Enhancing Human Activity Recognition via WiFi CSI with Machine Learning Techniques and Generalization

1. Introduction 1.1 Background of Human Activity Recognition 1.2 Importance of Machine Learning in HAR 1.3 Objectives of the Study 1.4 Structure of the Paper 2. WiFi Channel State Information (CSI) 2.1 Explanation of WiFi CSI 2.2 Role of CSI in Activity Recognition 2.3 Challenges in Using CSI 3. Machine Learning Techniques 3.1 Overview of Machine Learning Algorithms 3.2 Supervised Learning Approaches 3.3 Unsupervised Learning Techniques 4. Feature Engineering for HAR 4.1 Importance of Feature Engineering 4.2 Techniques for CSI Feature Extraction 4.3 Data Preprocessing Methods 5. Model Training and Evaluation 5.1 Training Methodologies 5.2 Evaluation Metrics for Accuracy 5.3 Model Optimization Strategies 6. Generalization in HAR Models 6.1 Definition and Importance of Generalization 6.2 Techniques to Improve Generalization 6.3 Cross-Dataset Performance Analysis 7. Case Studies and Applications 7.1 Real-world HAR Implementations 7.2 Impact of WiFi CSI in Different Environments 7.3 Comparative Analysis of Techniques 8. Conclusion and Future Work 8.1 Summary of Findings 8.2 Limitations of the Current Study 8.3 Directions for Future Research