Machine Learning and Python for Human Behavior, Emotion, and Health Status Analysis

Preface
    Acknowledgments
    About the Author
    Chapter 1 Smart Assisted Homes, Sensors, and Machine Learning
        1.1 Smart Homes
            1.1.1 Technologies
            1.1.2 Benefits
            1.1.3 Challenges
        1.2 Example Smart Assisted Homes
        1.3 Events in Smart Assisted Homes
        1.4 Sensors in Smart Home
            1.4.1 Wearable Sensors
            1.4.2 Ambient Sensors
        1.5 Machine Learning
            1.5.1 Supervised Learning
            1.5.2 Unsupervised Learning
            1.5.3 Semi-Supervised Learning
        1.6 Deep Machine Learning
            1.6.1 Transfer Learning
        1.7 Limitations of Machine Learning
            1.7.1 Underfitting and Overfitting
            1.7.2 Other Limitations
        1.8 Conclusion
        References
    Chapter 2 Python and Its Libraries
        2.1 Python’s Key Features
        2.2 Python in Practice
        2.3 Python Libraries and Frameworks
        2.4 Python’s Community to Learn
        2.5 Python’s Impact on Education
        2.6 Python 2 Versus Python 3
        2.7 Python’s Role in Data Science and Machine Learning
        2.8 Challenges and Considerations
        2.9 Python Basics
        2.10 Built-in Python Libraries
        2.11 Data Manipulation Libraries
            2.11.1 NumPy
            2.11.2 Statistical Analysis
        2.12 Pandas
            2.12.1 Pandas Data Structures
            2.12.2 Basic Operations
        2.13 Data Visualization
            2.13.1 Matplotlib
            2.13.2 Seaborn
        2.14 Conclusion
        References
    Chapter 3 Feature Analysis Using Python
        3.1 Feature Extraction
        3.2 Principal Component Analysis (PCA)
        3.3 Kernel Principal Component Analysis (KPCA)
        3.4 Feature Extraction Using ICA
        3.5 Linear Discriminant Analysis (LDA)
        3.6 Conclusion
        References
    Chapter 4 Deep Learning and XAI with Python
        4.1 Introduction
        4.2 Non-Deep Machine Learning
            4.2.1 Support Vector Machines
            4.2.2 Random Forests
            4.2.3 AdaBoost and Gradient Boosting
            4.2.4 Nearest Neighbors
            4.2.5 More Examples
        4.3 Deep Machine Learning
            4.3.1 Convolutional Neural Network
            4.3.2 Pre-trained CNN Models
            4.3.3 Long Short-Term Memory (LSTM)
            4.3.4 Neural Structured Learning
        4.4 Explainable AI (XAI)
            4.4.1 Local Explanations
            4.4.2 Visual Explanations
            4.4.3 Feature Relevance Explanations
        4.5 Conclusion
        References
    Chapter 5 Behavior and Health Status Recognition
        5.1 Wearable Sensor-based Behaviour Recognition
            5.1.1 Mobile Health Dataset and Application
            5.1.2 PUC-Rio Dataset
            5.1.3 ARem Dataset
            5.1.4 WISDM Dataset
            5.1.5 Real-Time HAR Using Wearable Sensor
        5.2 Video Camera-based Behavior Recognition
        5.3 Ambient Sensor-based Behavior Recognition
            5.3.1 Real-Time Home Monitoring Using Ambient Sensors
            5.3.2 Occupancy Prediction Dataset
        5.4 Health Status Monitoring
            5.4.1 LSTM for Prediction of Health Status
            5.4.2 ARIMA for Prediction of Health Status
            5.4.3 Case Study of Oxygen Saturation, Pulse, and Respiration Prediction
            5.4.4 Sleep Quality Analysis
            5.4.5 Case Study of Sleep Quality Analysis
        5.5 Synthetic Data Generation
        5.6 Conclusion
        Acknowledgement
        References
    Chapter 6 Emotion Recognition
        6.1 Image-based Emotion Recognition
        6.2 Case-Studies for Image-based Emotion Recognition
            6.2.1 Local Directional Strength Pattern (LDSP)
            6.2.2 Principal Component Analysis on LDSP
            6.2.3 Linear Discriminant Analysis on PCA
            6.2.4 Facial Expression Modeling
            6.2.5 Experiments on Depth Dataset
            6.2.6 Experiments on RGB-based Public Database
            6.2.7 Experiments on Depth-based Public Database
        6.3 Sample Code for Image-based Emotion Recognition
        6.4 Real-Time Image-based Emotion Recognition
        6.5 Voice-based Emotion Recognition
        6.6 Case-Studies on Voice-based Emotion Recognition
            6.6.1 Signal Pre-processing
            6.6.2 Feature Extraction with MFCC
            6.6.3 Emotion Modelling
            6.6.4 Experiments and Results
        6.7 Sample Code for Voice-based Emotion Recognition
        6.8 Conclusion
        References
    Index