation for
a single fall:
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Table 3.1: Sample data acquired from acceleromter
x-axis y-axis z-axis time
0.459687 1.37906 9.80665 57223591098406
0.459687 1.532289 9.346964 57223619296739
0.153229 1.225831 9.95987 572236779434050.459687 1.225831 9.040505 57223741578404
-0.30646 0.459687 10.72602 57223799416739
0.153229 0.459687 9.500193 57223862593408
0.153229 0.153229 9.500193 57223921180075
0.153229 -0.45969 10.5728 57223982330075
0.153229 -0.30646 9.500193 57224039275074
-0.15323 -0.76614 9.193734 57224100655074
-0.1533 -1.53229 9.346964 572241619600720.612916 1.225831 9.959879 57223529871740
0.459687 1.37906 9.8066 57223591098406
0.459687 1.532289 9.346964 57223619296739
0.153229 1.225831 9.959879 57223677943405
Figure 3-2: Fall annotation for a single fall
In getting data for fall detection system, the cell phone was put in the shirt
pocket of participants. In each case of position, every participant falls 7 times in
different directions and environment. To sum up, we recorded 5 examples of the
behavior from 15 persons and collecting Activities of daily living for 2 or 3 minutes
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from each person with some cases such as sitting, running, lying or walking. Mitja and
Igone [17] also gave some multiple activities:
3 x 15 recordings of falling, consisting of standing/ walking, falling and
lying. Fall detection is one of the main goals of our project.3 x 10 recordings of lying down, consisting of standing/walking, lying
down and lying. Lying down is same as falling down, so we wanted to
verify whether the classification can distinguish between the two.
3 x 10 recordings of sitting down, consisting of walking, sitting down
and sitting. Sitting down may also resemble falling and is a common
action .It is important for the analysis of the users behavior.
3 x10 recordings of walking. Walking is also popular and we wanted
clearly examples of it to test the classification. Recognizing word using
trial cutting and beam search
3.2.Algorithm for Fall Detection
Figure 3-3: Fall Detection Flow Chart
Figure 3-3describes the step of data processing and computation of attributes. It
is namely pre-noise processing data, attribute computation and feature selection.Especially, Feature selection is an important prior step to any classification problem
which reduces the dimensionality and thus the amount of data required for training.
These attributes are later combined to create the final attribute vector which is used in
the machine learning classification stage .They will be presented as follows:
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3.2.1.Pre-noise processing dataBecause the data is received from sensor (usually is noisy data), so the data
filter techniques have been applied. There are several techniques that perform signal
filter. In this thesis, we applied a low-pass and high-pass filter.
A simple low-pass filter for the time domain is a smoothing function. In other
work, the signal is smoother and less dependent on short changes. We used low-pass
filter to reduce the influence of sudden change on the accelerometer data.
It is also possible to filter a series such that the low-frequency variations are
reduced and the high-frequency variations unaffected. This type is called a high-pass
filter. This is particularly important in the acceleration data. This type also allows us to
remove the gravity component and take into consideration the sudden change in
acceleration.
The below algorithm shows the low-pass and high-pass filter that we use in this
thesis. It uses a low-value filtering factor to generate a value that uses 80% of
the previously filtered value and 20% of the unfiltered acceleration data. This factor
was chosen empirically.
Figure 3-4: Low-pas and high-pass filter algorithm
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3.2.2.Action SegmentationIn this section, we describe the process of action segmentation. Sliding window
is a common approach to solve the problems of fall recognition. We use an overlap
sliding window technique for fall detection where different actions are performed
continuously. The idea is to move the sliding window across the accelerometer in the
accelerometer sequences and decides what action the actor is performing inside the
window.
2 4{ ,..... }
t w t w t I I I
With:
W is the width of sliding window
t is the index of current acceleration
Figure 3-5: (a) Sliding window technique. (b) Overlap sliding window technique
When analyzing series of time, we chose a window size of 10 which is one
second time interval. We decided for one-second time because some transitional
activities usually last from one to five seconds.
