Fall Detection Using Depth Maps Acquired by a Depth Sensing Camera

101 Pages·2016·4.22 MB·English

Fall Detection Using
Depth Maps Acquired by
a Depth Sensing Camera

Jonathan Knorn & Fredrik Lindholm

2016

Master's Thesis

Department of Design Sciences
Lund University

Abstract
In a time when the population and life expectancy increase, the demands on
health care change. The biggest cost in Swedish health care today is related to
accidents regarding falls of old people. This Master’s thesis presents a solution to
fall detection and logging of data from falls. Falls themselves are hard to stop,
but there are several factors behind falls that can be changed in order to prevent
them.
In this Master’s thesis the development of a fall detection system and it’s results
are presented. The system is based on a Microsoft Kinect and a Raspberry Pi 2,
these components are standard, of-the-shelf products with a total price less than
2000 SEK, which is significantly less than the price of the hardware used in the
majority of other projects in this field. Using consumer components opens up the
possibility for others to further develop the system in the future. The developed
solution uses thresholds based on acceleration and height to identify falls. These
parameters have been used alone in earlier studies, by using the unique technique
of combining them gives more accurate results.
The development of the system was divided in to two phases. In the first phase a
data collection was carried out, 200 falls and activities performed by a total of 5
test subjects were logged and the data analyzed. The results were used when
developing the final fall detection software. In the second phase, 75 falls and
activities where performed by two test subjects in order to test the accuracy of
the software. Combining acceleration with height proved to be a good solution,
detecting falls with a sensitivity of 92 percent and a specificity of 96 percent.
Keywords: Fall detection, Smart Home, Home Care, Raspberry Pi,
Kinect
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Sammanfattning
I en tid där befolkningen ökar och medellivslängden blir äldre förändras villkoren
för vården. Den största kostnaden inom svensk vård idag är relaterade till äldre
människor och fallskador. I den här uppsatsen presenteras en framtagen lösning
som detekterar fall och sparar information om vad som händer under ett fall. Ett
fall i sig självt är väldigt svårt att stoppa, men det finns faktorer runt
människorna som faller som kan ändras så att risken för fall minskar.
Den här uppsatsen tar upp utvecklandet av ett falldetektionssystem och dess
testresultat. Systemet är baserat på en Microsoft Kinect och en Raspberry Pi 2,
standardkomponenter med ett totalpris på under 2000 kr. Det är signifikant
lägre än priset på de komponenter som används i majoriteten av andra projekt
inom samma område. Användandet av standardkomponenter gör systemet lätt
att utveckla vidare av andra i framtiden. Den framtagna lösningen använder
tröskelvärden baserade på acceleration och höjd för att identifiera fall. Dessa
parametrar har använts enskilt i tidigare studier, genom att använda den unika
tekniken där de kombineras fås mer exakta resultat.
Utvecklingen av systemet utfördes i två faser. I den första fasen gjordes en
datainsamling. Totalt utfördes 200 fall och vardagsaktiviteter av fem
testpersoner, datan från fallen loggades och analyserades. Resultatet från
datainsamlingen användes som grund för att utveckla det slutgiltiga
falldetektions-programet. I den andra fasen utfördes 75 fall och
vardagsaktiviteter av två testpersoner för att bestämma systemets noggrannhet.
Kombinationen av acceleration och höjd visade sig vara en bra lösning, fall
detekterades med en sensitivitet på 92 procent och en specificitet på 96 procent.
Nyckelord: Falldetektion, Smarta hem, Hemsjukvård, Raspberry Pi,
Kinect
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Acknowledgements
This Master’s thesis was conducted during the autumn of 2015 and spring of
2016. It was conducted at the Faculty of Engineering LTH at Lund University in
the Department of Design Sciences.
We would like to thank our supervisor Jenny Lundberg for her help and advice
during this master thesis.
We would also like to thank all of the test subjects that helped us to test the
implementation. Many thanks to all of you.
Lastly we would like to thank Miss G for always being there, always.
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Contents
I 1
1 Introduction 3
1.1 Structure of this Master’s thesis . . . . . . . . . . . . . . . . . . . . 3
1.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Purpose and Goal . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.4 Hardware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.5 Current Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.6 Focus and Boundaries . . . . . . . . . . . . . . . . . . . . . . . . . 7
2 Method 8
2.1 Investigation phase . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.2 Heuristic Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2.1 Research on other solutions . . . . . . . . . . . . . . . . . . 9
2.2.2 Research on algorithms . . . . . . . . . . . . . . . . . . . . . 9
2.3 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.4 Data collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.5 Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.6 Discussion and Analysis . . . . . . . . . . . . . . . . . . . . . . . . 15
3 Research on how elderly fall 17
3.1 Where and when people fall . . . . . . . . . . . . . . . . . . . . . . 17
3.2 How people fall . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
4 State of the Art 21
4.1 System using audio, camera and accelerometer . . . . . . . . . . . . 21
4.1.1 Camera . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
4.1.2 Accelerometer . . . . . . . . . . . . . . . . . . . . . . . . . . 22
4.1.3 Acoustic signal analysis . . . . . . . . . . . . . . . . . . . . . 23
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4.1.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
4.2 Fall detection using smart phones . . . . . . . . . . . . . . . . . . . 23
4.2.1 Categories . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
4.2.2 Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
4.3 Night Guards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
II 27
5 Selection of Tools and Techniques 29
5.1 Techniques used today . . . . . . . . . . . . . . . . . . . . . . . . . 29
5.1.1 Bounding box technique . . . . . . . . . . . . . . . . . . . . 29
5.1.2 Kinect SDK . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
5.2 Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
5.2.1 Microsoft Kinect . . . . . . . . . . . . . . . . . . . . . . . . 31
5.2.2 Raspberry Pi 2 . . . . . . . . . . . . . . . . . . . . . . . . . 31
5.2.3 Raspbian . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
5.2.4 OpenCV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
5.3 Implementation Approach . . . . . . . . . . . . . . . . . . . . . . . 32
6 Design and Implementation 33
6.1 Depth data linearization . . . . . . . . . . . . . . . . . . . . . . . . 33
6.2 Floor Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
6.2.1 Vertical Histogram . . . . . . . . . . . . . . . . . . . . . . . 36
6.3 Background subtraction . . . . . . . . . . . . . . . . . . . . . . . . 37
6.4 Processing Frames . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
6.4.1 Modifying the mask . . . . . . . . . . . . . . . . . . . . . . . 39
6.4.2 Erosion and mask application . . . . . . . . . . . . . . . . . 39
6.4.3 Contour finding and bounding box calculations . . . . . . . 40
6.5 Fall detection algorithm . . . . . . . . . . . . . . . . . . . . . . . . 41
7 Data Collection and Testing 42
7.1 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
7.1.1 First data collection . . . . . . . . . . . . . . . . . . . . . . 43
7.1.2 Second data collection . . . . . . . . . . . . . . . . . . . . . 45
7.1.3 Third data collection . . . . . . . . . . . . . . . . . . . . . . 47
7.2 Final test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
8 Ethical considerations 53
8.1 Software design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
8.2 User . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54