저작자표시 비영리 공연 및 방송할 수...

저작자표시-비영리-동일조건변경허락 2.0 대한민국

이용자는 아래의 조건을 따르는 경우에 한하여 자유롭게

l 이 저작물을 복제, 배포, 전송, 전시, 공연 및 방송할 수 있습니다. l 이차적 저작물을 작성할 수 있습니다.

다음과 같은 조건을 따라야 합니다:

l 귀하는, 이 저작물의 재이용이나 배포의 경우, 이 저작물에 적용된 이용허락조건을 명확하게 나타내어야 합니다.

l 저작권자로부터 별도의 허가를 받으면 이러한 조건들은 적용되지 않습니다.

저작권법에 따른 이용자의 권리는 위의 내용에 의하여 영향을 받지 않습니다.

이것은 이용허락규약(Legal Code)을 이해하기 쉽게 요약한 것입니다.

Disclaimer

저작자표시. 귀하는 원저작자를 표시하여야 합니다.

비영리. 귀하는 이 저작물을 영리 목적으로 이용할 수 없습니다.

동일조건변경허락. 귀하가 이 저작물을 개작, 변형 또는 가공했을 경우에는, 이 저작물과 동일한 이용허락조건하에서만 배포할 수 있습니다.

http://creativecommons.org/licenses/by-nc-sa/2.0/kr/legalcodehttp://creativecommons.org/licenses/by-nc-sa/2.0/kr/

Thesis for the Degree of Doctor of Philosophy

Ambient Intelligent System for EmergencyPsychiatric State Inference using Probabilistic

Graphical Models

Md. Golam Rabiul Alam

Department of Computer Science and EngineeringGraduate School

Kyung Hee UniversitySeoul, Korea

February, 2017

Dedicated To

my beloved father and mother for their never-ending support

and unconditional adoration

Abstract

Ambient Intelligence (AmI) is an incredible technology which works sensibly and pro-actively

in the background as a support for better quality life. AmI enabled assisted living can facilitate

optimum health and wellness by aiding physical, mental and social well-being. The proliferation

of the market of AmI technology in psychiatric care services is attracting attention in the health-

care industry; however, a remote mental healthcare system is still unattainable. In this thesis, an

ambient intelligent (AmI) system of in-home psychiatric care service for emergency psychiatry

(EM-psychiatry) is proposed for the remote monitoring of psychiatric emergency patients. The

patients’ psychiatric symptoms are collected through lightweight biosensors and web based psy-

chometric scales in a home environment and then analyzed through statistical machine learning

algorithms to provide ambient intelligence in psychiatric emergency. In this research, the psychi-

atric states are objectively defined as Normal, Atypical and Emergency according to the question

number 9 of Beck Depression Inventory-II (BDI-II). The time sequential biosensors observations

are considered as a sequence of observation and the psychiatric states are considered as the latent

variables to infer from observations. Therefore, the psychiatric states are modeled through prob-

abilistic graphical models to infer emergency psychiatric state from time sequential biosensors

observations.

Firstly, the emergency psychiatric states of patients are modeled as the states of Maximum

Entropy Markov Model (MEMM), in which sensor observations, psychiatric screening scores and

patients’ histories are considered as the observations of MEMM. A modified Viterbi, a machine-

learning algorithm, is used to generate the most probable psychiatric state sequence based on such

observations; then, from the most likely psychiatric state sequence, the emergency psychiatric state

is predicted through the proposed algorithm. The ambient EM-psychiatry model is implemented

i

ii

and the performance of the proposed prediction model is analyzed using the receiver operator char-

acteristics curves which demonstrates that the use of the EM-psychiatric screening questionnaire

with biosensor observations enhances the prediction accuracy.

Secondly, the psychiatric states are modeled through a hidden Markov model (HMM), and

the model parameters are estimated using a Viterbi path counting and scalable stochastic vari-

ational inference (SVI) based training algorithm. The most likely psychiatric state sequence of

corresponding observation sequence is determined and an emergency psychiatric state is predicted

through the proposed algorithm. Moreover, to enable personalized psychiatric emergency care ser-

vice a web of objects based framework is proposed for a smart-home environment. In this frame-

work, the bio-sensor observations and the psychiatric rating scales are objectified and virtualized

in the web space. Then the web of objects of sensor observations and psychiatric rating scores are

used to assess the dweller’s mental health status and to predict an emergency psychiatric state. The

proposed psychiatric state prediction algorithm reported highest 83.03 percent prediction accuracy

in empirical performance study.

Acknowledgement

In the name of Allah, the Beneficent, the Merciful.

”Behold! In the creation of the heavens and the earth; in the alternation of the night and the

day; in the sailing of the ships through the ocean for the benefit of mankind; in the rain which

Allah Sends down from the skies, and the life which He gives therewith to an earth that is dead;

in the beasts of all kinds that He scatters through the earth; in the change of the winds, and the

clouds which they trail like their slaves between the sky and the earth – (Here) indeed are Signs

for a people that are wise.”

(Quran: Surah Al-Baqarah, verse 164.)

Above all, I am ever grateful to Almighty ALLAH, who blessed me with the knowledge,

patience, fitness and courage towards the completion of my Ph.D. I believe he is the one, who

gave me the strength and courage when I was getting tired and lost my hope on this long journey

with many obstacles. Neither the thanks nor my heartfelt gratitude is enough for such blessings.

I would like to thank earnestly to my honorable advisor Prof. Choong Seon Hong for his

support, time and guidance. I am benefited from his advice, experience and wisdom to build up

my research base, solidification, and publication. The encouragement and the motivation of doing

good research, development and publication help me to build myself as a rationally ambitious

philosopher. I am also grateful to him for inviting my family in South Korea and giving me the

support of living and studying.

I would also like thank to the dissertation committee members for their astute comments,

positive criticism and insightful suggestions which surely help to improve the quality of the dis-

sertation.

My especial thanks go toward Prof. Tran Hoang Nguyen for his teaching and guidance

iii

iv

throughout my Ph.D. Thanks to all the networking lab members and alumni for supporting me

in various circumstances which I will never forget.

I would like to extend my acknowledgments to all my friends and colleagues in Korea and

Bangladesh, Dr. Chowdhury Farhan Ahmed, Dr. Zia Uddin, Dr. Mohammad Helal Uddin Ahmed,

Dr. Moshaddique Al Ameen, Dr. Muhammad Hameed Siddiqi, Dr. Rahman Ali, Dr. Kifayatullah

Khan, Dr. Wajahat Ali Khan, Dr. Maqbool Hussain, Dr. Muhammad Afzal, Dr. Cuong The Do, Dr.

Eung Jun Cho, Dr. Vo Thi Luu Phuong, Dr. Dang Ngoc Minh Duc, Dr. Saurav Zaman Khan Sajib,

Dr. Abdul Kadar Muhammad Masum, Dr. Abul Kalam Azad, Dr. Thant Zin Oo, Dr. Le Anh Tuan,

Dr. Kabir Hossain, Dr. Talha Gul, Rossi Kamal, Sarder Fakhrul Abedin, Ashis Talukder, Anupam

Kumar Bairagi, Md. Mostofa Kamal Rasel, Md. Abu Layek, Saeed Ullah, Kyi Thar, Pham Chuan,

Ho Manh Tai, Haw Rim, Seung Il Moon, Bong Yong Kwon, Sung Man Jang, Sung Soo Kim, Jae

Hyeok Son, DaEun Lee, DoHyun Kim, Namho Kim, YoungKi Kim and others for their friendship

and help to overcome the difficulties throughout my thesis research.

I have no words to express my gratitude to my family for their endless support, and especially

my parents Md. Moqbul Hossain and Mst. Saleha Begum; Father-in-law Prof. Iser Uddin, Mother-

in-law Mst. Hosne Zahan, my loving wife Mst. Arafat Zahan, and my siblings Md. Mirza Ashadul

Kibria, Md. Ashraful Kabir, and Ayesha Akhter Laboney, Sister-in-law Mst. Israt Zahan, Brother-

in-law Shahinur Alam, and Hosne Mobarak Rubai, for their love, encouragement, support and

prayers.

Last but not the least, so much love to my prince ”Saad Arafat bin Rabi”, “Jubayer bin Rabi”,

princes “Sarah bint Rabi”, niece “Tazkia-Tabia-Rifat”, “Fatema”, “Ruku” and nephew “Safa”,

“Talha”, and “Usaeed”.

