Literature Review Draft 4

7/31/2019 Literature Review Draft 4

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Hwa Chong Institution

NTU-A*STAR-HCI H3 SCIENCE RESEARCH PROGRAMME

2012

Research Proposal

Title of project: Ambient and Visual Sensor Based Falling Prevention and Detection

System

Name of student: Lee Choon Kiat

CT group: 12S68

Acknowledged by (pls sign)

___________________________ ______________________________

A*STAR mentor HCI teacher-mentor

(Name: ) (Name: )

Date of submission: ________________


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Literature Review + Proposal

1.1 Introduction

The progressive aging of the population, particularly the increase in the number of people living to a

very advanced age, has become a major issue in public health due to the societal challenges that this

demographic change creates. Due to most of the elderly living from health problems and living alone,

they require both support with daily activities and psychological support to feel safe because they are

alone. For the elderly, involuntary falls are frequent, and are likely to cause a loss in quality of life for the

victim. Furthermore, a fall in the elderlys house can be extra dangerous due to the fact that the victim

can easily lose consciousness and thus become unable to ask for help, which could be crucial to their

health if the accident is serious. Thus, in order to avoid this scenario, fall detection systems that are

capable of identifying falls and notifying caregivers when the elderly fall are very much required.

Firstly, a literature review has been conducted to gain an understanding into the background of the

project. With the background knowledge gathered during the literature review, aspects of the problem

that will be addressed in the project were then identified and a research proposal (section 3) was

drafted.

The literature review will be conducted in two portions. The first part of the literature review will be

conducted on the biomechanics behind the falling motion, as well as the reasons why the elderly fall.

The second part of the literature review will focus on existing solutions that possess fall detection

capabilities.


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2.0 The Falling Motion

2.1.1 Fall scenarios what happens during a fall

(Yu,2008) describes 3 of the most important fall scenarios that detail what happens during a fall.

Fall from standing

o Fall from standing is defined here as a transition from the standing state to lying on the

floor

o Process lasts 1-2 seconds, consisting of several sub-actions

1. A person will usually fall in one direction. Thus, this will cause both the head and

the center of mass of the person to move in one plane during falling

2. The head reduces in height from standing height to lying height.

Head will travel in free-fall

3. Head will fall within a circle centered at the foot position of the last standingtime and with a radius equal to the height of the person.

4. Head lies on the floor at the end of the falling process. Head will lie on the floor

with little / no motion for a period of time

Fall from sitting (on a chair)

o Process lasts 1-3 seconds

1. Person is sitting in chair at beginning of fall

2. Head reduces in height from sitting height to lying height.

Head will travel in free-fall

3. Lying body will be near to chair

Fall from sleeping (on bed)o Process lasts 1-3 seconds

1. Person is lying on bed at beginning of fall

2. Body reduces in height from bed height to lying height

Body will travel in free fall

3. Lying body will be on floor near to the bed.

From the above descriptions of a fall, coupled with other definitions of falls found in current literature, I

propose that a general description of a fall can be described as follows:

A fall is defined as an unintentional transition in position of an individual, resulting in him

coming to rest at a lower level

The head will reduce in height from the original height to lying height

o During this period, the head will travel in free-fall

The head will lie in the vicinity of the place of occurrence of the fall


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(Berg et al., 1997) conducted a survey on 96 male and female volunteers between the ages of 60 and 88

that aimed to determine the circumstance and consequences of falls in elderly patients. Causes of falls

were determined from the participants descriptions of their falls. The following graph details their

results.

Figure 1: Causes of falls in elderly (Berg et al. 1997)

From this graph, we can conclude that most of the falls (trip, slip, misplace step, legs giving way,

knocked over, loss of support surface) were falls from standing position, making this kind of fall the mostimportant kind of fall to detect.