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3.2.3.Feature ExtractionTo detect fall based on accelerometer sensor with machine learning algorithm,
we will use sliding window to transform stream of acceleration data into instances for
machine learning. The following attributes were derived from the data within slidingwindow. We will against on some method to calculation:
Length of the acceleration vector
The Standard Deviation of the acceleration vector within the window
The speed of change in acceleration between the maximum and minimum
along the x, y, z axes.
The maximum and the minimum acceleration along the x, y, z-axes
The acceleration vector changes (AVC)
The accelerometer inclination angles
3.2.3.1. Length of the acceleration vectorThe first computed attribute is length of the acceleration vector. It is a simple
but it is very useful attribute. Moreover, it is also used further in the process ofextraction the new attributes. It is not used as a separate attribute in the final vector
because of the sliding window technique. Its definition is:
2 2 2RSS x y z
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Figure 3-6: RSS plot of a forward fall
During static posture this attribute is constant with the value equal to the
Earchs gravity (RSS = 1g). In dynamic activities the acceleration vector is changing
the direction and its length.
3.2.3.2.The Standard DeviationThe Standard Deviation attribute is useful for distinguish the long lasting static
activities. It can detect when the movement of the sensor its intense. With thesecharacteristics, based on the acceleration value, we compute the Standard Deviation of
the acceleration vector. The Standard Deviation with in sliding window will be defined
as follows:
2
1
( )n
i
ii
a a
STDN
(4.1)
With N is the number of acceleration data within the window, ia is the length
of the thi acceleration vector and a is the average length of the acceleration vector of
the person)
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3.2.3.3.The speed of change in acceleration along the z-axisThe speed of change in acceleration along the z-axis was defined as follows:
max( ) min( )
tan (max( ) (min( ))
z z
zz z
a a
spd t a t a (4.2)
For this attribute, the value of raw materials for the length of the acceleration
vector is used instead of low-pass value .Max (z
a ) and min (z
a ) are the maximum and
minimum acceleration along the z axis within the window, and t (max ( za )) and t (min
( za )) are the time stamp of the data.
3.2.3.4. The acceleration vector changes (AVC)When the person's body is static, single accelerometer response only to the
gravity, producing a constant length of acceleration is 1g. In the moving accelerations
produce a changing in acceleration signal and drastically change the motion. Using
changes in the acceleration vector, an attribute is calculated to detect the motion
accelerometer: Acceleration Vector Changes (AVC). The AVC value of this attribute
increased as the acceleration (walking, going down, stand up, etc ...). This attribute
take into consideration the data from the current window (ten data samples). It
combines ten different length of the vector of length acceleration vector and divided
the sum by the time interval (one second) of data. The AVC is computed as followed:
1
1
0
| |n
i i
i
n
length length
AVCT T
(4.3)
0T is the time stamp for the first data sample in the window, and nT is the time
stamp of the last data sample. The fall can be detected by using this attribute. With this
attribute, the movement of people can be detected: it distinguishes static from the
dynamic active. For this attribute, the value of raw materials for the length of the
acceleration vector is used instead of low-pass value. The reason for this is that we are
more interested in small changes in the acceleration signal and the smooth of low-pass
filter.
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3.2.3.5. The accelerometer inclination anglesOther important features to be recognized as a static body posture are the
orientation angles of the accelerometer. The orientation angles are calculated as the
angles between the actual acceleration and each of the axes (x, y and z axes). (Figure3-7)
Figure 3-7: Accelerometer inclination angle
For instance, the angle x between the acceleration vector and the x axis(perpendicular to the ground) is computed as follows:
2 2 2arccos( )x
x y z
ax
a a a (4.4)
Where the value xa , ya , za represent the actual acceleration vector. In this
attributes, lower-pass filter should be used. Because of the changes of the angle is less
and less variation. Without the low-pass filter, the angles were sensitive angles to each
small change of the accelerometer. These angles improve the classification of activities
that have different accelerometer angle inclinations.
3.2.4.Forming feature vectorIn our work, feature vector is formed for each sliding window (sub-regions) in the fall
data sequence and thus, each sequence is represented by a set of feature vector. The process in
which feature are formed is illustrated in the following:
From each input sample, we use low-pass and high-pass filter as described in
section 3.2.1
Each feature is then divided into 10 non-overlapping sub-regions.