Md. Golam Rabiul Alam

Seoul, Korea

February, 2017

Table of Contents

Abstract i

Acknowledgment iii

Table of Contents v

List of Figures ix

List of Tables xiii

Chapter 1 Introduction 1

1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2 Approaches to Psychiatric State Inference . . . . . . . . . . . . . . . . . . . . . 4

1.2.1 Psychometric approach . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.2.2 Psychopathologycal approach . . . . . . . . . . . . . . . . . . . . . . . 6

1.2.3 Psychophysiologycal approach . . . . . . . . . . . . . . . . . . . . . . . 6

1.3 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

1.4 Challenges in Psychiatric State Prediction . . . . . . . . . . . . . . . . . . . . . 8

1.5 Study Goal and Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

1.6 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

1.7 Structure of the Dissertation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Chapter 2 Literature Review and Related Works 13

2.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.2 Emergency Psychiatry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

v

vi

2.3 Probabilistic Graphical Models . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.3.1 Hidden Markov Models (HMM) . . . . . . . . . . . . . . . . . . . . . . 17

2.3.2 Maximum Entropy Markov models (MEMM) . . . . . . . . . . . . . . . 17

2.4 AmI Enabled Assisted Living . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2.5 Tele-psychiatry and M-Psychiatry . . . . . . . . . . . . . . . . . . . . . . . . . 20

2.6 State-of-the-arts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

2.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Chapter 3 Maximum Entropy Markov Model based Psychiatric State Inference in AmI

Environment 25

3.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.2 AmI System for Emergency Psychiatry . . . . . . . . . . . . . . . . . . . . . . . 27

3.2.1 Body Area Networks (BANs) . . . . . . . . . . . . . . . . . . . . . . . 28

3.2.2 Healthcare Service Brokerage (HSB) . . . . . . . . . . . . . . . . . . . 29

3.2.3 Hospital or Rehabilitation Center . . . . . . . . . . . . . . . . . . . . . 29

3.2.4 Healthcare Cloud Service Provider (HCSP) . . . . . . . . . . . . . . . . 29

3.2.5 EM-Psychiatric Response Management . . . . . . . . . . . . . . . . . . 30

3.3 State Modeling of Emergency Psychiatry . . . . . . . . . . . . . . . . . . . . . 30

3.3.1 Maximum Entropy Markov Model (MEMM) . . . . . . . . . . . . . . . 30

3.3.2 Parameter Estimation and Learning in MEMM . . . . . . . . . . . . . . 32

3.3.3 Viterbi Algorithm for EM-Psychiatric State Sequence Generation . . . . 34

3.3.4 Prediction of Emergency Psychiatric State . . . . . . . . . . . . . . . . 37

3.4 Implementation and Experimental Results . . . . . . . . . . . . . . . . . . . . . 37

3.4.1 Prototype Implementation . . . . . . . . . . . . . . . . . . . . . . . . . 37

3.4.2 Data Collection and Annotation . . . . . . . . . . . . . . . . . . . . . . 40

3.4.3 Preprocessing and Feature Extraction of Biosensor Observations . . . . . 41

3.4.3.1 Dimensionality Reduction through Principal Component Anal-

ysis (PCA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

3.4.3.2 Dimensionality Reduction through Generalized Discriminant

Analysis (GDA) . . . . . . . . . . . . . . . . . . . . . . . . . 45

vii

3.4.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

Chapter 4 Hidden Markov Model based Psychiatric State Inference in AmI Environ-

ment 56

4.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

4.2 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

4.2.1 Web of Objects-Based Smart Home Framework for Ambient Assisted Living 60

4.2.1.1 Device Interface Layer . . . . . . . . . . . . . . . . . . . . . 60

4.2.1.2 Gateway Layer . . . . . . . . . . . . . . . . . . . . . . . . . . 60

4.2.1.3 Object Virtualization Layer . . . . . . . . . . . . . . . . . . . 62

4.2.1.4 Service Layer . . . . . . . . . . . . . . . . . . . . . . . . . . 62

4.2.2 Prediction of Psychiatric State . . . . . . . . . . . . . . . . . . . . . . . 63

4.2.2.1 Emergency Psychiatric State Modeling . . . . . . . . . . . . . 63

4.2.2.2 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . 64

4.2.2.3 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . 66

4.2.2.4 Feature Selection and Dimensionality Reduction . . . . . . . . 67

4.2.2.5 Training and Validation . . . . . . . . . . . . . . . . . . . . . 69

4.2.2.6 Prediction of Emergency Psychiatric State . . . . . . . . . . . 78

4.3 Prototype Implementation and Performance Evaluation . . . . . . . . . . . . . . 81

4.3.1 Prototype Implementation . . . . . . . . . . . . . . . . . . . . . . . . . 82

4.3.2 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 85

4.4 Experimental Study using the Developed AmI System Testbed for PSI . . . . . . 90

4.4.1 Experimental Procedure and Setup . . . . . . . . . . . . . . . . . . . . . 91

4.4.2 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

Chapter 5 Conclusion and Future Directions 98

5.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

5.2 Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

viii

Bibliography 100

Appendix A List of Publications 113

Appendix B List of Abbreviations 119

List of Figures

1.1 Sample Questionnaire of Scale of Suicide Ideation (SSI). . . . . . . . . . . . . . 5

1.2 Question number 9 of Beck Depression Inventory (BDI-II). . . . . . . . . . . . . 9

2.1 Sample Questionnaire of Beck Depression Inventory (BDI) . . . . . . . . . . . . 14

2.2 Sample Questionnaire of Beck Hopelessness Scale (BHS) . . . . . . . . . . . . . 15

2.3 Question Number 3 of the Hamilton Rating Scale for Depression. . . . . . . . . 16

2.4 Dependency graph of HMM . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.5 Dependency graph of MEMM . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2.6 Bio-sensors network to collect psychophysiological observations in an AmI System. 23

3.1 The framework of AmI system of in-home psychiatric care service for emergency

psychiatry. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

3.2 The dependency graph of (a) HMM (b) MEMM. . . . . . . . . . . . . . . . . . 31

3.3 Maximum Entropy Markov Model based psychiatric state model for EM-psychiatry. 32

3.4 The system prototype of the proposed AmI system (a) patients psychophysio-

logical symptoms are collected through body area networks (b)(c)(d) smartphone

based mental health screening questionnaires for ambient intelligent EM-psychiatry. 38

3.5 The preprocessed signals of (a) ECG (b) EDA (c) BVP sensors for feature extraction. 39

3.6 Question number 9 of Beck Depression Inventory (BDI-II). . . . . . . . . . . . . 41

3.7 Plotting of three discriminating principal component (PC) features for three types

of psychiatric state. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

3.8 3D Plotting of GDA on PC features of three types of psychiatric state. . . . . . . 50

ix

x

3.9 (a) The sensor observation increases the area under the ROC curve, which justi-

fies the use of bio-sensors in psychiatric emergencies (b) ROC curve of psychi-

atric emergency state prediction using proposed EM-psychiatry (MEMM), EM-

psychiatry (HMM) and clinical model [58] based m-psychiatry (c) The relation-

ship of error rate with the number of considered features. . . . . . . . . . . . . . 52

3.10 (a) Most likely psychiatric state sequence based on the sensor observations, psy-

chiatric screening scores and patients’ history. (b) Weekly report on emergency

psychiatric state. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

4.1 Question Number 3 of the Hamilton Rating Scale for Depression. . . . . . . . . 58

4.2 Bio-sensors network to collect psychophysiological observations in an AAL

framework. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

4.3 Web of Objects (WoO)-based smart home framework for ambient assisted liv-

ing. Different home appliances and sensors are objectified as web of objects and

virtualized in the virtualization layer. The virtualized web objects are defined as:

TS, Temperature Sensor; LS, Light Sensor; BHS, Beck Hopelessness Scale [35];

FH, Family History; PH, Patient’s History; SM, Smart Meter; SC, Smart Camera;

BPAQ, Buss–Perry Aggression Questionnaire [34]; BIS, Barratt Impulsiveness

Scale [32]; BDHI: Buss–Durkee Hostility Inventory [33]; SSI, Scale of Suicide

Ideation [31]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

4.4 Functional modules of a web of object-based AAL platform especially in a mental

healthcare scenario. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

xi

4.5 The mental healthcare semantic ontology for emergency psychiatry in an AAL

platform. The service layer of the proposed web of object-based AAL framework

uses this semantic ontology to create ambient assisted services, such as emergency

psychiatric state prediction. According to psychiatric state model presented in Fig-

ure 4.3, besides sensor observations, the scores of the psychiatric rating scale,

patient’s personal, medical and family histories are necessary for emergency psy-

chiatric mental state prediction. The ontology presented in this figure shows the

semantic relationship among those objects. The ontology is also used to multicast

messages to patient’s relatives, friends and caregivers in the case of emergency. . 61

4.6 Hidden Markov model-based psychiatric state model for predicting an emergency

psychiatric state in the AAL framework. . . . . . . . . . . . . . . . . . . . . . . 64

