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Development of real time systems to detect and track on duty injured firefighters using advanced signal processing techniques

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VIETNAM NATIONAL UNIVERSITY HA NOI UNIVERSITY OF ENGINEERING AND TECHNOLOGY PHAM VAN THANH DEVELOPMENT OF REAL-TIME SYSTEMS TO DETECT AND TRACK ON-DUTY INJURED FIREFIGHTERS USING ADVANCED SIGNAL PROCESSING TECHNIQUES DOCTORAL THESIS IN ELECCTRONICS AND TELECOMMUNICATIONS ENGINEERING Ha Noi - 2022 VIETNAM NATIONAL UNIVERSITY HA NOI UNIVERSITY OF ENGINEERING AND TECHNOLOGY PHAM VAN THANH DEVELOPMENT OF REAL-TIME SYSTEMS TO DETECT AND TRACK ON-DUTY INJURED FIREFIGHTERS USING ADVANCED SIGNAL PROCESSING TECHNIQUES Major: Electronic Engineering Code: 52 02 03.01 DOCTORAL THESIS IN ELECCTRONICS AND TELECOMMUNICATIONS ENGINEERING Supervised by: Assoc Prof Tran Duc Tan Ha Noi - 2022 DECLARATION “I hereby declare that the work contained in this thesis is of my own, and it has been written by myself under the supervison of Professor Tran Duc Tan at Faculty of Electrical and Electronic Engineering, Phenikaa University, Hanoi, Vietnam during the period from September 2017 to August 2021 The thesis has not been previously submitted for a degree or diploma at this or any other higher education institution I have duly acknowledged all the sources of information which have been used in the thesis The thesis content has been partly published in my list of publications as below: Pham Van Thanh, Tuan Khai Nguyen, Duc Anh Nguyen, Nhu Dinh Dang, Huu Tue Huynh, Duc-Tan Tran*, “Adaptive Step Length Estimation Support Indoor Positioning System using Low-Cost Inertial Measurement Units”, 2020 IEEE Eighth International Conference on Communications and Electronics, pp.271-275, 13-15 Jan.2021 Pham Van Thanh, Le Quang Bon, Nguyen Duc Anh, Dang Nhu Dinh, Huynh Huu Tue, Tran Duc Tan, "Multi-Sensor Data Fusion in A Real-Time Support System for On-Duty Firefighters", Sensors 2019 (ISSN: 1424-8220 – SCIE) Van Thanh Pham, Duc Anh Nguyen, Nhu Dinh Dang, Hong Hai Pham, Van An Tran, Kumbesan Sandrasegaran and Duc-Tan Tran, “Highly Accurate Step Counting at Various Walking Speeds Using Low-Cost Inertial Measurement Unit Support Indoor Positioning System”, Sensors 2018; 18(10):3186 (ISSN: 1424-8220 – SCIE) Pham Van Thanh, Duc-Tan Tran, Dinh-Chinh Nguyen, Nguyen Duc Anh, Dang Nhu Dinh, S El-Rabaie and Kumbesan Sandrasegaran, “Development of a Real-time, Simple and High-Accurate Fall Detection System for Elderly Using 3- DOF Accelerometers”, Arabian Journal for Science and Engineering 2018 (ISSN: 2191-4281 – SCIE) Pham Van Thanh, Anh-Dao Nguyen Thi, Quynh Tran Thi Thuy, Dung Chu Thi Phuong, Viet Ho Mau and Duc-Tan Tran, “A Novel Step Counter Supporting For Indoor Positioning Based On Inertial Measurement Unit”, 7th international conference on Integrated Circuit, Design, and Verification (ICDV), IEEE, pp 69-74, 5-6 Oct 2017 Nguyen Van Duong, Pham Van Thanh, Tran Van An, Nguyen Tuan Khai, Duong Thi Thuy Hang, Hoang The Hop and Tran Duc Tan, “Elevator Motion States Recognition Using Barometer Support Indoor Positioning System”, The 7th International Conference in Vietnam on the Development of Biomedical Engineering, IFMBE Proceedings, Springer, pp.581-587, 27-29 Jun.2018 The Hop Hoang, Van Thanh Pham, Thuy Quynh Tran Thi, Huu An Nguyen, Tuan Khai Nguyen and Tan Tran-Duc, “Xây dựng hệ thống xác định độ cao bên nhà cơng trình sử dụng đa cảm biến áp suất”, Hội nghị Quốc gia lần thứ XXI Điện tử, Truyền thông Công nghệ Thông tin (The 21st National Conference on Electronics, Communications and Information Technology), 2018, pp 193-197 Ha Noi, 16th January, 2022 Author Signature:……………………………………………… ACKNOWLEDGEMENT