Thesis - Instituto de TelecomunicaÃ§Ãµes

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84 CHAPTER 4. SIGNAL PROCESSING AND FEATURE EXTRACTIONExperimental results for normalized t 1 and t 2 values are consistently near 1/2, in thecenter of the interval, confirming the validity of the proposed model, that thus provides amathematical formalism to reflect the evenly spaced characteristics of the SCR events.A sounder model for the EDA signal, capable of extracting more precise parameterscan enhance the study of the electrodermal activity. The proposed model provides themeans for a low computational cost method capable of both identifying the SCR eventsand of modelling them, in complex events with overlapping instances. The companionalgorithm of this new model is easily implementable and can be applied to a long lastingEDA acquisition.Some of the listed models present strategies to compute the parameters of overlappingSCR events, but fail to provide a systematic and sound method to detect the events; andin case of events with distinct magnitude, a masked event normally fails to be detected.With the present model we cover the problems of overlapping and smooth events withan easy event detection mechanism.We have considered that the SCR can overlap but not appear inside each other. This iscoherent with the collected data where the duration times are of the same order, producingevents with the following property: the first to start is the first to finish.Concluding, the present work introduces a simple methodology for signal processing thatis able to detect and quantify the following type of EDA events: (a) normal discrete events,(b) overlapping responses in increasing zones, (c) overlapping responses in decreasing zones,and (d) detect small events.4.3 Electrocardiogram4.3.1 Signal ProcessingAs introduced in chapter 3, we used a montage with only two electrodes, called V 2 bipolarsingle lead electrocardiogram, to collect signals from the heart.The signal was filtered by a 4 th order band pass Infinite Impulse Response (IIR) Butterworthfilter between the frequencies of 2Hz and 30Hz. This filter removes EMG activitynoise and 50Hz noise that could be induced by some badly filtered power supply.The algorithm we propose assumes that all the subjects are healthy persons in relation
4.3. ELECTROCARDIOGRAM 8511.111.010.9SC (uS)10.810.710.610.5SC’02 4 6 82 4 6 8SC’’00 2 4 6 8 10Time (s)Figure 4.9: Single event SCR with first and second order derivatives.11.7011.65SC (uS)11.6011.5511.5011.45SC’02 4 6 8SC’’02 4 6 80 2 4 6 8 10Time (s)Figure 4.10: Pair of overlapping SCR’s (the second event overlaps in the decreasing zone ofthe first). First and second order derivatives are presented.
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UNIVERSIDADE TÉCNICA DE LISBOAINST
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iior gave some support to the devel
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ResumoO comportamento humano tem si
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viii2.3.2 Voice . . . . . . . . . .
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x6.2.2 Feature Analysis . . . . . .
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xii4.2 The x-y representation of th
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xiv
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xvi
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xviiiFCT Fundação para a Ciência
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Chapter 1Introduction1.1 The Proble
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1.1. THE PROBLEM 3Figure 1.2: ECG s
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1.2. MOTIVATION 5authentication sit
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1.3. CONTRIBUTIONS 7resource in the
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1.4. THESIS STRUCTURE 9The present
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Chapter 2State of the ArtIn this ch
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2.1. BIOMETRICS CONCEPTS 13to class
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2.1. BIOMETRICS CONCEPTS 15by the s
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2.2. BIOMETRICS TECHNOLOGIES 17Figu
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2.3. BEHAVIORAL BIOMETRICS 19Septem
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2.3. BEHAVIORAL BIOMETRICS 21and co
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2.3. BEHAVIORAL BIOMETRICS 232. ove
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2.3. BEHAVIORAL BIOMETRICS 25Figure
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2.3. BEHAVIORAL BIOMETRICS 27situat
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2.3. BEHAVIORAL BIOMETRICS 29of sig
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2.4. MULTIMODAL BIOMETRICS 312.4 Mu
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Page 59 and 60: Chapter 3Information Sources - HCI
Page 61 and 62: 3.2. HUMAN COMPUTER INTERACTION 37t
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Page 65 and 66: 3.2. HUMAN COMPUTER INTERACTION 41F
Page 67 and 68: 3.3. PHYSIOLOGICAL SIGNALS 43Histor
Page 69 and 70: 3.3. PHYSIOLOGICAL SIGNALS 45Figure
Page 71 and 72: 3.3. PHYSIOLOGICAL SIGNALS 47Term D
Page 73 and 74: 3.3. PHYSIOLOGICAL SIGNALS 49Figure
Page 75 and 76: 3.4. MATERIALS AND METHODS 51Figure
Page 77 and 78: 3.4. MATERIALS AND METHODS 53had th
Page 83 and 84: 3.6. CONCLUSION 59set of computatio
Page 85 and 86: Chapter 4Signal Processing andFeatu
Page 87 and 88: 4.1. HUMAN-COMPUTER INTERACTION 63I
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Page 93 and 94: 4.2. ELECTRODERMAL ACTIVITY 69To ex
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Page 97 and 98: 4.2. ELECTRODERMAL ACTIVITY 73model
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Page 103 and 104: 4.2. ELECTRODERMAL ACTIVITY 79The d
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Page 111 and 112: 4.3. ELECTROCARDIOGRAM 87to the car
Page 113 and 114: 4.3. ELECTROCARDIOGRAM 89analyzing
Page 115 and 116: 4.4. CONCLUSION 918. a T - The ampl
Page 117 and 118: Chapter 5Feature Selection andClass
Page 119 and 120: 5.1. STATISTICAL MODELING 95Weibull
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Page 137 and 138: 5.5. CONCLUSIONS 113of behavioral d
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6.4. CONCLUSION 135time(s) EDA EER
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Chapter 7ConclusionsIn this chapter
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7.2. APPLICATION SCENARIOS 139adequ
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7.3. FUTURE WORK 141the user clicks
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Appendix AToolsThe work underlying
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A.2. SCIENTIFIC COMPUTATION 145side
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Appendix BTestsThe set of tests dev
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B.1. INTELLIGENCE TEST 149Figure B.
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B.2. MEMORY TEST 151# 2 3 3# 2 3 3#
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B.2. MEMORY TEST 153Level. In this
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B.3. ASSOCIATION TEST 155Guide. Ini
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B.5. CONCENTRATION TEST 157Descript
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B.5. CONCENTRATION TEST 159Test Gen
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B.6. TEST’S SEQUENCE 161Orderpage
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B.7. RELATED DOCUMENTS 163Figure B.
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Bibliography[1] A. Acharya, M. Cutt
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BIBLIOGRAPHY 171[18] B. Bimbaum. US
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BIBLIOGRAPHY 173[42] H. D. Critchle
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BIBLIOGRAPHY 175[63] R. Fernandez a
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BIBLIOGRAPHY 177[84] H. Gamboa, A.
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BIBLIOGRAPHY 179[102] S. Hocquet, J
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BIBLIOGRAPHY 181[124] R. M. Kaplan
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BIBLIOGRAPHY 183[145] C. L. Lim, E.
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BIBLIOGRAPHY 185[169] NBSP. Interna
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BIBLIOGRAPHY 187[191] T. Pixley. Do
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BIBLIOGRAPHY 189[214] T. Shen and W
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BIBLIOGRAPHY 191[236] M. Trauring.
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Thesis - Instituto de TelecomunicaÃ§Ãµes

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