4.7 Preprocessed and extracted feature of (a) ECG; (b) EDA; (c) BVP and (d) EMG

sensors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

4.8 Discriminating feature groups based on mRMR. The cumulative mutual infor-

mation of different features of Scale of Suicide Ideation (SSI) [31], Beck De-

pression Inventory (BDI) [9], Electrocardiogram (ECG), Baratt’s Impulsiveness

Scale (BIS) [32], Buss-Durkee Hostility Inventory (BDHI) [33], Buss-Perry Ag-

gression Questionnaire (BPAQ) [34], Electro-Dermal Activity (EDA), Beck Hope-

lessness Scale (BHS) [35], Blood Volume Pulse (BVP), patients’ Medical History

(MH), patients’ Family History (FH), Electromyography (EMG), Personal Traits

(PT), Stress and Coping Self-Test (SCST) [36] and Patient Health Questionnaire

(PHQ) [38] are presented for the feature selection. . . . . . . . . . . . . . . . . . 67

4.9 (a) Plotting of GDA-PC features of three types of psychiatric state class using

HMM as a classifier; and (b) comparison of training times between VPC- and

SVI-based HMM training. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

4.10 Trails of prediction of the psychiatric state from the observation of bio-signals and

supporting features through the Viterbi algorithm [88]. . . . . . . . . . . . . . . 79

xii

4.11 The prototype implementation of AAL framework: (a) Enabled mental healthcare

services through the AAL framework; and (b) Evaluation result of benchmark

Scale of Suicide Ideation (SSI) [31]. . . . . . . . . . . . . . . . . . . . . . . . . 82

4.12 The benchmark questionnaire prototype: (a) Beck Depression Inventory (BDI) [9];

and (b) Beck Hopelessness Scale (BHS) [35]. . . . . . . . . . . . . . . . . . . . 83

4.13 The AAL framework sends patient’s: (a) Biosensor observations; and (b) Returns

an emergency psychiatric state sequence to the smart phone of a concerned psy-

chiatrist. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

4.14 Mental health report generated through the WoO-based prototype AAL frame-

work: (a) Monthly report on stress level; and (b) Weekly report on the prognosis

of emergency psychiatric states. . . . . . . . . . . . . . . . . . . . . . . . . . . 84

4.15 Area under the ROC curve of: (a) Normal, (b) Atypical and (c) Emergency psy-

chiatric states; (d) The grid of sensitivity, specificity, F-measure and accuracy of

different psychiatric states based on the testing dataset [93]. . . . . . . . . . . . . 86

4.16 (a) Receiver Operating Characteristic (ROC) curves for presenting the true posi-

tive rate against the false positive rate of different test settings presented in Table

4.2; (b) ROC curve of psychiatric emergency state prediction using the proposed

emergency psychiatry model (VPC and SVI training based) and the questioner-

based traditional psychiatry model. . . . . . . . . . . . . . . . . . . . . . . . . . 87

4.17 Informed consent of participants’ . . . . . . . . . . . . . . . . . . . . . . . . . . 90

4.18 Registration of participants’ in the AmI system . . . . . . . . . . . . . . . . . . 91

4.19 Registration of participants’ in the Mobile Application . . . . . . . . . . . . . . 91

4.20 Participant’s are completing Psychometric Questionnaire . . . . . . . . . . . . . 92

4.21 BHS evaluation report of one of the Participants’. . . . . . . . . . . . . . . . . . 92

4.22 Psycho-physiological data collection of the Participants’. . . . . . . . . . . . . . 93

4.23 Observed psycho-physiological signals of a participant. . . . . . . . . . . . . . . 93

4.24 One of the participants answer of question number 9 of BDI-II. . . . . . . . . . . 94

4.25 ROC of ‘Normal’ state inference by HMM classifier using testbed dataset [117]. . 96

4.26 ROC of ‘Atypical’ state inference by HMM classifier using testbed dataset [117]. 96

List of Tables

1.1 Suicidal and Homicidal Deaths in the USA [17–22] . . . . . . . . . . . . . . . . 3

1.2 Homicide-Suicide Events and Deaths in the USA [23–27] . . . . . . . . . . . . 3

3.1 Confusion Matrix of EM-Psychiatry using Dataset [77] (Unit:%) . . . . . . . . . . 49

3.2 Test Settings of EM-Psychiatry for Performance Evaluation . . . . . . . . . . . . 51

3.3 Test Settings for Comparative Study . . . . . . . . . . . . . . . . . . . . . . . . 52

4.1 Illustration of the algorithm with an example. . . . . . . . . . . . . . . . . . . . 80

4.2 Different test settings with performance evaluation. . . . . . . . . . . . . . . . . 86

4.3 Type I and Type II error rate, DOR and CI of the predicted psychiatric states. . . 89

4.4 Performance study results of AmI testbed for in-home psychiatric care. . . . . . . 95

4.5 Confusion matrix of experimental results using testbed dataset [93] . . . . . . . 95

xiii

Chapter 1Introduction

Background

Ambient intelligence (AmI) is the vision that technology will become invisible, embedded in our

natural surroundings, present whenever we need it, enabled by simple and effortless interactions,

attuned to all our senses, adaptive to users and context-sensitive, and autonomous [1]. Ambient

healthcare is one of the supportive technology where the AmI system accompanying the users as

a caring expert to lead healthy quality life and well-being.

The proliferation of wearable technology has elevated wearable electronics to one of the fastest

growing categories in consumer electronics. Most wearable electronic products are used in fitness

tracking and consumer well-being at home and in mobile environments. Incorporating “smartness”

in the home environment ensuring security, comfort and healthcare [2], which are the primary

goals of the AmI enabled assisted living technology. Winkley et al. [3] have proposed an ambient

assisted living platform for passive care and elderly monitoring. There are also home healthcare

research based on a 3-axis accelerometer sensor, cardio-tachometer [4], smart phone and wrist-

worn sensors [5]. A number of activity recognition [6] and fall detection [2,4] methods have been

proposed for monitoring physical health in ambient environment. Both physical and mental health

are necessary for achieving optimum wellness. Yet mental and cognitive health [7, 8] status are

not explored in such an extent for ambient assisted living. Therefore, this research proposes an

AmI system for in-home psychiatric care. Users’ or patients’ major psychiatric symptoms are

monitored through lightweight bio-sensors and web based psychiatric rating scales (e.g., Beck

Depression Inventory (BDI)) [9]. The bio-sensor observations and psychiatric rating scores are

used to assess the mental health status and thus to infer an emergency psychiatric state.

The healthcare industries are applying cutting edge technologies and methods to enhance oper-

1

CHAPTER 1. INTRODUCTION 2

ations and processes to ensure and manage efficient healthcare delivery [10]. The next generation

of ambient intelligent (AmI) technology sustains the development of an improved healthcare de-

livery system for the healthcare industry. Several AmI systems in healthcare are presented in [11],

where the authors reviewed the existing state-of-the-art infrastructures for AmI. In addition, the

intelligent medical box [12] and compressed sensing-based biomedical signal acquisition [13]

techniques are also some of the recent advancements in ambient technologies for assisted living.

Although, industrialization offers cost effective, high quantity, and affordable utilities, the delivery

of health care in the process of “industrialization” must hold a promise of more efficient and effec-

tive services [14]. Therefore, this research has attained a vast scope of the industrialization of AmI

based mental healthcare services through developing a knowledge-based, personalized psychiatric

care service.

1.1 Motivation

In 14th December 2012 a heavily armed man walked into a Sandy Hook elementary school, USA

and within a minute, he killed 26 people and out of them 20 are children [15]. The killer was 20

years old mentally sick Adam Lanza, also killed his mom and later he committed suicide. These

types of occurrences are not new and no one can say; that occurrence was the last one. This work

is a tiny step towards addressing such type of patient’s in a different way.

Emergency psychiatry (EM-psychiatry) deals with patients of acute disturbance in behavior,

thought and mood, who are also subject to potential imminent danger to themselves (suicide) or a

danger to others (homicide) [16]. According to the official record of the American Association of

Suicidology and CDC the suicidal and homicidal deaths and death rates are shown in the Table 1.1

[17–22]. Homicides followed by suicidal deaths are not officially recorded by American Associ-

ation of Suicidology. According to the report of the American Roulette, the ‘homicide-suicide’

deaths of the United States are shown in Table 1.2 [23–27]. The suicidal death-rates of USA in

2013 is 13% which is highest over the past several years [22]. Also, the suicide rate of the Republic

of Korea is the highest among 34 OECD countries which is 33.30 deaths per 100,000 people in

2011 [28]. Preventive and proactive measures are necessary to inhibit such tragic suicidal deaths.

Recognizing warning signs that are proximal to an impending suicidal crisis is the first step of


developing a safety plan to reduce suicide risk. Quantitative and objective ways for predicting and

tracking suicidal mental states is a challenge for emergency psychiatry, and can play a vital role in

suicide prevention.

Table 1.1: Suicidal and Homicidal Deaths in the USA [17–22]

Year Suicidal Deaths Suicide Rate Homicidal Deaths Homicide Rate2013 41149 13.0 16121 5.12012 40600 12.9 16688 5.42011 39518 12.7 15953 5.12010 38364 12.4 16259 5.262009 36909 12.0 16709 5.52008 38035 11.8 17826 5.9

Table 1.2: Homicide-Suicide Events and Deaths in the USA [23–27]

Year (Jan-Jun) Homicide-Suicide Deaths Homicide Victims Perpetrators2014 617 332 2852011 691 378 3132007 554 320 2342005 591 327 2642001 662 369 293

The emergency departments of hospitals and rehabilitation centers are overburdened [16].