However, I theorize that falls from the bed and falls from the chair may be particularly hard to detect by

ambient sensors as they happen over a long time frame and involve a relatively much smaller change in

vertical displacement of the head, thus causing the acceleration of the head to be much smaller than in

a fall from standing position. As ambient sensors typically work by applying algorithms to detect changes

in the vertical acceleration of the head to detect falls, falls from the bed and falls from the chair may be

a source of false negatives for ambient sensors that we could adapt the Kinect to detect. This would

allow a multi-modal system containing both the Kinect and ambient sensors to achieve a much higher

fall detection rate.


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2.1.2 Causes of falls why did a fall occur?

Some research has been conducted into identifying why falls occur, with most employing the same

methodology as (Berg et al.,1997). (Zecevic et al., 2006) conducted an investigation with the aim of

finding the differences between the elderlys perceptions of the causes of their falls and that of the

research community. His results are presented below:

Table 1: The most frequent reasons for falling suggested by seniors

and health care providers compared to risk factors reported in research literature

As can be seen from table 1, certain factors seem to be key indicators of a fall. For example, balance is

listed as an important reason for falling by all three parties, suggesting that many falls are due to a loss

of balance.

By comparing the two sets of causes of falling provided by (Berg et al., 1997) and (Zecevic et al. 2008),

we will be able to pinpoint the more important causes of falling, as well as identify causes of falling that

we may be able to predict using our fall detection system in order to attempt to implement fall

prevention technologies.

Cause of fall identified by Berg

et al. 1997

Matching cause identified by

Zecevic et al. 2008

Ranked importance

Trip Slip, trip, stumble, vision Slip, trip, stumble

Slip Slip, trip, stumble Loss of balance

Misplace step Inattention Inattention

Loss of balance Balance Vision

Legs giving way Muscle weakness Muscle weakness

Knocked over NIL

Loss of support surface Inattention


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Of the five main causes of falls that have been collated, I feel that loss of balance and inattention can be

causes of falls that can be prevented using the Kinect, as it may be possible to derive algorithms that can

predict balance given the center of mass and feet position, which are both obtainable from the Kinect.


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2.2 Fall detection

Fall detection is a relatively developed field, with numerous solutions provided by researchers all around

the globe. However, many problems still exist, with the problem of false readings proving to be the

largest barrier to their widespread adoption. However, other problems such as the lack of a pervasive

solution that is non-invasive exist, and are major limitations that prevent fall detection systems from

emerging from the lab. Most fall detection systems can be classified into three types that are

differentiated by the kind of sensors that are employed.

1. Body-worn sensors

These fall detection sensors utilize body-worn sensors such as accelerometers and

gyroscopes to determine the motion of the human body. Abnormally fast motions are

usually taken to represent falls

2. Ambient sensors

A relatively unemployed solution due to the difficulty in gathering data that is easily

employed in fall detection. This solution utilizes ambient sensors such as ultrasound andinfrared to gather data to determine falls.

3. Cameras

A field that has not been explored too extensively due to the difficulty of employing the

complicated image processing algorithms that require large amount of computing

power. However, this solution has been regularly employed due to the large amounts of

data that it can easily capture.

2.2.1 Body-worn sensors

Many implementations of fall detection systems have been created that utilize body-worn sensors as

their main mode of data collection. These systems generally utilize at least one or more accelerometers

to measure the acceleration of different parts of the human body.

If a large acceleration exceeding a preset acceleration threshold value is obtained, then a fall is indicated.

This method has the advantage of only needing to process relatively small amounts of data, and so the

data processing can sometimes be performed by the microprocessor connected to the sensor itself. This

eliminates the need for bulky wireless transfer modules that can make the device overly bulky and thus

unappealing to potential users.