I would like to express my sincere thanks to my advisor Assoc Prof Tran Duc Tan, Faculty of Electrical and Electronic Engineering, Phenikaa University for the guidance and support throughout the completion of my thesis My thanks go to all lecturers and people in Faculaty of Electronics and Telecommunications, Universisty of Engineering and Technology, Viet Nam National University Hanoi for their teaching and useful help I give my special thanks to leaders, colleagues and Mr Tran Van An at University of Fire Prevention and Fighting for their help, guidance and financial support in the entire thesis completion process I am grateful to all people in MEMS lab as well as my students in Universisty of Engineering and Technology, Vietnam National University Hanoi and University of Fire Prevention and Fighting for their contribution I would also like to greatly thank the Vingroup Innovation Foundation (VINIF) - Vingroup Big Data Institute (VinBigdata) for their grant support and encouragement, which help me overcome financial problems and difficulties Last but the most important, I would like to thank my parents, my brother, my sister-in-law because their comfort and support are the power for me going to success “This work was supported by the Domestic Master/ PhD Scholarship Programme of Vingroup Innovation Foundation” Ha Noi, 16th January, 2022 CONTENTS CONTENTS LIST OF ABBREVIATIONS LIST OF FIGURES 12 INTRODUCTION 18 Defining Problem 18 The purpose of thesis 19 Objectives and Scope of the Thesis 21 Research Methodology 22 Scientific significance and Contributions of the Thesis 22 Thesis structure .23 CHAPTER OVERVIEW OF THE RESEARCH 25 1.1 Literature review 25 1.2 Related Studies on Injured Detection 26 1.3 Related Studies on Indoor Positioning 29 1.4 The challenges in study on injury detection and indoor positioning .29 1.5 Summary 30 CHAPTER SYSTEM DESCRIPTION, SENSOR ERRORS ELIMINATION AND MAP PROCESSING 31 2.1 System Description 31 2.2 Sensors Errors Elimination 33 The 3-DOF Accelerometer 33 The Magnetic Sensor 35 The Barometer 36 The MQ7 Sensor .38 2.3 Map Processing 38 Map Preprocessing 38 Map Simplification 39 Map Scale 43 2.4 Summary 45 CHAPTER DEVELOPMENT OF A METHOD TO DETECT INJURED FIREFIGHTERS 46 3.1 Fall Detection Method 46 Fall Detection Module .46 Post-fall Recognition Module 48 The Posture Recognition Estimation 49 The Vertical Velocity Estimation 49 3.2 Injury Detection for On-Duty Firefighters 52 The Proposed Fall Detection Algorithm for Firefighter 53 The Proposed Loss of Physical Performance Detection Algorithm for Firefighter 57 The CO Detection Algorithm 60 3.3 Result and Discussion 61 The Experimental Results 61 Fall Detection Results .63 Loss of Physical Performance Detection 64 The High CO Level Alerting Algorithm 66 3.4 The Comparison .67 The Comparison on the Experimental Data 68 The Comparison on Public Datasets 71 3.5 Summary 77 CHAPTER DEVELOPMENT OF A METHOD TO TRACK ONDUTY INJURED FIREFIGHTERS 79 4.1 The Step Counting Method 80 The Results 95 Discussion 99 4.2 Step Length Estimation 107 The Proposed Method .107 Results and Discussion 110 4.3 Turning Time and Direction Estimation 113 Turning Time Estimation 113 Turning Direction Estimation 116 4.4 Vertical Position Estimation 121 4.5 Summary 124 CHAPTER INDOOR FIREFIGHTER POSITIONING AND TRACKING USING MULTI-SENSOR DATA FUSION AND MAP MATCHING ALGORITHM 125 5.1 Data Fusion 125 5.2 Combining Data Fusion and Map Matching to Detect Indoor Position 126 Experiment Setup 126 The Scenarios Testing .126 5.3 Summary 135 CONCLUSIONS AND FUTURE WORK 136 LIST OF PUBLICATIONS 138 THE RELATED PUBLICATIONS 140 REFERENCES 141 LIST OF ABBREVIATIONS 3-DOF Three Degrees Of Freedom ADLs Activities of Daily Living CO Carbon