They also have a scarcity of mental health professionals for inpatient and outpatient services.

Due to the lacking’s of sufficient resources, the emergency psychiatric care adversely affects the

safety measures of patients and staffs. AmI system for emergency psychiatric care can be the com-

plementary treatment model to enhance the service quality of the patient-care system. However,

the indicators of table 1.1 and 1.2 shows the urgency of developing an AmI system to assess,

monitor and infer emergency states of patients having psychiatric crisis.

The Psychiatric emergency covers a broader range of mental illness i.e. suicide efforts, drug

and alcohol intoxication, homicide and violent behaviors etc. Whereas, we mostly distillate on

suicide behavioral emergency. The higher severity levels of stress, depression, hopelessness, ag-

gressiveness, and anxiety are the most influential features of psychiatric emergency. As human

psychology is diversified based on individual’s culture race and region, only the psychiatric rating

scores are not sufficient to assess the psychiatric emergency risk. The proposed AmI system, ana-


lyzes patients’ real-time biosensor observations, family histories, medical and treatment histories,

and traits with psychiatric rating scores to infer the risk of psychiatric emergency.

1.2 Approaches to Psychiatric State Inference

Mental health monitoring is challenging because human mentality varies dynamically and it is

difficult to fetch the patterns from the behavior. Also, human behavior differs over so many met-

rics like age, ethnicity, education, marital status, family history and habits etc. However, human

brain chemistry changes over different mental disorders but still causes and effects are not fully

explored. And pinpointing the location of mental disorder in our brain is very difficult (almost

impossible) as our brain consists of about 100 billion neurons and trillion of glial cells, and the

neurons form a telecommunications network in the brain to communicate each other and also carry

the signals back and forth between your brain and the rest of your body [29]. As a result, it is not

possible to diagnose most of mental diseases like bipolar disorder using some definite patholog-

ical examination. Three approaches have been mainly employed for Psychiatric State Inference

and those are 1) Psychometric 2) Psychophysiology and 3) Pathology.

1.2.1 Psychometric approach

The psychometric approach is the traditional way of assessing and monitoring psychiatric patients.

Till now this approach is well accepted and has strong statistical base to asses and monitor mental

disordered patients. The Diagnostic and Statistical Manual of Mental Disorders (DSM) [30] is con-

sidered as the bible of psychometric approach. The researchers of clinical psychology and psychi-

atry developed several questionnires e.g., Scale of Suicide Ideation (SSI) [31](please see Fig. 1.1),

Beck Depression Inventory (BDI) [9], Baratt’s Impulsiveness Scale (BIS) [32], Buss-Durkee Hos-

tility Inventory (BDHI) [33], Buss-Perry Aggression Questionnaire (BPAQ) [34], Beck Hopeless-

ness Scale (BHS) [35], Stress and Coping Self-Test (SCST) [36], Hamilton Rating Scale for De-

pression (HRSD) [37] and Patient Health Questionnaire (PHQ) [38] for quantitative measurement

of different psychiatric stressors.


Figure 1.1: Sample Questionnaire of Scale of Suicide Ideation (SSI).


1.2.2 Psychopathologycal approach

Pathological approach is a recent addition in psychiatry [39]. In last decades, psychiatrist don’t

even think that pathological test can determine stress, depression, anxiety or other mental disor-

ders. The development of nuroscience figure out the neurotransmitters e.g.,serotonin, dopamine

and adrenalin, which are responsible for different psychiatric stressors [40]. The blood, serum and

urine samples [41] are used to find out the imbalances in nervous, immune and hormone system

by testing the neurotransmitter levels. Moreover, H. L. Niculescu et al. [42] successfully classified

the blood biomarkers to recognize suicidality in the patients with mood disorder. Therein, the au-

thors used convergence functional genomics to classify the genes responsible for suicidality and

identified the spermidine/spermine N1-acetyltransferase 1 (SAT1) as the most influential blood

biomarker among other blood biomarkers.

1.2.3 Psychophysiologycal approach

In 2006, John-Dylan Haynes & Geraint Rees published their research in Nature Reviews Neu-

roscience, where they revealed the possibility to decode human’s mental state such as conscious

experience and covert attitude by assessing non-invasive measurement of brain activity [43]. Psy-

chophysiology deals with the physiological bases of mental disorders [44]. Psychophysiophysi-

ological approch dteremines the microcosmos relating to human behavior and psychology. The

biofeedbacks from the Functional Magnetic Resonance Imaging (fMRI), Electroencephalography

(EEG), Positron Emission Tomography (PET), Electrocardiography (ECG), Electromyography

(EMG), Blood Volume Pulse (BVP) and Electrooculography (EOG) etc. are used to assess mental

disorders i.e. Attention-Deficit Hyperactivity Disorder (ADHD), Bipolar-disorder, Anxiety, and

Stress etc. In this research, we proposed a psychophysiological approach of infering emergency

psychiatric state where we used four kinds of biosensors i.e., electro-dermal activity sensor (EDA),

electrocardiogram sensor (ECG), blood volume pulse sensor (BVP), and surface electromyogra-

phy sensor (EMG).


1.3 Problem Statement

The major problem in the traditional psychometric approach of emergency psychiatric state in-

ference is it’s off-line nature. A questionnaire-based (i.e. psychometric) clinical model is fully

depends on the subjective (e.g., Beck depression inventory, BDI-II) and objective (e.g., Hamilton

Depression Rating Scale, HDRS) psychiatric rating scales. However, the psychiatric emergency

(e.g., homicidal and/or suicidal) state can even be developed within a moment and may persist

for a very short (in some cases a long) duration. But, the questionnaire-based psychiatric state

measurement methods cannot measure the state of emergency psychiatric patients continuously.

Therefore, it is infeasible or sometimes impossible to get psychiatric rating scores from patients

for continuous monitoring of psychiatric state. A continuous assessment through neuroimaging

or wearable biosensor may be an alternative option. Furthermore, the questionnaire based model

has some impediments e.g. patient’s may hide information, provide fallacious information, be un-

able to remember information, feel cautious to provide accurate information, misapprehend the

question, misjudge the significance of the answer etc.

However, psychopathologycal approach requires extensive clinical setup to measure psychi-

atric state of patients. Moreover, the problem of blood biomarker based suicidality prediction is its

invasiveness, i.e. need the blood to investigate the suicidality biomarkers. However, the suicidal

genome classification is only possible in an intensive clinical and pathological setup environment.

Thus, it is infeasible to adopt the technique in ambient intelligent (AmI) system for continuous

monitoring of psychiatric patients or elderly people in the home environment.

Therefore, a continuous assessment through neuroimaging or wearable biosensor may be an

alternative option, whereas there is no individual channel (including EEG, fMRI and PET) of

neuroimaging and biosensor so far, which can pinpoint the psychiatric emergency. Thus the alter-

native option is to measure the potential triggers or stressors (e.g. depression, stress, frustration,

sleep apnea, drug-induced arrhythmia) of psychiatric emergency through multiple neuroimaging

and/or biosensor channels to predict emergency psychiatric state. The channels of EDA, ECG,

EMG and BVP are used to extract features, which are the markers of psychiatric risk factors for

the emergency psychiatric mental state, e.g. skin conductance level can demonstrate the stress

level [45, 46], the R-R and QTc intervals can demonstrate the depression level [47–51], and the


variation of peak-to-peak interval and the pinch of the BVP can indicate the frustration level [37].

However, the features of any of the single channels are not adequate to decode the major risk fac-

tors of a psychiatric emergency. The EEG bio-signals can also be used along with other biosensors,

e.g. EDA and BVP likewise the previous work [52], but the poor spatial resolution of EEG [53]

and sophistication in analyzing data make it mostly suitable for clinical or laboratory environment

rather than an ambient assisted living environment. Furthermore, unlike a fully functional 32 elec-

trode EEG sensor, the EDA, ECG, EMG and BVP sensors are wearable as a wrist or chest band,

which are suitable for ubiquitous patient monitoring.

1.4 Challenges in Psychiatric State Prediction

The main challenge of this research is to decode the psychiatric states from biosensor observations.

Currently, there is no neuroimaging and biosensor individual channel, which can pinpoint the

psychiatric emergency. Thus, in this research, the potential triggers or stressors (e.g., depression,

stress, frustration level) of the psychiatric emergency are measured through multiple biosensor

channels to infer the state of the psychiatric emergency.

Moreover, monitoring the rapid behavior changes of psychiatric emergency patients is an-

other key challenge of this research because of the instability of potential triggers of psychi-

atric emergency patients. To tackle these research challenges, the emergency psychiatric states

are modeled, realized and formulated through probabilistic graphical models e.g., the discrimina-

tive Maximum entropy Markov model (MEMM) [54] and the generative hidden Markov model

(HMM) [55], which are suitable for modeling sequential observations and states. Afterwards, the

modified Viterbi [54] is applied to predict the state sequence from the continuous and sequential

bio-sensor observations.