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The value of attributes in section 3.2.3 is calculated for each sub-region.
The feature vector is created by concatenating all attribute computed from sub-
regions ( each sliding window)
3.3.Action learningIn this thesis, we make use of the famous Support Vector Machine (SVM) for action
learning and recognition.
3.3.1.Support Vector Machine (SVM)Support Vector Machine is one of the most famous machine learning techniques
which has been used widely in not only Computer Vision but also in Artificial Intelligent and
other fields. Especially, with classification, SVM is the tool for finding good hyperplanes for
separating different classes of instance.
Considering binary classification problem with L training point { ix , iy } where:
1, 1( ),....,( , ) {1, 1}m mx y x y X
and test set1,.....,
mx x X
With a given training set1 2{( , ) | 1... }, ( , ,... ) , { 1, 1}
n
i i i i i in iD x y i m x x x x R y which is
separable, we need to find a hyper plane which separates two object classes +1, -1 and
predicts bets with data not in the training set. Figureillustrates a hyper plane separating two
types of data objects.
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Figure 3-8: Separating hyper plane
Optimal hyper plane has the equation:
0)(1
m
i
ii bxwbxwxf
Satisfying constraints:
mibxwyi ,...,1,1)(
Then, an objectx is classified into +1 class iff(x) 0 and into1 class otherwise.
The distance from margin to optimal hyper plane is w1
. The problem here is to find w
so that ||w|| has a minimum value satisfying constraints ,1)( bxwyi i= 1 m
with the hope that the larger margin, the better classifier.
This problem can be solved by solving its dual problem. Lagrangian for the
primal problem is:
m
i
iii xwywwbwL
1
]1)([
2
1),,( (3.3.1)
where i 0 is Lagrange multiplier. We can transform the primal into a dual
by simply setting to zero the derivatives of the Lagrangian with respect to the primal
variables, and substituting the relations so obtained back into the Lagrangian, hence
removing the dependence on the primal variables:
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m
i
ii
m
i
iii
yb
bwL
xyww
bwL
1
1
),,(
),,(
Then, we have:
01
1
i
m
i
i
m
i
iii
y
xyw
Replace them into (3.3.1), we have:
m
i
iii xwywwbwL1
]1)([2
1),,(
m
ji
jijiji
m
i
i
m
i
i
m
ji
jijiji
m
ji
jijiji
xxyy
xxyyxxyy
1,1
11,1,
2
1
2
1
The formula above is the dual representation of primal optimization problem. In
optimization theory, solving primal problem is equivalent to solving its dual problem.
Assuming * is the solution of the following dual optimization problem:
Maximizei ji
jijijiiD xxyyL,2
1
where i 0, i = 1m and 01
i
m
i
iy
Then vector of weights ism
i
iii xyw1
** and
Ii
n
j
jijji xxyy
I
b1||
1
whereIis set ofi so that i > 0. The classification function is sgn(f(x)) where:
*)(*
bxxyxfIi
iii
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3.3.2. LearningIn this work, we also apply grouping technique for the learning phase:
Assume training example iX has iN sub-regions which are represented by iN different feature vectors. Each of these feature vectors will be labeled with the corresponding
class of the sample. Therefore, with n samples of two action class (fall and non-fall), a
training set D is described as:
{ , , }iNn
ij ij ij i
i j
D x y y y
With:
ijx is theth
j feature vector of the thi example
ijy is the label for
ijx
iy is the action class of the thi example ( {0,1}iy )
3.4.SummaryIn this section, we presented an overview from data collection to feature extraction.
Pre-processing data is one of the most important parts in the process of feature extraction.
Especially, we also presented overlapping window technique which is usually used in
classifying activity sequences or image series, etc. Based on the overlapping window
techniques, the typical attribute for accelerometer vector are computed and created feature
vector for classification.
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.
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Chapter 4
Experimental Setup and evaluation
This chapter presents the experiments we carry out to build a database of fall
data from real people and analyze the performance of the fall detection method. In this
section, we first describe how we set up our experiments, explain the choice of
parameters for fall detection and finally show the result of our method in comparison
with other related methods.