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Figure 2: Acceleration observed when falling backwards

More advanced systems such as that by (Bourke & Lyons, 2006) combine gyroscopes together with the

accelerometer data. As the gyroscopes can obtain rotational data in 3 or even 6 degrees of freedom, by

combining it with the acceleration data obtained by the accelerometer, the specificity of the solutionthat is created can be improved significantly as posture detection and other data can be calculated more

accurately. However, this addition of data means that the fall detection algorithm that has to be

developed becomes much more complicated in order to fully capitalize on the extra capability provided

by the extra data. Thus, many such solutions utilize wireless data transfer technologies such as

Bluetooth, Zigbee to send the data back to a computer, where the data is then analysed. While this

allows much more complicated and computationally intensive algorithms to be employed to improve

the specificity of the system, it increases the bulkiness and complexity of the system

Figure 3: Fall detection using 2 axis gyroscope and accelerometer

Indicates a fall


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2.2.2 Ambient Sensors

Various ambient sensors have been employed for fall detection, from microphone arrays, pyroelectric IR

sensors (Sixsmith et al., 2005) and ultrasound (Hou et al., 2007) These solutions have the advantage of

being extremely cheap, yet with the ability to be very pervasive while avoiding being invasive. This is due

to the sensors being relatively inexpensive and able to be mounted such that they will not invade the

users personal space. This makes these solutions very attractive for those looking to develop user-

friendly systems. However, these systems generally suffer from much reduced sensitivity and specificity

in fall detection due to the difficulty in gathering useful and accurate data from the limited number of

sensors and the constantly changing background. For example, the pyroelectric IR grid employed by

(Sixsmith et al., 2005) only achieved a 30% fall detection rate, a far cry from the 99% achieved by other

fall detection systems. Thus, I feel that these systems will be able to overcome this weakness by

employing multi-modal solutions that utilize different kinds of sensors working in tandem to provide

adequate useful data to allow for accurate fall detection. This has been employed by (Hou et al., 2007)

and shows promise for further research.

Figure 4: IR and visible images using a 16x16 array based system by (Sixsmith et al. 2005)

2.2.3 Cameras

Video based detection has a rich history in the field of fall detection due to its powerful data acquisition

potential. Furthermore, as it is non-invasive, it also possesses the advantage of ambient sensors while

combining it with the data acquisition prowess of the body worn sensor solutions. However, the

availability of data is a double-edged sword, as it becomes difficult to isolate the user from the

background data. This results in many complicated algorithms having to be applied to the camera datato generate useful conclusions that can be used for fall detection. Thus, many applications do not run in

real time due to the large amount of processing power needed, making the applications not very

feasible in the real world. The sheer amount of data also results in the accuracy of the fall detection

algorithms being compromised if the data is not processed accurately due to extraordinary ambient

conditions (eg: constantly changing background, multiple users).


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For processing of data, many solutions utilize Matlab and (suspected) OpenCV. Matlab is used due to the

easy implementation of the complicated and highly mathematical image processing algorithms while

OpenCV is used due to its powerful inbuilt image processing functions.

Once the background is removed and the user is isolated, there are several general methods that are

used to determine if a person has fallen. The following paragraphs detail some of the methods used:

Aspect Ratio

The aspect ratio of a person is probably the simplest and easiest to implement feature that can help in

fall detection. However, it is quite effective due to the big difference in posture when a person has fallen

or not fallen. In this method, the aspect ratio of the user is first determined from the data. A sudden

increase in the persons width to height ratio is then used to determine if the person has fallen. The

main problem with this method is that it does not work when the person falls and ends up in a lying

position where he is occluded, resulting in unpredictable fall detection when numerous occlusions are

present.

Figure 5: Comparison of aspect ratio between fallen and standing positions

Standing width to

height ratio is small

Fallen width to height

ratio is large


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3D head tracking

Advanced systems are able to perform 3D head tracking, which tracks the motion of the users head in

the video in 3D using multiple cameras. The 3D model obtained is then used for fall detection. For

example, in (Rougier & Meunier, 2007), the 3D head model obtained is used to determine the vertical

and horizontal velocity of the persons head. A fall is then indicated if certain thresholds for these twodata are breached. This system should be quite accurate based on the descriptions of falls obtained

earlier from (Yu, 2008) as it can directly obtain the vertical displacement of the head, which is a key

descriptor in the events of a fall.