1.5 Study Goal and Methodology

The objective of this study is to propose an AmI framework of personalized and in-home psychi-

atric care service for emergency psychiatry (EM-psychiatry) where non-invasive biosensor (e.g.,

ECG, EDA, EMG and BVP) observations are used with a patient’s personal, medical and family


Figure 1.2: Question number 9 of Beck Depression Inventory (BDI-II).

histories to predict emergency psychiatric state. Therefore, the psychiatric states are defined objec-

tively as Normal, Atypical, and Emergency, based on question number 9 of the Beck Depression

Inventory-II (BDI-II) [9] as presented in Fig. 1.2.

The biosensor signals are firstly preprocessed through denoising and smoothing methods. The

features are extracted from the preprocessed signals and then quantized for further processing. The

features from biosensors are combined with the observations of the patients’ psychiatric screening

scales, and their traits and histories to form a complete feature set for classifying the psychiatric

state. The observed psychiatric rating scores were quantified based on the benchmark psychi-

atric scales. Furthermore, the feature values are normalized through the max-min normalization

method. However, the minimal-redundancy-maximal-relevance (mRMR) [23] feature selection

method was applied to select a compact discriminating feature set based on statistical dependency

of features on emergency, atypical and normal psychiatric states.

Subsequently, the Principal Component Analysis (PCA) was performed for further dimension

reduction in the transformed domain. Therefore, the individual contribution of signal features for

psychiatric state prediction become indistinctive because of the transformed feature domain. In

fact, PCA is a prominent subspace projection method that is widely used for reducing the di-

mension space from a higher dimension as well as for maintaining the high order relationship.

Afterwards, the Generalized Discriminant Analysis (GDA) was applied on the Principal Com-

ponent (PC) feature vectors for concentrating more closely the features of the same psychiatric


state classes. Then, the relevant feature set is used to train the MEMM and the HMM probabilis-

tic graphical models (PGM) for parameter estimation. The trained PGM acts as the classifier of

psychiatric states. As in the proposed AmI system, the patient’s biosensor produces continuous

observations, the most likely psychiatric state sequence is generated through the modified Viterbi

algorithm based on the trained PGM. Finally, the emergency psychiatric state is predicted through

the proposed psychiatric state prediction algorithm. To the best of our knowledge, we are the

first in utilizing multi-sensor observations with psychiatric assessment scale scores to monitor and

predict a psychiatric emergency in the AmI environment towards the standardization of in-home

psychiatric care service for emergency psychiatry.

1.6 Contributions

The major contributions of this thesis comprise three main components: AmI enabled assisted

living framework, EM-psychiatric state modeling and inference, and m-psychiatry system.

• Emergency psychiatric state inference: Decoding the psychiatric state of a patient through

indirect channels of non-invasive biosensors is challenging. The discriminative features of

putative risk factors for emergency psychiatric states are extracted from biosensor obser-

vations. For structured prediction, the emergency psychiatric states are modeled through

probabilistic graphical models (i.e., MEMM and HMM). The psychophysiological features

along with the psychometric features and patient histories are used as the observations to

predict psychiatric state. Additionally, an emergency psychiatric state prediction algorithm

is proposed for inferring the risk of psychiatric emergency. The probabilistic model param-

eters are estimated, and the accuracy of the model is validated over a training and testing

dataset. The prototype is developed and tested.

• AmI enabled assisted living framework: A web of objects-based AAL framework is pro-

posed for in-home personalized psychiatric care. In order to predict and monitor an emer-

gency psychiatric state, collaboration among the objects is required. Therefore, a mental

healthcare ontology is developed for presenting and extracting semantic relationships among

the web of objects in a psychiatric care scenario. The framework enables a platform to coop-


erate, harmonize and share the mental healthcare objects for ambient assisted living services

(e.g., emergency psychiatry).

• The m-psychiatry system alters the traditional paper-based manual psychiatric state recog-

nition technique to mobile application based mental healthcare system to support the in-

dustrial development of ambient assisted living (AAL) technologies and services for mental

healthcare.

The proposed AmI framework supports the industrialization of psychiatric care services

through fragmenting the patient care environment at home and hospital environment. In addition

to ambient intelligence [56], it incorporates psychiatrist, relatives, and emergency care department

in the loop of psychiatric care. The EM-psychiatric state modeling and classification system tran-

sit the psychiatric care service delivery systems towards more knowledge-based semi-autonomic

industrialization.

The findings of this thesis address the scopes of industrial usage of the proposed model in the

following ways.

1) The delivery of healthcare in the process of “industrialization” as in the means of organi-

zation of work; division of work, standardization of work, and degradation of work [14]. Since,

patients with mental disorder have distinct cases; a personalized treatment system is the absolute

solution through healthcare industrialization rather establishing a common production line as it

stands in product industrialization.

The proposed psychiatric emergency state recognition model, thus, can meet the personalized

requirement of psychiatric emergency and provides one-to-one psychiatric care service as a part

of organization of work. However, modeling the emergency psychiatric states through MEMM

and predicting the continuous state from psychophysiological and psychometric observations is a

significant contribution towards the standardization of in-home psychiatric care service for emer-

gency psychiatry.

2) The proposed ambient intelligent system can successfully replace the traditional paper based

psychiatric emergency state recognition technique. Thus, a readymade and efficient mental health

care service can be offered at an affordable price.

3) The industrial use of the proposed psychiatric emergency state detection technique can


significantly reduce social burden by ensuring quality life using remote treatment at convenient

home environment. Thus, the use of remote mental healthcare service can greatly reduce a state’s

financial liability.

1.7 Structure of the Dissertation

The thesis has been organized into five chapters, as given below.

• Chapter 1 has presented a brief introduction of AmI system for psychiatric state inference.

It discussed the motivation of thesis, challenges of psychiatric state inference, the objective

of the study and contributions of thesis research.

• Chapter 2 has discussed the literature review and different existing approaches of psychi-

atric state inference. The limitation of traditional approaches and the draw-backs of existing

works are also presented in this chapter.

• Chapter 3 provided the details of Maximum Entropy Markov Model (MEMM) based psy-

chiatric state inference method. The healthcare service brokarage based ambient intelligent

system for psychiatric emergency is also presented in this chapter.

• Chapter 4 introduced the Web of Objects(WoO) based assisted living framework for emer-

gency psychiatry. The Hidden Markov Model (HMM) based psychiatric state inference

method is also discussed in details.

• Chapter 5 concluded the thesis with the significant findings of this research and also provides

future directions in this research area.

Chapter 2Literature Review and Related Works

2.1 Overview

This chapter discusses the concept of emergency psychiatry and the state-of-the-art technologies

in mental healthcare and AmI enabled assisted living.

2.2 Emergency Psychiatry

Emergency psychiatry deals with psychiatric emergencies, which are acute disturbances of be-

havior, thought and mood in mentally-disordered patients, who are at risk of potential danger to

themselves (e.g., suicide) and to others (e.g., homicide) [57]. As far we know there is no clin-

ical model exists for diagnosing and monitoring the homicide psychiatric emergency but there

exists the clinical model of suicide behavior [58]. However, this research is focused only on the

suicidal psychiatric emergency due to the unavailability and unobtainability of datasets of the

homicide psychiatric emergency. The higher severity levels of stress, depression, hopelessness,

aggressiveness and anxiety are the most influential features of a psychiatric emergency as pre-

sented in the clinical model of suicidal behavior [58]. Thus, different psychiatric scales, such as

the Beck Depression Inventory (BDI) [9](please see Fig. 2.1) and the Beck Hopelessness Scale

(BHS) [35](please see Fig. 2.2), are used to measure the severity levels of potential triggers for

an emergency psychiatric state. However, those scales are insufficient for real-time and continu-

ous assessment of the putative risk factors of an emergency psychiatric state. Therefore, in this

research, the non-invasive and wearable biosensor signals are analyzed by utilizing an ambient

assisted living (AAL) framework to predict the emergency psychiatric state.

13

CHAPTER 2. LITERATURE REVIEW AND RELATED WORKS 14

Figure 2.1: Sample Questionnaire of Beck Depression Inventory (BDI)


Figure 2.2: Sample Questionnaire of Beck Hopelessness Scale (BHS)

One of the pioneer researches [58], presented a questionnaire-based clinical model of suicide

behavior which is based on the subjective (e.g., Beck depression inventory, BDI-II) and objective

(e.g., Hamilton Depression Rating Scale, HDRS)(please see Fig. 2.3) psychiatric rating scales.

The European project MONARCA is the pioneer research which uses wearable sensors to

monitor the manic and depression states of bipolar disorder patients [5]. On the other hand, this

research proposed a novel emergency psychiatric state prediction method for AmI system using

the non-invasive or minimally invasive biosensor observations.


