| Titre : | Smart Device For Peace Of Mind |
| Auteurs : | Meriem Brahmi, Auteur ; FERIAL BOUSSEHAL, Auteur ; Fatma zohra Boughagal, Auteur ; Soheyb Ayad, Directeur de thèse |
| Type de document : | Monographie imprimée |
| Editeur : | Biskra [Algérie] : Faculté des Sciences Exactes et des Sciences de la Nature et de la Vie, Université Mohamed Khider, 2023 |
| Format : | 1vol.(86p.) / ill.couv.ill.encoul / 30 cm |
| Langues: | Anglais |
| Langues originales: | Anglais |
| Mots-clés: | GPS, Geolocation, Internet of Things (IoT),Machine Learning,Smart GPS Tracker and Health Monitoring System,Smart Bracelet. |
| Résumé : |
Monitoring and healthcare systems have become more in demand in recent years as these systems provide a comprehensive solution for patient care and monitoring. It combines geolocation technology, real-time tracking, and physiological monitoring, which has led to important developments in this field and a good number of tracking systems. Some startups have also worked on the system, but they have not helped or cared for children with autism in particular. Parents of autistic children in Algeria face many challenges in caring for their children, fear of losing them, and struggle to deal with their children’s unexpected outbursts. To deal with these problems and deepen the study, a questionnaire was distributed to caregivers of children with autism. 91.8% of the respondents stated that they have difficulty taking care of their children all the time, and 93.9% of the respondents confirmed that their children suffer from anxiety and stress attacks. Based on the results obtained, a smart bracelet connected to the mobile application was developed. This smart locator aims to track individuals who need special attention and care. Using advanced geolocation technologies such as GPS and IoT, the system can accurately monitor a person’s real-time location and also include the ability to monitor patients’ health status. By collecting various physiological data, such as heart rate, body temperature, and movement patterns, by means of sensors built into the system. In addition, the system includes the ability to predict the stress and anxiety levels of a child with autism, and to achieve this, machine learning algorithms analyze the collected data and identify patterns and indicators of stress and anxiety. By detecting and predicting these conditions early on, appropriate interventions can be initiated to manage and mitigate the distress experienced by the child, and it reached the highest classification accuracy of 98% for estimating the child’s stress level. |
| Sommaire : |
General Introduction 3 1 GENERALITIES 5 1.1 Introduction . . 5 1.2 Geolocation . 5 1.2.1 Definition of Geolocation . 5 1.2.2 Geolocation Techniques . 5 1.3 Global Positioning System (GPS) . 6 1.3.1 Definition of GPS . 6 1.3.2 Elements of GPS . 6 1.3.3 How Does GPS Technology Work? . .. 7 1.4 Global System for Mobile Communication(GSM) . . 8 1.4.1 Definition of GSM . .8 1.4.2 GSM Network Architecture . 8 1.5 General Packet Radio Service(GPRS) . 10 1.5.1 Definition of GPRS . . .10 1.5.2 Architecture of GPRS . 10 1.6 Internet of Things . . 10 1.6.1 What is Internet of Things? . 11 1.6.2 Characteristics of IoT . . 11 1.6.3 Architecture of IoT. 12 1.6.4 IoT prtocols .13 1.7 Artificial Intelligence (AI.. 14 1.8 Machine Learning (ML) . . 14 1.9 Artificial Intelligence vs Machine Learning . .. 14 1.10 Types of Machine Learning . 15 1.10.1 Supervised Learning . 16 1.10.2 Unsupervised Learning . .17 1.10.3 Reinforcement Learning . 17 1.11 Supervised Machine Learning Categories . 18 1.11.1 Regression . . 18 1.11.2 Classification . . 18 1.12 Classification Algorithms in Machine Learning . . 20 1.12.1 K-Nearest Neighbors Classifier Algorithm . . 20 1.12.2 Support Vector Machine Algorithm . . 20 1.12.3 Decision Tree Algorithm . . 21 1.12.4 Random Forest Classifier Algorithm . . 21 1.13 Machine Learning Life Cycle . 22 1.14 Conclusion . 23 2 RelatedWorks 24 2.1 Introduction . . 24 2.2 Cases of Study . . 24 2.2.1 Autism . . 24 2.2.2 Discussion . . 25 2.3 GPS Tracking Solutions . . 27 2.3.1 RingOn . 27 2.3.2 Silvertree . .. 27 2.3.3 4G TK905 . .. 28 2.3.4 Osmile ED1000 . . 28 2.3.5 GF-07 . . 28 2.3.6 Nabi Z7 . . . 29 2.4 Discussion . . 29 2.5 Scientific Researches . . . 32 2.5.1 Machine Learning and IoT for Stress Detection and Monitoring.. 32 2.5.2 Stress Prediction Model Using Machine Learning . . . . 33 2.5.3 Stress Detection by Machine Learning and Wearable Sensors . 34 2.5.4 Introducing WESAD, a Multimodal Dataset for Wearable Stress and 35 2.5.5 SaYoPillow: Blockchain-Integrated Privacy-Assured IoMT Framework for Stress Management Considering Sleeping Habits . . 36 2.6 Discussion . .37 2.7 Conclusion . . . 39 3 Conception 40 3.1 Introduction . . . . 40 3.2 General Architecture of System . . 40 3.3 Detailed Architecture of System . . 41 3.4 Autism Smart Stress Prediction System . . 43 3.4.1 Data collection . . 43 3.4.2 Data Preparation . 44 3.4.3 Classification and Training . .45 3.4.4 Confusion Matrix and Evaluation Metrics . 47 3.5 Pseuocodes of System . 48 3.5.1 Location Function .. 48 3.5.2 Healthcare Sensor Function . 49 3.5.3 Geofencing Function . . 50 3.5.4 Healthcare Monitoring Function . 50 3.5.5 Path Historique Function . . 50 3.5.6 Track Location Function . .51 3.5.7 Command Function . 51 3.6 Diagrams of System . . . 52 3.7 Conclusion . . . 55 4 Implementation 56 4.1 Introduction . . . 56 4.2 Environment and Software Tools . 56 4.2.1 Python . . 56 4.2.2 Anaconda . . 56 4.2.3 Jupyter Notebook . 57 4.2.4 JSON . . 57 4.2.5 Flutter . 57 4.2.6 Firebase . . 57 4.2.7 Arduino . 57 4.3 Hardware Implementation .58 4.3.1 ESP32 DevkitC . 58 4.3.2 GPS module Neo6m . 58 4.3.3 Sim800L . . 58 4.3.4 MAX30102 Pulse Oximetry . . 58 4.3.5 LM35 . .59 4.3.6 Schematic Diagram . . 59 4.4 Smart Bracelet Prototype . 60 4.5 Machine learning Models Implementation . 61 4.5.1 Dataset Import and Preparation . . 61 4.5.2 Training the ML Models ..63 4.5.3 Testing the RF Model . . 64 4.5.4 Save the RF Model . 64 4.6 Software Implementation . . 65 4.6.1 Web Site Interfaces . 65 4.6.2 Mobile Application Interfaces . 68 4.6.3 Firebase Interface. 74 4.7 Conclusi.. 76 General conclusion 77 A Questionnaire and Result 82 A.1 Survey . . 82 A.2 Results . . 84 B Start-Up 86 |
| Type de document : | Mémoire master |
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