Monoxide CO2 Carbon Dioxide COHb Blood Carboxyhemoglobin FFT Fast Fourier Transform GPS Global Positioning System iOS iPhone Operating System (Apple) I2 C Inter-Integrated Circuit IC Incident Commander IMU Inertial Measurement Unit MEMS Micro-Electro-Mechanical systems NFPA National Fire Protection Association OADs On-Duty Activities PAA Piecewise Aggregate Approximation PASS Personal Alert Safety System ppm Parts Per Millions RMS Root Mean Square ROC curve SAX receiver operating characteristic curve Symbolic Aggregate approximation SCBA Self-Contained Breathing Apparatuses SpO2 Saturation of Peripheral Oxygen SVM Support Vector Machine UFPF University of Fire Prevention and Fighting US United States US OSHA CFR U.S Occupational Safety and Health Administration Code of Federal Regulations 𝐴𝑐𝑐 Accuracy 𝐹𝑁 False Negative 𝐹𝑃 False Positive 𝑆𝑒𝑛 Sensitivity 𝑆𝑝𝑒𝑐 Specificity 𝑇𝑁 True Negative 𝑇𝑃 True Positive the commander in monitoring the firefighting and rescue performance as well as supporting on-duty injured firefighters 137 LIST OF PUBLICATIONS Pham Van Thanh, Tuan Khai Nguyen, Duc Anh Nguyen, Nhu Dinh Dang, Huu Tue Huynh, Duc-Tan Tran* (2021), Adaptive Step Length Estimation Support Indoor Positioning System using Low-Cost Inertial Measurement Units, 2020 IEEE Eighth International Conference on Communications and Electronics, 13-15 Jan 2021, pp 271-275 Van Thanh Pham, Quang Bon Le, Duc Anh Nguyen, Nhu Dinh Dang, Huu Tue Huynh and Duc Tan Tran (2019), Multi-Sensor Data Fusion in A Real-Time Support System for On-Duty Firefighters, Sensors, 19(21): 4746, (ISSN: 14248220 – SCIE) Van Thanh Pham, Duc-Tan Tran, Dinh-Chinh Nguyen, Nguyen Duc Anh, Dang Nhu Dinh, S El-Rabaie and Kumbesan Sandrasegaran (2019), Development of a Real-time, Simple and High-Accurate Fall Detection System for Elderly Using 3DOF Accelerometers, Arab J Sci Eng 44, 3329–3342 (2019), (ISSN: 2191-4281 – SCIE) Van Thanh Pham, Duc Anh Nguyen, Nhu Dinh Dang, Hong Hai Pham, Van An Tran, Kumbesan Sandrasegaran and Duc-Tan Tran (2018), Highly Accurate Step Counting at Various Walking Speeds Using Low-Cost Inertial Measurement Unit Support Indoor Positioning System, Sensors, 18(10):3186, (ISSN: 1424-8220 – SCIE) Nguyen Van Duong, Pham Van Thanh, Tran Van An, Nguyen Tuan Khai, Duong Thi Thuy Hang, Hoang The Hop and Tran Duc Tan (2018), Elevator Motion States Recognition Using Barometer Support Indoor Positioning System, The 7th International Conference in Vietnam on the Development of Biomedical Engineering, pp.581-587 The Hop Hoang, Văn Thành Phạm, Thúy Quỳnh Trần Thị, Hữu An Nguyễn, Tuan Khai Nguyen and Tan Tran-Duc, “Xây dựng hệ thống xác định độ cao bên nhà cơng trình sử dụng đa cảm biến áp suất”, Hội nghị Quốc gia lần thứ XXI Điện tử, Truyền thông Công nghệ Thông tin (The 21st National 138 Conference on Electronics, Communications and Information Technology), 2018, pp 193-197 Pham Van Thanh, Anh-Dao Nguyen Thi, Quynh Tran Thi Thuy, Dung Chu Thi Phuong, Viet Ho Mau and Duc-Tan Tran (2017), A Novel Step Counter Supporting For Indoor Positioning Based On Inertial Measurement Unit, 7th international conference on Integrated Circuit, Design, and Verification (ICDV), pp 69-74 139 THE RELATED PUBLICATIONS Pham Van Thanh, Nguyen Thi Huyen Nga, Le Thi Thu Ha, Do Van Lam, DinhChinh Nguyen, Duc-Tan Tran, “Development of a Real-time Supported System for Firefighters in Emergency Cases”, the 6th International Conference on the Development of Biomedical Engineering, June 2016 IFMBE Proceedings, Springer, vol 63, pp 341-344 140 REFERENCES Abbate S, Avvenuti M., Bonatesta F., Cola G., Corsini P., Vecchio A (2012), “A smartphone – based fall detection system”, Pervasive and Mobile Computing, Vol 8(6), pp 883-899 Ahmed A., Mohammed H., Saeid N (2018), “A Skeleton-Free Fall Detection System From Depth Images Using Random Decision Forest”, IEEE Syst J, Vol 12(3), pp 2994–3005 Anania G., Tognetti A., Carbonaro N., Tesconi M., Cutolo F., Zupone G., Rossi D.D (2008), “Development of a novel algorithm for human fall detection using wearable sensors”, in Proceedings of the IEEE Sensors, Lecce, Italy, pp 1336– 1339 Apple Inc Health Available online: www.apple.com/ios/health (Accessed on 3rd March 2018) Babu B.R., Patil S.K., & Gayathri T (2014), “Baandhav: smart mobile application for the safety of women and elderly population”, International journal of innovative research and development, Vol 3, pp 575-580 Bassett D.R., Toth L.P., LaMunion S.R et al (2017), “Step Counting: A Review of Measurement Considerations and Health-Related Applications”, Sports Med, vol 47, pp 1303–1315 Bevilacqua V., et al (2014), “Fall detection in indoor environment with kinect sensor”, 2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings, pp 319–324 BMP180 Digital Pressure Sensor, BMP180 Data Sheet, BOSCH Invented for Life Version 5 April 2013 Bourke A.K., O’Brien J.V., Lyons G.M (2007), “Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm”, Gait & posture, Vol 26(2), pp 194199 10 Bourke A.K., P van de Ven., Gamble M., O’Connor R., et al (2010), “Evaluation of waist-mounted tri-axial accelerometer based fall-detection algorithms during 141 scripted and continuous unscripted activities”, Journal of Biomechanics, Vol 43(15), pp 3051-3057 11 Cao Y., Yang Y., Liu W-H (2012), “E-FallD: A fall detection system using Android – Based smartphone”, 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, Chongqing, China 12 Castillo J C., et al (2014), “A Multi-Modal Approach for Activity Classification and Fall Detection”, International Journal of Systems Science, Vol 45, pp 810– 824 13 Cavagnaand G.A., Franzetti P (1986), “The determinants of the step frequency in walking in humans”, J Physiol, Vol 373(1), pp 235–242 14 Chang N., Rashidzadeh R., Ahmadi M (2010), “Robust indoor positioning using differential Wi-Fi access points”, IEEE Trans Consum Electron, Vol 56 (3), pp 1860–1867 15 Charleston Sofa Super Store Fire Available online: https://en.wikipedia.org/wiki/Charleston_Sofa_Super_Store_fire (Accessed on 1st April 2019) 16 Chen C.H., Ferreira J.C., Gross E.R., Mochly-Rosen D (2014), “Targeting aldehyde dehydrogenase 2: New therapeutic opportunities”, Physiol Rev, Vol 94(1), pp 1– 34 17 Chris S., and Toby B (2011), Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab, Wiley, pp 197-216 18 Cramariuc A., Huttunen H., Lohan E.S (2016), “Clustering benefits in mobilecentric WiFi positioning in multi-floor building”, in Proceedings of the 2016 International Conference on Localization and GNSS (ICL-GNSS), Barcelona, Spain, pp 1–6 19 Curone D., Secco E.L., Tognetti A., Loriga G., Dudnik G., Risatti M., Whyte R., Bonfiglio A., Magenes G (2010), “Smart Garments for Emergency Operators: The ProeTEX Project”, IEEE Trans Inf Technol Biomed, Vol 14(3), pp 694-701 142 20 Dräger SCBA systems: The Right Choice for Every Department Available online: https://www.draeger.com/Products/Content/pss-series-br-9041173-us.pdf (Accessed on 30 May 2019) 21 Erdem A., Muzaffer A., Abdulkadir Ş., Haibo W., Melih C.