Question regarding suicidal ideation in Hamilton Rating Scale

for Depression

[ 0: Normal 1~2: Atypical 3~4: Emergency ]

Q 3. SUICIDE

0=Absent 1=Feels life is not worth living 2=Wishes he were dead or any thoughts of possible deaths to self 3=Suicidal ideas or gesture 4=Attempts at suicide

Figure 2.3: Question Number 3 of the Hamilton Rating Scale for Depression.

2.3 Probabilistic Graphical Models

Probabilistic graphical models are the integration between probability theory and graph theory,

which provides a natural tool for dealing with uncertainty and complexity, which are playing

an increasingly important role in the design and analysis of machine learning algorithms [59].

Fundamental to the idea of a graphical model is the notion of modularity i.e. a complex system

is built by combining simpler parts. Probability theory provides the glue whereby the parts are

combined, ensuring that the system as a whole is consistent, and providing ways to interface

models to data. The graph theoretic side of graphical models provides both an intuitively appealing

interface by which we can model highly-interacting sets of variables as well as a data structure.

Many of the classical multivariate probabalistic systems studied in fields such as statistics,

information theory, pattern recognition and statistical mechanics are special cases of the general

graphical model formalism e.g., mixture models, factor analysis, hidden Markov models, Kalman

filters and maximum entropy Markov models.

Probabilistic graphical models are graphs in which nodes represent random variables, and

the lack of arcs represent conditional independence assumptions. Hence they provide a compact

representation of joint probability distributions. Undirected graphical models are called Markov

Random Fields (MRFs). By contrast, directed graphical models also called Bayesian Networks or

Belief Networks (BNs). To model the psychiatric states of patients, we used two especial kinds

of Dynamic Bayesian Networks(DBN) namely hidden Markov models(HMM) and maximum en-

tropy Markov models(MEMM).


2.3.1 Hidden Markov Models (HMM)

The simplest kind of DBN is a Hidden Markov Model (HMM), which has one discrete hidden

node and one discrete or continuous observed node per slice.

s1

o1 o2 o3

s2 s3

Figure 2.4: Dependency graph of HMM

The generative hidden Markov model (HMM), determines maximum likelihood of observa-

tion sequence through determining joint probability. The dependency graph of Fig. 2.4 shows the

dependency among the states and observations in HMM.

2.3.2 Maximum Entropy Markov models (MEMM)

Like the hidden Markov model (HMM), the Maximum entropy Markov model (MEMM) is also

suitable for modeling sequential observations and states.


s1

o1 o2 o3

s3s2

Figure 2.5: Dependency graph of MEMM

However, unlike the HMM, discriminative MEMM can predict the state sequence from the ob-

servation sequence through conditional probability [54]. The dependency graph of Fig. 2.5 shows

the dependency among the states and observations in MEMM.

2.4 AmI Enabled Assisted Living

This section reviewed state-of-the -art technologies of AmI enabled assisted living and psychiatry

systems.

AmI enabled assisted living, where the living is supported by surrounded ambient intelligent

entities, has growing concern form last decade. J. Winkley et al. [3] propose an ambient assisted

living platform for passive care and elderly monitoring. In their platform, contact temperature

sensor, ambient temperature sensor, pulse sensor and accelerometer are used to detect standing,

sitting, running, walking and sleeping postures of a subject. In case of detected abnormal posture,

the system can make emergency dial-out for further assistance.

Kun Wang et al. [60] present local data processing architecture in AAL communications. The

architecture reduces the data processing burdens from remote healthcare service providers to ease

data transmission overload communication network. The local data processing unit has data gath-

ering, data filtering and data analyzing layers. The ambient sensors data are analyzed through

variable neighboring search based data analysis algorithm.


Igor Bisio et al. [61] introduce a smartphone centric AAL platform, which is suitable for mon-

itoring the patients’ of co-morbidity in home and outdoor environment. In this solely smartphone

centric solution, the audio processing approach is presented to identify whether the patient re-

mains alone or not. A place recognition method is discussed for indoor localization and a physical

activity recognition approach is explained based on the raw data acquired from smartphone.

Tommaso Magherini et al. [62] design an AAL framework for automated recognition of human

activities, in which they use temporal logic for the specification of activities of daily living. The

considered activities in prototype implementation are correct coffee preparation, incorrect coffee

preparation, telephone answered, missed call, correct medication intake, and incorrect medica-

tion intake which are recognized from video frame of two installed camera in a smart kitchen.

The online model checking engine of the proposed framework can recognize potential dangerous

activities and raising alerts for assistance.

Parisa Rashidi and Alex Mihailidis et al. [11] survey on AAL tools for elderly, in which the

existing solutions for AAL are reviewed. The smart home, assistive robotics, e-textile, and mobile

and wearable sensor technologies are used in AAL. The graphical model based activity recognition

systems are successfully used in ALL, whereas supervised learning and temporal logic based

methods are also used in ambient and vision based activity recognition.

Some related research on AAL has been done as a part of ubiquitous and pervasive comput-

ing and in form of smart-home system [63, 64], pervasive and persuasive healthcare system, and

telemedicine and remote patient care system. Perumal et al. [65] introduce simple object access

protocol (SOAP) based smart home protocol, which is a real time and bidirectional smart-home

control and monitoring system. It has web based feedback control channels to ensure interop-

eration among home appliances. The SMS module can control the system remotely in case of

emergency e.g. server failure.

Chao-Lin Wu et al. [66] propose a service oriented smart home architecture, which enables

a flexible infrastructure to adopt user requirements. The architecture is based on open services

gateway initiative (OSGi) so that the software components or bundles can install, update and re-

move without interrupting device operations. It also includes mobile agents (MA), which migrates

dynamically to ubiquitous devices of smart home and usage the resources to perform the assigned


tasks. Multiple OSGi devices can communicate with each other following P2P communication

strategy to facilitate device depended services over multiple devices. SOA ignores the layered

design approach and the architecture is based on synchronous communication. To ensure continu-

ous monitoring of AAL environment, it will require huge data and massage passing which causes

performance degradation in case of SOA. Furthermore, service composition and communication

are burdensome in SOA because of its complexity in abstraction of objects’ functionalities [67].

Therefore, the traditional Service-Oriented Architecture (SOA) [66], which ignores the layered

design approach, is proven to be too heavyweight for the resource constraints of assisted living

environment to enable AAL service. This research proposes an assisted living framework based

on a Web of Objects (WoO) [67, 68], especially for the mental healthcare scenario. Dwellers’ or

patients’ major psychiatric symptoms are monitored through home-based lightweight biosensors

and web-based psychiatric rating scales (e.g., the Beck Depression Inventory (BDI) [9], the Hamil-

ton Rating Scale for Depression (HRSD) [37]). The biosensor observations and psychiatric rating

scores are used to assess the dweller’s mental health status and thus to infer an emergency psy-

chiatric state. However, web of object (WoO) based architecture can overcome the limitations of

SOA by resetting layered design approach, objectifying of devices and entities, and virtualizing

those objects [12][32].

2.5 Tele-psychiatry and M-Psychiatry

The emergency psychiatry is a challenging research field to the psychiatric and mental healthcare

research community [16]. Monitoring psychiatric mental and behavioral state has always been

challenging for the researchers of mental healthcare [57]. Some of the legendary research and

developments regarding diagnosis of emergency psychiatry, tele-psychiatry and mental healthcare

platforms are studied in this section.

In 2006, John-Dylan Haynes & Geraint Rees [43] published a research paper in Nature Re-

views Neuroscience, where they revealed the possibility to decode human’s mental state such as

conscious experience and covert attitude by assessing non-invasive measurement of brain activity.

H Le-Niculescu et al. [42] successfully classified the blood biomarkers to recognize suicidality of

the patients of mood disorder. The author used convergence functional genomics to classify the


genes responsible for suicidality and identified the spermidine/spermine N1-acetyltransferase 1

(SAT1) as the most influential blood biomarker with other blood biomarkers.

Mbusa. C. Takenga et al. [69] propose a telematics platform to estimate and monitor stress

and fitness levels of an individual. The platform used stochastic fuzzy modeling-based heart rate

variability (HRV) analysis to model the stress levels. Steven K. Dobscha et al. [70] study the

feasibility of in-home telehealth system to monitor depression and pain of psychiatric patients. The

collected psychiatric rating scores for depression measurement (patient health questionnaire, PHQ-

9) and pain severity measurement (SF36-V) shows promising results in case of remote psychiatric

care system. The study recommends home health monitoring system to observe symptom severity

of mental health condition for both in clinical and research purposes.

Peter M. Yellowlees et al. [71] investigate the suitability and trustworthiness of asynchronous

telepsychiatry (ATP). The authors’ explain the complications in psychiatric treatment of limited

English proficient patients or minority groups’ patients and then propose ATP approaches of tran-

scultural psychiatry.

Dietmar Bruckner et al. [8] survey the cognitive automation technologies, tools and meth-

ods. The popular machine perception methods used in cognitive automation are, i) sensor fusion

and ii) behavioral modeling through statistical, probabilistic and hierarchical processing models.

