Activity Recognition and Prediction for Smart IoT Environments (Internet of Things)

Preface
Contents
Discovering Human Habits Through Process Mining: State of the Art and Research Challenges
	1 Introduction
	2 Background and Related Works
		2.1 Sensor Data in Smart Spaces
		2.2 Process Mining
		2.3 Unsupervised Approaches to Ambient Intelligence
	3 Unsupervised Human Habit Discovery
	4 Experiments
	5 Conclusion
	Appendix
	References
Methodology for Human Activity Recognition Based on Wearable Sensor Networks
	1 Introduction
	2 System Overview
		2.1 System Overview
		2.2 Definition and Conversion Relationship of Coordinate System
		2.3 Algorithm Design and Validation of Motion Capture System
	3 Data Processing and Model Evaluation
		3.1 Data Processing and Model Evaluation
		3.2 Feature Extraction
		3.3 Feature Selection
		3.4 Activity Recognition Algorithm
		3.5 Experimental Results and Discussion
	4 Applications of Wearable Inertial Sensor Networks
		4.1 Human–Human Interactional Synchrony Analysis
		4.2 Smart Healthcare-Wearable Gait Analysis for Parkinson\'s Disease
		4.3 Intelligent Sports Performance Analysis: Investigating Horse-Rider Interaction through Body Sensor Network
	5 Summary
	References
A Sitting Posture Monitoring System in Wheelchair Users
	1 Introduction
	2 Related Works
		2.1 Postural Monitoring Devices
		2.2 Machine Learning Techniques Used for Posture Recognition
	3 i-KuXin: New Sitting Postural Monitoring System
		3.1 Monitoring Device Design
		3.2 Acquisition System
	4 Methodology of Experimental Trial Design
		4.1 Definition of Experimental Tests
		4.2 Database Generation
		4.3 Data Preprocessing
	5 Design of the Intelligent Postural Recognition System
		5.1 Postural Recognition Intelligent Techniques Selection
		5.2 System Training
	6 Results and Discussion
		6.1 Comparison of Results According to Technique
		6.2 Analysis of the Optimal Number of Sensors
	7 Conclusion
	References
A Comprehensive Review of Deep Learning for Activity Recognition
	1 Introduction
		1.1 Challenges in HAR
		1.2 Deep Learning in HAR
		1.3 Contributions
	2 Datasets
		2.1 Sensory Data
			Wearable Sensors
			Ambient Sensors
			Object Sensors
			Hybrid Sensors
	3 Challenges and Role of Deep Learning in HAR
		3.1 Feature Extraction
			Temporal Features
			Multimodal Features
		3.2 Annotation Scarcity
			Unsupervised Learning
			Semi-Supervised Learning
		3.3 Class Imbalance
		3.4 Distribution Discrepancies
		3.5 Composite Activity
			Fused Model
			Hierarchical Model
		3.6 Concurrent Activity
		3.7 Multi-Occupant Activity
		3.8 Computational Cost
		3.9 Privacy
		3.10 Explainability and Interpretability
	4 Conclusion
	References
Multi-User Activity Monitoring Based on Contactless Sensing
	1 Introduction
	2 Related Work
		2.1 Bodily Sensing Based on Wi-Fi
		2.2 Multi-User Activity Recognition with Wi-Fi Signals
	3 Proposed Method
		3.1 Mathematical Modeling of the Human Activity based on CSI Data
		3.2 Processing Workflow
	4 Experiments and Results
		4.1 Experiment Setup
		4.2 Experimental Results and Discussion
	5 Conclusion
	References
Efficient Sensing and Classification for Extended Battery Life
	1 Introduction
	2 Problem Statement
	3 Method
		3.1 Sensing and Computation Efficiency
			Sampling Frequency Determination
			Feature Selection
		3.2 Cascading Classifier
			Binary Classifiers
			Groups Classifier
			Classifier Flow
		3.3 Resource Analysis
	4 Validation
		4.1 Experimental Setup
		4.2 Binary Group Intensity classifiers
		4.3 Multi-Class Classification of Activities
		4.4 Cascading Classifier Performance
		4.5 PAMP2 Dataset Evaluation
		4.6 Power Consumption and Memory Usage Evaluation
		4.7 Comparison with State-of-the Art
	5 Conclusion, Discussion, and Future Work
	References
Unveiling the Potential of Machine Learning in Activity Recognition for Industry 4.0
	1 Introduction
	2 Background
		2.1 Machine Learning
		2.2 Human Activity Recognition
	3 Literature Review
		3.1 Overview of ML Techniques for Activity Recognition
		3.2 Benefits and Challenges of Using ML in Activity Recognition
	4 Impact and Applications of HAR in Industry 4.0
		4.1 Integration of AR and Industry 4.0
		4.2 Case Studies
	5 Future Directions and Conclusions
		5.1 Challenges and Future Directions
	6 Conclusions
	References
Human Activity Recognition: Trends and Challenges
	1 Introduction
		1.1 Contributions
	2 Trends and Challenges Related to Data in HAR
		2.1 Sensor-Based HAR
		2.2 Vision-Based HAR
	3 Trends and Challenges in HAR System Implementation
		3.1 Data Preprocessing
		3.2 Segmentation
		3.3 Feature Engineering
		3.4 Model Selection for Training
	4 Conclusion
	References
Index