İ (2017), “Silhouette Orientation Volumes for Efficient Fall Detection in Depth Videos”, IEEE J Biomed Health Inform, Vol 21(3), pp 756–763 22 Fahy R.F., LeBlanc P.R., Molis J.L (2016), “Firefighter Fatalities in the United State–2015”, NFPA Fire Analysis and Research, National Fire Protection Association: Quincy, MA, USA 23 Feng Y., Wong C.K., Janeja V., Kuber R., Mentis H.M (2017), “Comparison of triaxial accelerometers step-count accuracy in slow walking conditions”, Gait & Posture, Vol 53, pp 11–16 24 Feng Z., Zhiguo C., Yang X., Jing M., Junsong Y (2018), “Real-Time Detection of fall from Bed Using a Single Depth Camera”, IEEE Trans Autom Sci Eng, Vol 16, pp 1018–1032 25 Ferreira A.G., Fernandes D., Catarino A.P., Monteiro J.L (2017), “Localization and Positioning Systems for Emergency Responders: A Survey”, IEEE Commun Surv Tutor, Vol 19, pp 2836–2870 26 Figo P.C., Diniz D.R., Ferreira J.M.P.C (2010), “Preprocessing techniques for context recognition from accelerometer data”, Pers Ubiquitous Comput, Vol 14, pp 645–662 27 Fire Statistics Vietnam (2014) Available online: http://thoibaotaichinhvietnam.vn (Accessed on 30 May 2019) 28 Fire Statistics Vietnam (2015) Available online: http://hanoimoi.com.vn (Accessed on 30 May 2019) 29 Frank Y S (2011), Image Processing and Mathematical Morphology Fundamentals and Applications, CRC Press 30 Genovese V., Mannini A., Sabatini A.M (2017), “A Smartwatch Step Counter for Slow and Intermittent Ambulation”, IEEE Access, Vol 5, pp 13028–13037 143 31 Gjoreski H., Lustrek M and Gams M (2011), “Accelerometer Placement for Posture Recognition and Fall Detection”, 2011 Seventh International Conference on Intelligent Environments, Nottingham, UK, pp 47-54 32 Gjoreski M., Gjoreski H., Luštrek M., Gams M (2016), “How Accurately Can Your Wrist Device Recognize Daily Activities and Detect Falls?”, Sensors, Vol 16(6):800 33 Gu F., Kealy A., Shang K.K.J (2015), “User-Independent Motion State Recognition Using Smartphone Sensors”, Sensors, Vol 15(12), pp 30636–30652 34 Gu F., Khoshelham K., Shang J., Yu F., Wei Z (2017), “Robust and Accurate Smartphone-Based Step Counting for Indoor Localization”, IEEE Sens J, Vol 17(11), pp 3453–3460 35 Harvey W (2002), Using the ADXL202 in Pedometer and Personal Navigation Applications, Analog Devices AN-602 application note 36 He Y., Li Y and Bao S.-D (2012), “Fall detection by built-in tri-accelerometer of smartphone”, Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics, pp 184-187 37 Ho N.H., Truong P.H., Jeong G.M (2016), “Step-Detection and Adaptive StepLength Estimation for Pedestrian Dead-Reckoning at Various Walking Speeds Using a Smartphone”, Sensors, Vol 16(9), 1423 38 Hu X., Shang J., Gu F., Han Q (2015), “Improving Wi-Fi indoor positioning via ap sets similarity and semi-supervised affinity propagation clustering”, International Journal of Distributed Sensor Networks, Vol 11(1) 39 Jeon H., Jo U., Jo M., Kim N., Kim Y (2013), “An Adaptive AP Selection Scheme Based on RSS for Enhancing Positioning Accuracy”, Wirel Pers Commun, Vol 69(4), pp 1535–1550 40 Jimenez A.R., Seco F., Prieto C and Guevara J (2009), “A Comparison of Pedestrian Dead-Reckoning Algorithms using a Low-Cost MEMS IMU”, 2009 IEEE International Symposium on Intelligent Signal Processing, Budapest, Hungary 144 41 Johnson M (2015), “Activity Monitors Step Count Accuracy in CommunityDwelling Older Adults”, Gerontology and Geriatric Medicine, Vol 42 Kales S.N., Soteriades E.S., Christoudias S.G., Christiani D.C (2003), “Firefighters and on-duty deaths from coronary heart disease: A case control study”, Environ Health, 2, 14 43 Kalman R.E (1960), “A New Approach to Linear Filtering and Prediction Problems”, J Basic Eng, pp 35-45 44 Kang X., Huang B., Qi G (2018), “A Novel Walking Detection and Step Counting Algorithm Using Unconstrained Smartphones”, Sensors, Vol 18, 297 45 Karter M.J., Molis J (2016), NFPA Research, “U.S Firefighter Injuries - 2015”, NFPA J 2016, 92, 48 46 Kirmse A., de Ferranti J (2017), “Calculating the prominence and isolation of every mountain in the world”, Progress in Physical Geography: Earth and Environment, Vol 41(6), pp 788–802 47 Kuanghsuan C., Jingjung Y., Fushan J (2016), “Accelerometer-based fall detection using feature extraction and support vector machine algorithms”, Instrum Sci Technol, Vol 44 (4), pp 333–342 48 Lai Y.