4.1. Experiment SetupThe following briefly presents the basic concept of fall in our daily activities and
describe the process of data collection which is used in our experiments.
4.1.1.Background of fall and non-fall actionAs mentioned in Chapter 2, we have presented some fall detection system that
uses various approaches in the study. Their disadvantages and advantages were
discussed. There is need for a precision of fall detection system that wishes train and
test. Because of this, studying for falls is an important issue with the thesis.
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4.1.1.1.Study of fallDefinition a fall
Many definitions about a fall are given. This is because fall for one specific
group of users such as young people, elder people or different other groups. This thesis
focuses on distinguishing fall and non-fall activities (it may be called Activity of Daily
Livings or ADLs). Now as mentioned, types of fall of each group of people always are
different (elderly people or childetc).
Figure 4-1: The stage of fall
In our thesis, we will distinguish between two kinds of falls. The first place is
falls which are caused by external factor (such as behavioral or environmental). The
second place is falls that caused by internal factors (such as biology).
Lying down/ Standing/ Walking
Fall Forward/Backward/Left/Right
Near Fall
Cause of falling
The normal changes of aging, like poor vision and poor hearing. They can make
you more likely to fall.Diseases and physical conditions can affect your strength and
balance.
There are some risk factors for cause of falling. In this thesis, we divide these
factors into intrinsic (biology) and extrinsic (behavioral and environmental). We use
both of factors to detect falling.
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Firstly, biological factors may be are:
1. Age2. Medical condition such as Parkinsons disease, patients.3. Muscle Weakness4. Visual impairment.5. Foot problem.Secondly, Behavioral and Environment factors are:
1. Sedentary2. Medication intake.3. Alcohol misuse.
Beside two types of groups for falling, we also research some trends of falls.
They are as follows:
1. Time2.
Climate/weather
3. Location4. Race5. Depression
Phases of falling
There are four phases of falling: the pre-fall, the critical phases, the post-fall phase
and the recovery phase
Figure 4-2: The four phases of a fall event
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The pre-fall phase corresponding movement of daily life, sometimes
with sudden movements directed towards the ground such as sitting
down or bending over. These activities should not create an alarm with a
fall detection system.
The critical phase, corresponding to the fall, is highly short. This phase
can be detected by the motion of the body toward the ground or by the
shock of impact with the floor
The post-fall phase is usually characterized by a real person on the
ground immediately after the fall. It can be detected by a lying position
or by an absence of significative motion
A recovery phase may eventually occur if the person can stand alone or
with the help of another person
4.1.1.2.The simulated-fall studyThe simulated fall study related to healthy young subjects and carrying out
under the supervision. Tri-axial accelerometers signals recorded from the trunk and
thigh in a fall event simulation. Each subject performed eight different kinds of fall
and each type was repeated three times.
The fall types used in the testing process for the current study were selected tosimulated common types of fall in elderly people. The most common causes of falls
are the trips, slips, and loss of balance. ONeill et al.[20]indicated that 60% of falls in
older people were falling in the forward direction. Laterally directed fall also were a
major pose of threat. Laterally directed fall causes the bad impact such as the potential
to fracture every time it happens. Therefore, falls from all directions should be
considered when validating fall detection system. We also should attempt to make
mimicking the realistic falls. Thus, simulation of performance were backward falls,
lateral falls right, lateral fall left and forward falls.
4.1.1.3.The ADL study (Activity of Daily Living)The second study involved older people performing ADL, in their own homes,
while equipped with the same sensor configuration. Ten subjects of the elderly
community, four females and six males, were monitored. They ranged in age from 60
to 80 years old. Each ADL was performed three times by each older person. The ADL
were a task that could produce impacts or sudden changes in movement and result of a
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mistake caused by a threshold-based fall detection algorithm. This below picture
shows activities of daily living:
Figure 4-3: Two types of non-fall(ADL)
The second study involved every activity of living from children to elderly
people. Each ADL was performed three times by each person. To evaluate fall
detection algorithm, Bourke, OBrien and Lyons showed some types of ADL in their
research [7]:
1 Sitting down and standing up from an arm-chair2 Sitting down and standing up from a kitchen-chair.3 Sitting down and standing up from a toilet seat.4 Sitting down and standing up from a low stool5 Getting in and out of a car seat6 Sitting down on and standing up from a bed7 Lying down and standing up from a bed8 Walking 5m9 Cycling
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4.1.2.Data CollectionWith intent and purposes of the system that we want to make. We define a fall
to be a sudden change of body position coming to rest on the ground, it does not
include intentional change, and then is inactivity. This means that the most serious
falls where user loses a balance after hitting the ground. They also lose the ability for
getting help.