Figure 6: Head Tracking by (Rougier & Meunier, 2007)


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Fall angle

The fall angle is another parameter used for determining falls. The fall angle () is the angle that the

centre of mass of the persons body makes with respect to the ground. When there is a sharp decrease

in fall angle from close to 90o

to close to 0o, a fall is indicated. I feel that this method could be quite

accurate and suitable for determining falls due to its widespread use in body-sensor solutions thatpossess >90% fall detection rates. However, it may be difficult to implement this solution without

efficient and proven algorithms due to the difficulty in determining the fall angle.

Figure 7: Fall angle of object in different poses (Vishwakarma et al.)


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3.0 Proposal

3.1 Overview

In this proposal, I will outline a research plan for a fall detection system based on the Microsoft Kinectthat can detect falls automatically.

3.2 Sensor employed

The main sensor that I propose to employ is the Microsoft Kinect, coupled with other ambient sensors

that are linked together to form a multi-modal system. The Kinect is a motion sensing input device by

Microsoft for the Xbox 360 video game console and Windows PCs. The Kinect sensor is a horizontal bar

connected to a small base with a motorized pivot and is designed to be positioned lengthwise above or

below the video display. The device features an RGB camera, depth sensor and multi-array microphone,

which provides full-body 3D motion capture, facial recognition and voice recognition capabilities.

RGB CameraDepth Sensor

Multi-array

Microphone Motorized Tilt


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3.2.1 Capability of the sensor what can the sensor detect?

RGB camera

o Horizontal field of view: 57 degrees

o Vertical field of view: 43 degrees

Depth Sensor

o Quoted by Microsoft 1.2m to 3.5m

o However, preliminary tests seem to show that its minimum range is about one

handspan (about 50-60cm), while some research papers seem to suggest that the depth

sensor can detect objects as far as 10-12m away.

o The resolution of the depth sensor is not constant and varies according to the distance

between the object and the Kinect. The greater the distance between the object and the

Kinect, the coarser the resolution. The resolution of the Kinect at 2m has been found to

be approximately 40mm (Gill et al., 2012)

o Inner workings of the depth sensor

The depth sensor consists of a IR projector and an IR detector. The IR projector

projects a speckled pattern of IR dots, and the IR detector detects the reflected

pattern. This allows the IR detector to determine the depth from the sensor to

the object.

o Issues with the depth sensor

Unreliable depth values

Depth values can be unreliable due to a few reasons

o Low resolution at far distances

With a resolution of 0.1cm at 9-10m, the Kinect may not

have the required sensitivity at long distances

o The IR sensor introduces noise to the edges of objects

The variation in depth values can vary by as much as

130mm at 2m, and increases as the distance between

the object and the sensor increases

Shadows

As the IR sensor requires the pattern projected by the IR projector to

determine the depth map, it is not possible for the IR sensor to estimate

distances outside the line of sight of the sensor. This results in the

shadow seen in the figure below. Due to the relative positions of the

laser and IR sensor, the shadows can only be observed to the left of the

object.


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Figure 9: Sketch of why shadows occur in depth image

3.2.2 Capability of the Microsoft Kinect SDK API what can the API do?

Microsoft, the manufacturer of the Kinect, has developed the Microsoft Kinect SDK, which provides the

tools and APIs, both native and managed, that are needed to develop Kinect-enabled applications for

Microsoft Windows by providing support for the features of the Kinect, including color images, depth

images, audio input, and skeletal data. Some of the key functions that the SDK provides are as follows:

Recognize and track moving people using skeletal tracking.

Determine the distance between an object and the sensor camera using depth data.

Capture audio with noise and echo cancellation or find the location of the audio source.

Enable voice activated applications by programing a grammar for use with a speech recognition

engine.

In this project, the main functionalities of the SDK that will be employed are its ability to capture depth

and colour images from the Kinect sensor as well as the skeleton tracking functions of the SDK.

One of the key advantages of the Microsoft Kinect SDK has over the open-source OpenNI API, the otheralternative API for the Kinect, is that the SDK can perform skeleton tracking without requiring a

calibration pose, allowing more advanced and innovative fall-detection algorithms that require the use

of skeleton tracking to be implemented. As this feature is extremely new (it was first available in the

May 2012 release of the SDK), this will allow our project to pioneer fall-detection algorithms if the time

allows.