-C., Chang C.-C., Tsai C.-M., Huang S.-C., Chiang K.-W (2016), “A Knowledge-Based Step Length Estimation Method Based on Fuzzy Logic and Multi-Sensor Fusion Algorithms for a Pedestrian Dead Reckoning System”, ISPRS Int J Geo-Inf, Vol 5(5) 49 Lee H., Bae M., Shin D.B., Lee S., Myeong S., Hong S.G., Yang H., Choi J., Son K.Y., Lee K.B., et al (2017), “ATHENA: Distributed IoT Systems Providing Salient Features for Safety of Firefighters in Infra-Less Fire Environments”, in Proceedings of the International Conference on Platform Technology and Service (PlatCon), Busan, South Korea 50 Levin B.C., Erica D.K (2005), Toxicology of Fire and Smoke, Inhalation Toxicology, 2nd ed; CRC Press (Taylor and Francis Group): Boca Raton, FL, USA, pp 114-125 145 51 Lim D., Park C., Kim N H., Kim S.-H and Yu Y S (2014), “Fall-Detection Algorithm Using 3-Axis Acceleration: Combination with Simple Threshold and Hidden Markov Model”, Journal of Applied Mathematics, Vol 2014 52 Loh D., Zihajehzadeh S., Hoskinson R., Abdollahi H., Park E.J (2016), “Pedestrian Dead Reckoning With Smartglasses and Smartwatch”, IEEE Sens J, Vol 16, pp 8132–8141 53 M Lustrek et al (2015), “Detecting falls with location sensors and accelerometers”, IEEE Pervasive Computing, Vol 14(4), pp 72-79 54 Mannini A., Intille S.S., Rosenberger M., Sabatini A.M., Haskell W (2013), “Activity recognition using a single accelerometer placed at the wrist or ankle”, Med Sci Sports Exerc, Vol 45(11), pp 2193–2203 55 Maria C., Koray O., Yu Z., Senem V (2016), “A Survey on Activity Detection and Classification Using Wearable Sensors”, IEEE Sens J, Vol 17(2), pp 386–403 56 Mark G RN, BSN, EMT-P I/C, Royal Oak, Mich (2008), “Carbon Monoxide Poisoning”, J Emerg Nurs, Vol 34(6), pp 538–542 57 Masateru M., Yasuhiro F., Kazuki H., Tomonori A (2004), “DOLPHIN: A Practical Approach for Implementing a Fully Distributed Indoor Ultrasonic Positioning System”, UbiComp 2004, pp 347-365 58 Mazurek P., Morawski R Z (2015), “Application of Naïve Bayes Classifier in a Fall Detection System Based on Infrared Depth Sensors”, 2015 IEEE 8th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), Warsaw, Poland, pp 717 – 722 59 Medrano C., Igual R., Plaza I., Castro M (2014), “Detecting falls as novelties in acceleration patterns acquired with smartphones”, PLoS ONE, Vol 9(4): e94811 60 Mellone S., Tacconi C., Schwickert L., Klenk J., Becker C., Chiari L (2012), “Smartphone – based solutions for fall detection and prevention: the FARSEEING approach”, Zeitschrift für Gerontologie und Geriatrie, Vol 45(8), pp 722-727 61 Mostarac P., Malarić R., Jurčević M., Hegeduš H., Lay-Ekuakille A and Vergallo P (2011), “System for monitoring and fall detection of patients using mobile 3-axis 146 accelerometers sensors”, 2011 IEEE International Symposium on Medical Measurements and Applications, pp 456-459 62 Moussa M M., Hemayed E E., El Nemr H A., Fayek M B (2018), “Human Action Recognition Utilizing Variations in Skeleton Dimensions”, Arab J Sci Eng, Vol 43(2), pp 597-610 63 MQ7 Datasheet 2015 Available online: https://www.sparkfun.com/datasheets/Sensors/Biometric/MQ-7.pdf (Accessed on 01 April 2019) 64 Nelson B., Daniel M., David S (2005), “Performance of Thermal Exposure Sensors in Personal Alert Safety System (PASS) Devices”, Building and Fire Research Laboratory National Institute of Standards and Technology, USA 65 Nilsson J.O., Rantakokko J., Händel P., Skog I., Ohlsson M., Hari K.V.S (2014), “Accurate Indoor Positioning of Firefighters using Dual Foot-mounted Inertial Sensors and Inter-agent Ranging”, in Proceedings of the 2014 IEEE/ION