The major application areas of cognitive automation are AAL, artificial general intelligence and

robotics.

2.6 State-of-the-arts

Some of the legendary research and developments regarding mental healthcare [8, 72] are studied

in this section. The MONitoring, treAtment and pRediCtion of bipolAr (MONARCA) disorder

episodes is a European project, which opens the door for self-management, assessment and treat-

ment of mentally sick bipolar patients using some tiny sensors [5]. The wearable system consists

of a smart phone, wrist-worn sensors for monitoring patient’s activity and smart socks to recog-

nize the mental states of bipolar patients. The project is especially developed for monitoring the

episodes of bipolar disorder patients, where they consider manic episode, mild depression episode

and severe depression episode as the mental states of bipolar patients. Basically, the MONARCA


is a pioneering research that uses wearable sensors (e.g., GPS and accelerometer) to monitor psy-

chiatric patients. However, the MONARCA did not consider the psychophysiological observations

through biosensors to predict the manic and depression states of bipolar disorder patients. On the

other hand, a novel emergency psychiatric state (i.e., atypical, emergency and normal) prediction

method for AAL is proposed in this thesis, where the EDA, ECG, EMG and BVP sensor observa-

tions are used for determining the potential triggers of psychiatric emergencies.

Dobscha, S. K. et al. [70] study the feasibility of an in-home telehealth system to monitor the

depression and pain of psychiatric patients. The collected psychiatric rating scores for depression

measurement (Patient Health questionnaire (PHQ-9)) [38] and pain severity measurement through

the short form 36-items health survey questionnaire for veterans (SF36-V) [73] shows promising

results in the case of the remote psychiatric care system. The study recommends home the health

monitoring system to observe the symptom severity of the mental health condition for both clinical

and research purposes.

Niculescu, H. L. et al. [42] successfully classified the blood biomarkers to recognize suici-

dality in the patients with mood disorder. Therein, the authors used convergence functional ge-

nomics to classify the genes responsible for suicidality and identified spermidine/Spermine N1-

Acetyltransferase 1 (SAT1) as the most influential blood biomarker among other blood biomark-

ers. The drawback of the blood biomarker-based suicidality prediction is its invasiveness, i.e., one

needs the blood to investigate the suicidality biomarkers. However, the suicidal genome classi-

fication is only possible in an intensive clinical and pathological setup environment. Thus, it is

infeasible to adopt the technique in an Ambient Intelligence (AmI) system for continuous moni-

toring of psychiatric patients or elderly people in the home environment.

There has been some influential research to measure the stress, depression, anxiety, and frustra-

tion levels of patients through biosensors. Cornella, K. S. et al. [45] used Electro-Dermal Activity

(EDA) sensors to observe the skin conductance response (i.e., sympathetic arousal) to determine

the stress levels of patients. Imaoka, K. et al. [47] used Electrocardiogram (ECG) sensors to mea-

sure the coefficient of variations of consecutive 100 R-Rintervals, which is an objective index of

depression. Using ECG sensors, Reilly, J. G. et al. [48] observed lengthening QTc-intervals (rate-

corrected QT) in case of drug-induced arrhythmia. The Blood Volume Pulse (BVP) sensor is used


to measure heart rate, heart rate variability, inter-beat interval and the deviation in BVP amplitude.

Riseberg, J. et al. [74] measured the frustration and anxiety levels of a patient using Galvanic Skin

Response (GSR), BVP and Electromyography (EMG) sensors.

However, this paper deals with the emergency psychiatric state detection through feature level

fusion of biosensors and psychometric observation and patient histories. In this proposal, four

kinds of biosensors (i.e., Electro-Dermal Activity sensor (EDA), Electrocardiogram sensor (ECG),

Blood Volume Pulse sensor (BVP) and surface Electromyography sensor (EMG) observation are

used to infer the emergency psychiatric state, as shown in Figure 2.6.

Figure 2.6: Bio-sensors network to collect psychophysiological observations in an AmI System.

2.7 Summary

Questionnaire based psychometric methods are traditionally used in mental health assessment and

monitoring. There are numerous drawbacks lies in psychometric methods e.g., off-line method,

patient's may hide information, feel cautious to provide accurate information, or misjudge the

significance of the answer, whereas the biomedical sensor based psychophysiological approaches

have benefits over all the mentioned limitations. Most of the bio sensors are lightweight and readily

available with consumer products (e.g., smartphones, smart wrist bands, chest belt and bracelet).

Recently, BVP sensors have been embedded in smart phones where the camera is used to pass op-

tical rays for the measurement of pulse and oxygen saturation level in the blood. Some of the EDA


sensors are embedded with smartwatches and wristbands as consumer products for measuring a

subject’s stress level. The ECG and EMG sensors are also available as a chest belt, arm band and

smart vest. Each of the aforementioned sensors have either their own communication capability

or they use their host as the gateway node to communicate with the Internet. However, these sen-

sors may also be included in health kits for assisted living as mass-marketed consumer products.

Although some of the recent state-of-the-arts research proposal e.g., MONARCA, cognitive au-

tomation opens the door of sensor based assessment and monitoring of mental health but there lies

a vast scope of research in personalized psychiatric care domain.

The next generation of ambient intelligent (AmI) technology sustains the development of an

improved healthcare delivery system for the healthcare industry. Several AmI systems in physical

healthcare are presented in [11], where the authors reviewed the existing state-of-the-art infras-

tructures for AmI. In addition, the intelligent medical box [12] and compressed sensing-based

biomedical signal acquisition [13] techniques are also some of the recent advancements in ambi-

ent technologies for assisted living. Although, industrialization offers cost effective, high quantity,

and affordable utilities, the delivery of health care in the process of “industrialization” must hold

a promise of more efficient and effective services [14]. Therefore, this research has attained a

vast scope of the industrialization of AmI based mental healthcare services through developing a

knowledge-based, personalized psychiatric care service.

Chapter 3Maximum Entropy Markov Model based Psychiatric

State Inference in AmI Environment

3.1 Background

Enterprise information systems in the healthcare industry apply cutting edge technologies and

methods to enhance operations and processes to ensure and manage efficient healthcare deliv-

ery [10]. The next generation of ambient intelligent (AmI) technology sustains the development

of an improved healthcare delivery system for the healthcare industry. Several AmI systems in

healthcare are presented in [11], where the authors reviewed the existing state-of-the-art infras-

tructures for AmI. In addition, the intelligent medical box [12] and compressed sensing-based

biomedical signal acquisition [13] techniques are also some of the recent advancements in ambi-

ent technologies for assisted living. Although, industrialization offers cost effective, high quantity,

and affordable utilities, the delivery of health care in the process of “industrialization” must hold

a promise of more efficient and effective services [14]. Therefore, this research has attained a

vast scope of the industrialization of AmI based mental healthcare services through developing a

knowledge-based, personalized psychiatric care service.

One of the pioneer researches [58], presented a questionnaire-based clinical model of suicide

behavior which is based on the subjective (e.g., Beck depression inventory, BDI-II) and objective

(e.g., Hamilton Depression Rating Scale, HDRS) psychiatric rating scales. However, the psychi-

atric emergency (e.g., homicidal and/or suicidal) state can even be developed within a moment

and may persist for a very short (in some cases a long) duration. But, the questionnaire-based psy-

chiatric state measurement methods cannot measure the state of emergency psychiatric patients

continuously. Therefore, this research proposes an AmI system for psychiatric home care service;

25

CHAPTER 3. MAXIMUM ENTROPY MARKOV MODEL BASED PSYCHIATRIC STATE INFERENCE IN AMIENVIRONMENT 26

a b

c

d e

sink node

(6a) Report & Recommendation

(3) Quantified Observations

(5) Predicted Psychiatric State & Recommendation

Healthcare Service Brokerage

Healthcare Cloud Service Provider

Hospitals &Rehabilitation Centres

Psychiatrist

(1) Sensor Observations and Screening Scores

(6b) Notification of Psychiatric Emergency

(0) Patient’s Medical & Family History

Patient with BAN

(2) Request for Patient’s History

(4) Medical & Family History

(Healthcare Cloud)Psychiatric Mental

State Sequence Generator(PMSSG)

Private cloud(IaaS)

Relatives Emergency care Departments

Registration &Authentication

Schedule & Deployment

Service Manager

Feature Extraction &

Quantification

DKB

Communication

(0)

(1)

(2)

(3)

(4)

(5)

(6a)

(6b) (6b) (6b) (6b)

Figure 3.1: The framework of AmI system of in-home psychiatric care service for emergencypsychiatry.

where the patients’ psychophysiological symptoms are analyzed continuously through real-time

biosensors (e.g., ECG, EDA and BVP) to infer the state of the psychiatric emergency. To develop

the AmI system, the psychiatric states are objectively defined as normal, atypical and emergency.

A subject with sound mental health or mild mental illness is considered as normal, while severe

psychiatric patients are deemed to be in an atypical state. The individuals with atypical states may

have homicidal and/or suicidal tendencies [16].