To have database to train and test the character recognizer, we designed a
number of this scenario was designed to investigate the events which can be difficult
to recognize such as fall and non-fall. It includes 9 different events which are showed
in Table 4.1. They are recorded in single recordings that it interspersed with shortperiods of walk, each record lasted from 3 to 5 minutes. An example for specific
image of the event can be view inhttp://dis.ijs.si/confidence/iaai.html
Table 4.1: Events Sequence in the test Scenario
No Description Fall Ending position
1 Sitting down normally on the chair No
2 Tripping, landing normally on the
bed
Yes
3 Lying down normally on the bed No
4 Falling slowly( trying to hold onto
furniture),landing flat on the ground
Yes
5 Sitting down quickly on the chair No
6 Falling when trying to stand up,
landing sitting of the ground
Yes
7 Lying down quickly on the bed No
8 Falling slowly when trying to stand
up(trying to hold onto furniture),
landing sitting of the ground
Yes
9 Searching for something on the
ground on all fours any lying
No
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Some types of falls were selected from a list of typical falls. As shown in the
choosing on related work. Accelerometer sensor can accurately detect typical falls.
Because of this, we gave a fall (such as event 2) to prove that the system can recognize
.In addition, we also choose three atypical falls (event 4, 6 and 8) to test the use of
information in each circumstance. A specific example that a person is not expected to
lie or sit on the ground (in contrast with the bed or the chair). They are atypical in
speed (event 4 and 8) and starting/ending posture (event 6 and 8). With each subject,
we would repeat 2 or 3 times for each. Therefore, there were about 300 sequences of
data (120 fall data and 180 ADL data)
The experiments with groups of real persons are conducted. Both activities of daily
living and falls are tested and training. However, the fall situation is dangerous to
human body, especially to the elderly people, so we cannot test falls with elderlypeople, the elderly volunteers only attended with non-fall (ADL). We received data
from 2 groups of subject:
Ten young subjects (6 male and 4 female, age 256 years, body mass
5311.5 kg and height 16610.5 cm)
Table 4.2: The detail of ten young volunteers
Volunteers Age(years)
Weight(kg)
Height(cm)
Subject 1 22 53 169
Subject 2 19 54 168
Subject 3 33 58 164
Subject 4 25 56 165
Subject 5 28 63 174
Subject 6 21 55 170
Subject 7 25 59 166
Subject 8 23 60 171
Subject 9 22 50 167
Subject 10 22 58 165
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Ten elderly subjects and children (6 subjects for age 646 years, body mass
525kg, and height 1645cm,4 subjects for age 104 years old).
4.2.Performance measuresFall detection is either positive if the detector properly recognizes a fall event or
negative if it does not. The quality of fall detection cannot be evaluated from a single
test; instead, it is necessary carry out from a series of test. It will include four possible.
True positive (TP): fall occurs, the algorithm detects the fall
True Negative (TN): a normal movement (non-fall) is performed. Thealgorithm does not detect a fall.
Fall Positive (TP): The algorithm detect a fall, but fall does not occur
Fall Negative (FN): a normal movement (non-fall) is performed. The
algorithm detects a fall.