Position, Location and Navigation Symposium (PLANS), Monterey, CA, USA 66 Noury N., Rumeau P., Bourke A., Laighin G., Lundy J (2008), “A proposal for the classification and evaluation of fall detectors”, IRBM, Vol 29(6), pp 340–349 67 Özdemir A.T., Barshan B (2014), “Detecting Falls with Wearable Sensors Using Machine Learning Techniques”, Sensors, Vol 14(6), pp 10691–10708 68 Pacer Heath Pedometer, Step Counter & Weight Loss Tracker App Available online: www.mypacer.com (Accessed on 3rd March 2018) 69 Pannurat N., Thiemjarus S., and Nantajeewarawat E (2014), “Automatic Fall Monitoring: A Review”, Sensors, Vol 14(7), pp 12900-12936 70 Paola P., Alberto B., Lorenzo M., Lorenzo P., Luca P., Michele P., Simone V (2016), “A Wearable Fall Detector for Elderly People Based on AHRS and Barometric Sensor”, IEEE Sens J, Vol 16 (17), pp 6733–6744 71 Park C., Kim J., Sohn J-C and Choi H.-J (2011), “A Wrist-Type Fall Detector with Statistical Classifier for the Elderly Care”, KSII Transactions on Internet and Information Systems, Vol 5(10) 147 72 Planinc R., Kampel M (2013), “Introducing the use of depth data for fall detection”, Personal and Ubiquitous Computing, Vol 17, pp 1063–1072 73 Pope C.A., Bhatnagar A., McCracken J.P., Abplanalp W., Conklin D.J., O'Toole T (2016), “Exposure to Fine Particulate Air Pollution Is Associated With Endothelial Injury and Systemic Inflammation”, Circ Res, Vol 119, pp 1204-1214 74 Prominence Theory Available online: http://www.peaklist.org/theory/theory.html (Accessed on October 2017) 75 Public Datasets Available online: https://archive.ics.uci.edu/ml/machine-learningdatabases/00455/ (Accessed on April 2019) 76 Q Guo, W Deng, O Bebek, C Cavusoglu, C Mastrangelo and D Young (2018), "Personal inertial navigation system employing MEMS wearable ground reaction sensor array and interface ASIC achieving a position accuracy of 5.5m over 3km walking distance without GPS", 2018 IEEE International Solid - State Circuits Conference - (ISSCC), pp 180-182 77 Qingchi Z., Biao Z., Changqiang J., Nammoon K., and Youngok K (2015), “A Novel Step Counting Algorithm Based on Acceleration and Gravity Sensors of a Smart-Phone”, International Journal of Smart Home, Vol 9(4), pp 211-224 78 Rantakokko J., Rydell J., Strömbäck P., Händel P., Callmer J., Törnqvist D., Gustafsson F., Jobs M., Grudén M (2011), “Accurate and reliable soldier and first responder indoor positioning: Multisensor systems and cooperative localization”, IEEE Wirel Commun, Vol 18(12), pp 10–18 79 Raul I., Carlos M., Inmaculada P (2015), “A comparison of public datasets for acceleration-based fall detection”, Med Eng Phys, Vol 37(9), pp 870–878 80 Rui T., Lino F., Ramiro B., Ricardo A (2014), “Traveled Distance Estimation Algorithm for Indoor Localization”, Conferenceon Electronics, Telecommunications and Computers – CETC 2013, Procedia Technology, Vol 17, pp 248 – 255 81 S H Shin, C.G.Park, J W Kim, H S Hong and J M Lee, “Adaptive Step Length Estimation Algorithm Using Low-Cost MEMS Inertial Sensors”, 2007 IEEE Sensors Applications Symposium, San Diego, CA, USA 148 82 Samsung Electronics Co., Ltd Samsung Health Available online: Health.apps.samsung.com (Accessed on March 2018) 83 Sebastian, O.H.M (2010), An efficient orientation filter for inertial and inertial/magnetic sensor arrays 84 Stone E.E., Skubic M (2011), “Evaluation of an inexpensive depth camera for passive in-home fall risk assessment”, 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops, pp 71-77 85 Suad A., Ahmad L., Heather P., Kofi A (2018), “Video Based Fall Detection using Features of Motion, Shape and Histogram”, PETRA 18 In Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference, Corfu, Greece, pp 529–536 86 Tang Z., Guo Y., Chen X (2016), “Self-adaptive