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3.3 The Proposal What will we do?

In most camera based fall detection systems, it has been established that the ability of the data to

provide the 3D real world coordinates of the user is extremely important to allow for accurate fall

detection (Rougier et. al, 2011). Currently, 3D real world coordinates are obtained by using either a

multiple camera array system, which requires precise calibration or a Time-of-Flight camera, which can

be extremely expensive to purchase. Thus, I feel that the Kinect sensor can be employed instead due to

its low cost and ease of installation. This will enable the solution to be more user friendly. Furthermore,

the Kinect can obtain a depth image, which is invaluable in precisely tracking the motion of the user

around the room, as well as allowing the privacy of the person to be readily preserved. Furthermore,

due to the use of an IR sensor to obtain the depth image, this solution will be able to work both day and

night in zero-light conditions, which greatly increases the pervasiveness of the solution. In our project, I

propose that a fall detection system based on the Kinect sensor should be developed.

3.3.1 Hardware Gathering of data from the Kinect sensor and its placement

As explained earlier, in order to perform accurate fall detection, it is often necessary to obtain the 3D

real world coordinates of the user from the data gathered from the Kinect sensor. Thus, in order to

obtain this data accurately, I feel that it would be best to employ the depth sensor from the Kinect. By

utilizing the voxel map obtained from the depth sensor, together with ground plane detection

techniques, it should be possible to obtain the 3D real world coordinates of the user. The use of the

depth sensor also has the important advantage of being able to preserve the users privacy in a way that

is difficult to replicate using a RGB camera system, helping to alleviate some of the users potential

concerns.

The Kinect sensor comes with a unique set of limitations not present in other camera based fall

detection system, with the first and foremost being that it cannot detect the depth of objects that are

less than 80-100cm in front of the Kinect sensor. By taking into account this limitation, as well as other

key limitations of the Kinect sensor, I have come up with three rough guidelines that can be applied to

the placement of the Kinect sensor in the room

1. The Kinect sensor should be placed such that it will not be able to obtain the depth image for

the whole room. This is most easily accomplished by placing it in one of the four top corners of

the room, which would provide the Kinect sensor with the biggest field of view possible.

2. The Kinect sensor also has to be placed in such a way that it will be physically

impossible/unlikely for a fall to occur in the blind spot of the sensor that is due to the minimum

range of the Kinects depth sensor.

3. Lastly, due to the shadow effect, it may be wise to place the Kinect sensor to the left of the

room to minimize blind spots caused by the shadow effect. This should help to reduce the

effect of the shadow effect.


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3.3.2 Software

The C++ programming language

C++ is a statically typed, free-form, multi-paradigm, compiled, general-purpose programming language.

It is regarded as an intermediate-level language, as it comprises a combination of both high-level andlow-level language features. Developed by Bjarne Stroustrup starting in 1979 at Bell Labs, it adds object

oriented features, such as classes, and other enhancements to the C programming language.

C++ is one of the most popular programming languages and is implemented on a wide variety of

hardware and operating system platforms mainly due to its high speed and efficiency, as well as the

wide support for C++ available.

Furthermore, as the Microsoft Kinect SDK API that was chosen to interface with the Kinect sensor

requires the use of either C++, C# or visual basic, the choice was made to use C++ as the programming

language of choice for the fall-detection program.

Microsoft Kinect SDK API

Microsoft, the manufacturer of the Kinect, has developed the Microsoft Kinect SDK, which provides the

tools and APIs, both native and managed, that are needed to develop Kinect-enabled applications for

Microsoft Windows by providing support for the features of the Kinect, including color images, depth

images, audio input, and skeletal data.


