Body area networks : smart IoT and big data for intelligent health management : 16th EAI International Conference, BODYNETS 2021, Virtual event, October 25-26, 2021, Proceedings / Masood Ur Rehman, Ahmed Zoha (eds.).

This book constitutes the refereed post-conference proceedings of the 16th International Conference on Body Area Networks, BodyNets 2021, held in October 2021. The conference was held virtually due to the COVID-19 pandemic. The 21 papers presented were selected from 44 submissions and issue new tech...

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Bibliographic Details
Corporate Author: EAI International Conference on Body Area Networks Online
Other Authors: Ur-Rehman, Masood (Editor), Zoha, Ahmed (Editor)
Format: eBook
Language:English
Published: Cham, Switzerland : Springer, 2022.
Series:Lecture notes of the Institute for Computer Sciences, Social Informatics, and Telecommunications Engineering ; 420.
Subjects:
Online Access:Click for online access
Table of Contents:
  • 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