Step Counting on Smartphones under Unrestricted Stepping Modes”, in Proceedings of the 2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC), Atlanta, GA, USA, pp 788–797 87 Tao X., Yun Z (2018), “Elders’ fall detection based on biomechanical features using depth camera”, Int J Wavelets Multiresolut Inf Process, Vol 16, pp 1840005-01 – 1840005-15 88 Tartare G., Zeng X., Koehl L (2018), “Development of a wearable system for monitoring the firefighter's physiological state”, in Proceedings of the 2018 IEEE Industrial Cyber-Physical Systems (ICPS), St Petersburg, Russia, pp 561–566 89 Thorup C.B., Andreasen J.J., Sørensen E.E., Grønkjær M., Dinesen B.I., Hansen J (2017), “Accuracy of a step counter during treadmill and daily life walking by healthy adults and patients with cardiac disease”, BMJ Open 2017, Vol 7(3), e011742 90 Tomkun J., Nguyen B (2010), “Design of a Fall Detection and Prevention System for the Elderly”, Electrical and Biomedical Engineering Design Project, Ontario, Canada 149 91 Vavoulas G., Pediaditis M., Spanakis E G and Tsiknakis M (2013), “The MobiFall dataset: An initial evaluation of fall detection algorithms using smartphones”, 13th IEEE International Conference on BioInformatics and BioEngineering, pp 1-4 92 Walking Datasets Available online: https://github.com/Oxford-step- counter/DataSet/tree/master/validation (Accessed on 30 August 2018) 93 Wang L., Liu T., Wang Y., Li Q., Yi Y., Inoue Y (2017), “Evaluation on Step Counting Performance of Wristband Activity Monitors in Daily Living Environment”, IEEE Access, Vol 5, pp 13020–13027 94 Wang X., Kim H (2015), “Detecting User Activities with the Accelerometer on Android Smartphones”, Journal of Multimedia Information System, Vol 2(2), pp 233–240 95 Worcester Cold Storage and Warehouse Co Fire Available online: https://en.wikipedia.org/wiki/Worcester Cold Storage and Warehouse Co fire (Accessed on 1st April 2019) 96 Yang S.-W., Lin S.-K (2014), “Fall Detection for Multiple Pedestrians Using Depth Image Processing Technique”, Computer Mehtods and Programs in Biomedicine, Vol 114, pp 172-182 97 Zhang C., Tian Y., and Capezuti E (2012), “Privacy Preserving Automatic Fall Detection for Elderly Using RGBD Cameras”, Computers Helping People with Special Needs, Vol 7382, pp 625–633 98 Zhang H., Yuan W., Shen Q., Li T., Chang H (2015), “A handheld inertial pedestrian navigation system with accurate step modes and device poses recognition”, IEEE Sens J, Vol 15(3), pp 1421–1429 99 Zhao Q., Zhang B., Wang J., Feng W., Jia W., Sun M (2017), “Improved method of step length estimation based on inverted pendulum model", International Journal of Distributed Sensor Networks, Vol 13(4) 100 Zhao Y.X., Shen Q., and Zhang L M (2011), “A Novel High Accuracy Indoor Positioning System Based On Wireless Lans”, Progress In Electromagnetics Research C, Vol 24, pp 25-42 150 101 Zhou Z., Chen T., Xu L (2015), “An Improved Dead Reckoning Algorithm for Indoor Positioning Based on Inertial Sensors”, International Conference of Electrical, Automation and Mechanical Engineering, pp 369 – 371 151 ... NOI UNIVERSITY OF ENGINEERING AND TECHNOLOGY PHAM VAN THANH DEVELOPMENT OF REAL-TIME SYSTEMS TO DETECT AND TRACK ON-DUTY INJURED FIREFIGHTERS USING ADVANCED SIGNAL PROCESSING TECHNIQUES Major:... scale 45 CHAPTER DEVELOPMENT OF A METHOD TO DETECT INJURED FIREFIGHTERS This chapter presents details of the proposed method to detect injured firefighters through the recorded and public datasets... proposed to develop a wearable system to measure the heartbeat and respiration cycle of firefighters in order to monitor their health This study aimed to monitoring physiological states of firefighters,

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