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Activity Fall / Non-Fall

1 Forward collapse (fall on knees) Fall

2 Forward collapse (end lying down) Fall

3 Backward collapse (fall on knees) Fall

4 Backward collapse (end in sitting position) Fall

5 Backward collapse (end lying down) Fall

6 Sideways collapse (right) Fall

7 Sideways collapse (left) Fall

8 Fall from bed Fall

9 Sitting down on a chair Non-Fall

10 Standing up from a chair Non-Fall

11 Lying down on bed Non-Fall

12 Getting up from bed Non-Fall

13 Jumping Non-Fall

14 Pick up something from the floor Non-Fall

15 Bend forward and tie shoe laces Non-Fall

Table3: Fall and non fall scenarios developed by (Tolkiehn et al., 2011) Table 4: Fall and non fall scenarios adapted from (Tolkiehn et al., 2011)


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An analysis of these above sets of fall scenarios that is similar to many others utilized by other research

projects allows us to generalize the fall scenarios and come up with our own more comprehensive set of

fall scenarios by integrating this table with the previous definitions of a fall provided by (Yu,2008).

(Yu, 2008) provided his definitions of a fall in terms of the starting and ending positions of the head,

while those provided by (Tolkiehn et al., 2011) employs roughly the same methodology. However,(Tolkiehn et al., 2011) further classifies falls by the direction of the fall (sideways, forward collapse,

backward collapse) and gives extra indications as to the ending position (ending lying down, sitting

down). Other fall scenarios developed by other research projects specifically to challenge the

specificity of their system have included falls that end up with the person occluded as well as falls that

occur in the presence of multiple users. Thus, I feel that for our fall scenarios to be comprehensive

enough to cover more types of falls to improve the specificity of our system, we should try to classify the

falls through different parameters and obtain the various fall scenarios by mixing and matching the

different parameters. The implementation of this system is shown below:

A fall is defined by its:

Starting position

o A fall can have either one of these three starting positions

Fall from standing

Fall from sitting

Fall from lying (on bed)

Direction of fall

o A fall is characterized in two dimensions

Y-direction

Forwards

Backwards

X-direction

Rightwards

Leftwards

Variations in the X and Y magnitudes allows the direction of the fall to be

accurately pinpointed

Ending position

o There are two main ending positions

Sitting down

Lying down Unconscious or face flat on floor

Hands supporting head off the floor

Time of fall

o Based on the definitions of a fall provided by Yu, 2008, the time of fall is characterized

into two main portions

Fast fall


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1-1.5s

Slow fall

1.5-3s

Occluded?

o The person may be occluded during different periods of the fall

Start of the fall

During the fall

At the end of the fall

Next, in order to obtain the fall scenarios, it is only necessary to mix and match the different fall

classifications to obtain the different fall scenarios. For example, one fall scenario that could be obtained

is as follows: A fall that occurs when the person is in the standing position, falling backwards, and lands

on the floor sitting down after a short period of time (about 1-1.5s), and is not occluded during any

period of the fall.

This particular fall characterization allows us to cover different kinds of fall comprehensively and thus beable to identify gaps in current research that can be addressed by us. For example, many fall detection

solutions have a lot of trouble with occlusions that block the cameras view of the fall, resulting in

inaccurate fall detection. However, as mentioned earlier, during preliminary testing and

experimentation the fall scenarios shown in Table 4 will be used instead to allow us to focus on the few

fall scenarios that will occur most frequently and thus enable us to improve on the specificity and

sensitivity of our system with the greatest efficiency.

Fall detection algorithm

After identifying the different fall scenarios, different fall detection algorithms that had been employed

by other research projects were then evaluated, and then solutions with potential for use in the project

were selected and modified for use in this project. The algorithms will be developed sequentially and in

a modular fashion so that the sensitivity of the system can be tweaked by the end user in the Graphical

User Interface (GUI) described in the next section.

First filter: aspect ratio change

o The change in aspect ratio of the person from start to end positions of the fall is quite

stark, especially when the person falls from a standing position and ends up in a lying

position. While this solution is a little crude, it provides a relatively accurate way of

determining if a fall has occurred with little computational overhead, and thus I feel that

it can be employed in our fall detection solution to provide a sort of first pass filter. Thiswill allow real time computational overheads to be reduced. In order to improve this

algorithm and provide more accurate fall detection, the aspect ratio of the person could

be calculated in 3 dimensions instead of the current 2 dimensions, providing a more

accurate estimate of a fall. However, this method runs into problems when the user is

occluded at the end of the fall, resulting in other filters having to be applied to

guarantee accurate fall detection.


