Intro
Preface
Conference Organization
Contents
Human Activity Recognition
Data Fusion for Human Activity Recognition Based on RF Sensing and IMU Sensor
1 Introduction
2 Materials and Methods
2.1 IMU State Modeling
2.2 USRP State Modeling
3 The Proposed Structure Matrix to Data Fusion
3.1 Principal Component Analysis for Feature Extraction
4 Experimental Evaluation
5 Conclusion
References
Indoor Activity Position and Direction Detection Using Software Defined Radios
1 Introduction
2 Materials and Methods
2.1 Technical Specifications

2.2 Experimental Design
3 Results and Discussion
3.1 Detection Accuracy vs. Activity Position
3.2 Detecting Position, Direction of Movement, and Occupancy
4 Conclusion
References
Monitoring Discrete Activities of Daily Living of Young and Older Adults Using 5.8GHz Frequency Modulated Continuous Wave Radar and ResNet Algorithm
1 Introduction
2 Methodology
2.1 Data Acquisition
2.2 Classification Using Residual Neural Network
3 Results and Discussion
4 Conclusions and Future Work
References

Elderly Care
Human Activity Recognition Using Radar with an Open Dataset and Hybrid Maps
1 Introduction
1.1 Context
1.2 Current Research Progress
2 Methodology and Implementation
2.1 Dataset Information
2.2 Pre-processing
2.3 Feature Extraction and Classification
3 Results and Discussion
3.1 Hardware and Software Environment
3.2 Classification Results
3.3 Discussion
4 Conclusions and Future Work
References
Wireless Sensing for Human Activity Recognition Using USRP
1 Introduction
2 Related Work
3 Methodology
3.1 Data Collection

3.2 Machine Learning
4 Results and Discussion
4.1 Machine Learning Algorithms Comparison
4.2 Real Time Classification
4.3 Benchmark Dataset
5 Conclusion
References
Real-Time People Counting Using IR-UWB Radar
1 Introduction
2 Methodology
2.1 People Counting Algorithm
2.2 Experiment
3 Results
4 Conclusion
References
Bespoke Simulator for Human Activity Classification with Bistatic Radar
1 Introduction
2 Radar Simulation
3 Classification
3.1 Feature Extraction
3.2 Classification Algorithm
4 Classification Results

4.1 Monostatic Results
4.2 Bistatic Results
5 Discussion
5.1 Monostatic
5.2 Bistatic
6 Conclusion
References
Sensing for Healthcare
Detecting Alzheimer's Disease Using Machine Learning Methods
1 Introduction
2 Related Work
3 Methodology
3.1 Machine Learning Methods
3.2 Deep Learning Methods
4 Experimental Results and Discussions
4.1 Discussion
5 Conclusion
References
FPGA-Based Realtime Detection of Freezing of Gait of Parkinson Patients
1 Introduction
2 Related Work
2.1 Overview of Recent Methods of Detecting FoG