Based on four situations, we proposed three criteria to evaluate the fall
detection system:
Sensitivity is the capability to detect a fall. It can be expressed:
ensTP+FN
TPS itivity
Specificity is the capability to detect an ADL (Activity of Daily Living). It
can be expressed:
TNSpecificityTN FP
F measure is harmonic mean of the specificity and the precision
2sensitivity specificity
F measureSensitivity specificity
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4.3. Experimental results of the fall detection systemWe tried various machine learning algorithms to train classifiers for classifying
the fall and non-fall. In our experiment, 20 volunteers were selected to attend in the
experiments. The detail of them was mentioned in the previous section. The
experiment was performed with the scenario in Table 4.1.From 300 samples received
from the testing scenario, we conducted experiment with four categories. With these
samples, we randomly selected 3/4 of samples for training and other model for testing.
It was classified with libSVM function in the software of toolkit WEKA. As shown in
Table 4.3 is the detail of four categories.
Table 4.3: The distribution of the samples
Category total samples samples for
training
sample for
test
1 90 66 24
2 70 52 18
3 60 45 15
4 80 60 20
The test results are shown in Table 4.4, and the mean ratio of accuracy mr is 92.2
percent, where:
1
1
Nm i
i
r r
N is the number of categories, ir is ratio of accuracy ofthi category.
Table 4.4: The test result
Category 1 2 3 4
Sensitivity (%) 91.3 94 91.3 92.6
Specificity (%) 91.4 93.9 90.3 92.6
F-measure (%) 91.4 94.0 90.8 92,6
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When using an accelerometer for fall detection, the machine learning based
methods somewhat outperformed threshold algorithm. Considering the simplicity of
these methods, threshold may be simpler than machine learning methods, but the
related work shows that in some case, machine learningbased methods gave the better
result because this may seem surprising, but the related work shows that threshold-
based methods were apparently able to learn the pattern of acceleration during the
falling.
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Chapter 5
Conclusion
Because our goal is to detect fall, most of the non-fall (ADL) we have obtained
samples are more specific than daily activities (such as cooking, sleeping). In other
words, the probability of fall-actions is higher than in everyday of life. Compare with
other methods in [21]. The result of our study is more specific. The correct ratio is
94%.
This thesis studies to build a standard database and typical attributes for fall
events based on accelerometer sensor. We studied the fundamental knowledge about
fall detection such as data feature extraction, machine learning models, support vector
machine and how to combine them.
This thesis has achieved the following results:
Study to build a model of fall detection based on accelerometer sensor
Studying and learning the typical case of falls, the data processing techniques
and the characteristic of the accelerometer sensor are used.
Build a special dataset for testing falls.
About theory and methods, in terms of the thesis, we explore several detection
techniques identify different falls including pre-processing techniques
accelerometer sensor, feature extraction from raw accelerometer data and
classification techniques using in signal classification problems.
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In particular, we proposed a model to improve the accuracy of fall detection
based on accelerometer sensor to ensure objectivity, the proposed model was
compared with the evaluation results of other fall detection algorithms in
previous studies.
In the future, we will continue to improve the recognition quality with a large
enough data for a fully evaluated general methods and experimental models.
Especially, we also try to fix the standard signal from many different users more fully.
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Bibliography
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International Workshop on, 138143
[2] Noury,N.,Fleury,A.,Rumeau,P.,Bourke,A.,Laighin,G.,Rialle,V.,&Lundy,J.2007.Fall detection-principles and methods. In Engineering in Medicine And
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IEEE,16631666.
[3] Mitja, L. &Bostjan, K. Fall detection and recognition with machine learning.Technical report, Joef Stefan Institute, Department of Intelligent Systems, 2009.
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achine_learning-Informatica-09.pdf.
[4] Noury, N., Fleury, A., Rumeau, P., Bourke, A., Laighin, G., Rialle, V., & Lundy, J.2007. Fall detection - principles and methods. In Engineering in Medicine and
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Appendix A
ADL (Non-Fall)
1. From standing position, sit down (normal speed) on chair , remain 5 seconds andthen stand up
2. Kneel down and pick up an item
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3. Standing position, bend-down and pick up an item on the floor. Rise-up
4. Standing position, quickly fall down facing the floor, push up 3-5 times then rise up
5. Lie down and remain 5 seconds. Rotate 180 degree, remain 5 second , then rise up
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6. Standing position, back to original position
7. Standing position and jump
8. Standing position and run from A to B.
9. Lie down on the table
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10.Climb up on the table, remain 5 second and then jump down
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