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Second filter: Vertical velocity of the centre of mass of the user with respect to the ground

o The centre of mass of the user could be identified by modeling each pixel that makes up

the user as a particle of mass 1 unit. This would then allow us to obtain the centre of

mass of the user in real world 3 dimensional coordinates. By obtaining the slope of the

ground plane, we could then calculate the vertical velocity / acceleration of the centre

of mass of the user with respect to the ground, which could be sufficient for us to

conclude that a fall has fallen.

Last filter: Fall angle / angular velocity of the user

o The fall angle of the person could also be calculated. By differentiating the fall angle of

the person against the time taken, we could obtain the angular velocity of the user,

which when coupled with the second filter should enable us to accurately determine if

the user has fallen even if he is occluded at the end of the fall. However, the mainproblem with this solution is that this filter will be quite difficult to implement due to

the difficulty of the coding. This last filter is designated as future work, and will be

implemented only if there is extra time at the end of the project as the previous two

filters should be able to detect the majority of the falls. If the previous two filters are

unable to perform as well as expected, this last filter could also then be implemented to

improve the accuracy of the system even further.

In order for the fall detection algorithms to work, thresholds which will indicate falls will have to be

developed. For example, when the vertical velocity of the user is greater than the threshold value for

vertical velocity, a fall alarm will be triggered. In order to determine the proper threshold values for

each of the fall detection algorithms, experiments will be conducted in which the Kinect sensor is placed

in the target location, and a variety of falls that correspond to the fall scenarios provided in Table 4 will

be recorded. The resulting data will then be analysed to obtain an optimal threshold value.

For each filter implemented, a few threshold values will be collected that correspond to different

scenarios and sensitivity levels. This will allow the end user of the system to tweak the sensitivity

settings of the actual program based on the actual conditions, allowing the system to be more robust

and customizable.

Graphical User Interface (GUI)

In order for the final product to be appealing to the end user, a graphical user interface (GUI) will be

built to give a user-friendly experience. The GUI will mainly provide the end-user with a user-friendly

interface with which to operate the fall detection program, allowing the program to be more appealing

to the end user. The GUI will include:


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A screen which will display the skeletons of the users as well as blurred out depth/color images

from the Kinects depth sensor and camera, which will help to preserve the privacy of the users

while allowing the end-user to evaluate and check the accuracy of the fall-detection program

Customisable sensitivity settings, which will allow the user to adjust the sensitivity of the fall-

detection program, as well as the sensitivity of the fall-detection program to certain specific falls

which might occur much more where the sensor is deployed.

An alarm that will alert the end user when a fall is detected by the fall-detection program

Optional settings that allow the end user to indicate if they want to receive sms or email

updates when falls occur in case they are not able to hear the alarm.

3.4 References

Y. Xinguo, (2008). Approaches and principles of fall detection for elderly and patient,. Proc. 10th IEEE

Int. Conf. e-Health Netw., Appl. Serv., 4247.

Berg, W. P., Alessio, H. M., Mills, E. M., & Tong, C. (1997). Circumstances and consequences of falls in

independent community dwelling older adults.Age and Ageing, 26, 261-268.

Zecevic, A., Salmoni, A. W., Speechley, M., & Vandervoort, A. A. (2006). Defining a Fall and Reasons for

Falling: Comparisons Among the Views of Seniors, Health Care Providers, and the Research Literature.

The Gerontologist, 46(3), 267-276.

Bourke, A., & Lyons, G. (2008). A threshold-based fall-detection algorithm using a bi-axial gyroscope

sensor. Medical Engineering & Physics, 20, 84-90.

A. Sixsmith, N. Johnson and R. Whatmore. (2005) Pyroelectric IR sensor arrays for fall detection in the

older population,J. Phys. IV France 128, 153-160

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