The main challenge of this research is to decode the psychiatric states from bio-sensor ob-


servations. Currently, there is no neuroimaging and biosensor individual channel, which can pin-

point the psychiatric emergency. Thus, in this research, the potential triggers or stressors (e.g.,

depression, stress, frustration level) of the psychiatric emergency are measured through multiple

biosensor channels to infer the state of the psychiatric emergency.

Moreover, monitoring the rapid behavior changes of psychiatric emergency patients is an-

other key challenge of this research because of the instability of potential triggers of psychi-

atric emergency patients. To tackle the research challenge, the emergency psychiatric states are

modeled, realized and formulated through the discriminative Maximum entropy Markov model

(MEMM) [54], which is suitable for modeling sequential observations and states. Afterwards, the

modified Viterbi [54] is applied to predict the state sequence from the continuous and sequential

bio-sensor observations.

The European project MONARCA is the pioneer research which uses wearable sensors to

monitor the manic and depression states of bipolar disorder patients [5]. On the other hand, this

research proposed a novel emergency psychiatric state prediction method for AmI system using

the non-invasive or minimally invasive biosensor observations.

The contributions of this research comprise three main components: AmI framework, EM-

psychiatric state modeling and classification system, and m-psychiatry system. The proposed AmI

framework supports the industrialization of psychiatric care services through fragmenting the pa-

tient care environment at home and hospital environment. In addition to ambient intelligence [56],

it incorporates psychiatrist, relatives, and emergency care department in the loop of psychiatric

care. The EM-psychiatric state modeling and classification system transit the psychiatric care

service delivery systems towards more knowledge-based semi-autonomic industrialization. The

m-psychiatry system alters the traditional paper-based manual psychiatric state recognition tech-

nique to mobile application based mental healthcare system to support the industrial development

of ambient assisted living (AAL) technologies and services for mental healthcare.

3.2 AmI System for Emergency Psychiatry

The framework of the AmI system of in-home psychiatric care service for emergency psychiatry

is presented in Fig. 3.1. The service oriented architecture (SOA) based service delivery model is


followed to develop the AmI system. The functionalities of five functional units of the proposed

framework are described in following subsections.

3.2.1 Body Area Networks (BANs)

In the proposed AmI system, the body area network (BAN) is designed for collecting patients’ psy-

chophysiological markers through biosensors. In the proposed EM-psychiatry system, the BAN

comprises three types of body sensors and a sink node. The Electro-Dermal Activity (EDA) sen-

sor observes skin conductance response, i.e. sympathetic arousal, to determine the stress level of

the patients as developed in [45]. The Electrocardiogram (ECG) sensor is placed to measure the

coefficient of variations of consecutive 100 R-R intervals as an objective index of depression [47],

while the QTc-interval also lengthens in the case of drug-induced arrhythmia [48]. The features of

the ECG is also used as the markers of drug and alcohol abuse. The frustration level of a patient

is measured through the features of BVP i.e., the variation of peak-to-peak interval and the pinch

of the BVP [52]. However, the abnormal motifs of ECG can be discovered through analyzing time

series data [75].

The features of any of the single channels are not adequate to decode the major risk factors

of a psychiatric emergency. Therefore, the fusion of indirect channels is an alternative way to

recognize a psychiatric emergency. The EEG biosignals also can be used with other biosensors,

e.g., EDA and BVP, as in the previous work [57], but the poor spatial resolution of the EEG and

sophistication in analyzing data makes it mostly suitable for a clinical environment.

As most of the time smartphone belongs close to the human body and possess both cellular and

Wi-Fi internet connectivity, so smartphone is used as the sink node [76] to facilitate ambulatory

(e.g., in home, in-office or in-car) monitoring. The sink node collects signal from biosensors and

send the encrypted and compressed signal to the registered healthcare service brokerage (HSB)

unit. However, smart phones are also used to collect the psychiatric screening scores through

standard questionnaires to measure the depression, stress and hopelessness levels of psychiatric

patients. The collected scores are also sent to the HSB unit for further processing.


3.2.2 Healthcare Service Brokerage (HSB)

The HSB acts like a collaborative entity among the patients, hospitals, psychiatrists, healthcare

clouds and other care-givers through a registration process. In the proposed AmI system, the HSB

receives the patients’ data from the BAN and then decrypts and decompresses the observations

after valid authentication. The features are extracted from the decompressed sensor observations

and then quantified in different scales, as discussed in section IV (C). The schedule and deployment

module of the HSB sends the quantified observations to the healthcare cloud for analyzing the

observations through complex machine learning algorithms. If necessary, this module sends a

request through the communication module to the corresponding private clouds of hospitals or

rehabilitation centers for the patients’ medical, personal, family and treatment histories.

3.2.3 Hospital or Rehabilitation Center

The information repository of the hospital unit stores the patient information, e.g., demographic

information such as age, gender, ethnic background, and personal history of substance abuse,

smoking habits, alcohol habits, suicide attempts, and medical and family histories, in a secure

private cloud. The private cloud shares the patients’ necessary medical, family and genetic histories

with the HCSP in respect of the transfer request from the HSB and in compliance with the Health

Insurance Portability and Accountability Act (HIPPA) and the agreement among the psychiatric

patients, hospitals, HSB and HCSP.

3.2.4 Healthcare Cloud Service Provider (HCSP)

The healthcare cloud service provider integrates patients’ histories from the private cloud of the

hospitals through the inter cloud communication process with the intervention of the HSB. In

addition, a psychiatric mental state sequence generator (PMSSG) generates the psychiatric state

sequence based on extracted features from the patients’ histories and sensor observations through

the MEMM and the modified Viterbi algorithm [54]. Then, the prognoses of emergency psychiatric

states are determined from the generated state sequence using the optimal thresholding policy.


3.2.5 EM-Psychiatric Response Management

In the proposed AmI system, several entities, e.g., psychiatrist, patients’ relatives, hospitals, emer-

gency transportation, and law and enforcement agencies are considered as the responsible support-

ers of the psychiatric emergency response team. These entities are assumed to be registered to the

prototype AmI system. The smartphone is also registered with patients’ SSN to recognize patient's

identity and for delivering the necessary recommendation based on predicted psychiatric state. Af-

ter inferring the emergency psychiatric state, the HCSP sends the psychiatric state sequence and

predicted state to the HSB. The interventions of healthcare service brokerage (HSB) entity of the

psychiatric care service in different psychiatric states are as follows:

• Normal: Sends the necessary recommendation to the patients’ smartphone.

• Atypical and Emergency:

– Sends the necessary recommendation to the patients’ smartphone.

– Sends psychiatric state reports to caregivers, i.e. psychiatrists and hospitals

Frequent Emergency: Multicast an emergency flash message to the psychiatric emergency re-

sponse team e.g., psychiatrist, patients’ relatives, and hospitals emergency care units.

3.3 State Modeling of Emergency Psychiatry

This section discusses the detail of the psychiatric state modeling and emergency state prediction

method of the AmI system for in-home psychiatric care service. The psychiatric states are con-

cealed, so a state can only be inferred through an observation sequence. Thus, state-observation

oriented probabilistic graphical models are mostly suitable to model psychiatric states.

3.3.1 Maximum Entropy Markov Model (MEMM)

Like the hidden Markov model (HMM), the Maximum entropy Markov model (MEMM) is also

suitable for modeling sequential observations and states. The generative hidden Markov model

(HMM), determines maximum likelihood of observation sequence through determining joint


Figure 3.2: The dependency graph of (a) HMM (b) MEMM.

probability. However, unlike the HMM, discriminative MEMM can predict the state sequence

from the observation sequence through conditional probability [54]. Here, the maximum en-

tropy Markov model (MEMM) is used to model the emergency psychiatric states. The depen-

dency graph of Fig. 3.2 shows the dependency among the states and observations in HMM and

MEMM. The M state maximum entropy Markov model is presented in Fig. 3.3, where the set

of states is S = {s1, s2, . . . , sM} = { normal, atypical, emergency }. The psychophysiologi-

cal, psychometric and static observations are combinely represented as set O = {o1, o2, . . . , oT },

where T is the time duration of taking observations. Here, oi is the vector of observed features

{f0, f1, . . . , fN−1}, which are extracted from the patients at time slot ti , and N is the total num-

ber of discriminative features. Now, the primal objective is to determine the most likely state

sequence Q = {q1, q2, . . . , qp} ∈ S based on the current sequential observations O for duration

T .

The challenges of MEMM deployment are optimal weight vector ŵ determination, overfitting

mitigation and the most likely state sequence generation. The limited memory L-BFGS algorithm,

Gaussian regularization function and modified Viterbi algorithm are applied respectively to over-

come those challenges. However, the assumptions of the proposed model for EM-psychiatry are

as follows,

• The probability of the current psychiatric state depends on the previous state, and current

저작자표시 비영리 공연 및 방송할 수...

Documents