Artificial Intelligence Development in Sensors and Computer Vision for Health Care and Automation Application.

Format:

Book

Author/Creator:

Hoang, Minh Long.

Language:

English

Physical Description:

1 online resource (179 pages)

Edition:

1st ed.

Place of Publication:

Sharjah : Bentham Science Publishers, 2024.

Summary:

Artificial Intelligence Development in Sensors and Computer Vision for Health Care and Automation Application explores the power of artificial intelligence (AI) in advancing sensor technologies and computer vision for healthcare and automation. Covering both machine learning (ML) and deep learning (DL) techniques, the book demonstrates how AI optimizes prediction, classification, and data visualization through sensors like IMU, Lidar, and Radar. Early chapters examine AI applications in object detection, self-driving vehicles, human activity recognition, and robot automation, featuring reinforcement learning and simultaneous localization and mapping (SLAM) for autonomous systems. The book also addresses computer vision techniques in healthcare and automotive fields, including human pose estimation for rehabilitation and ML in augmented reality (AR) for automotive design. This comprehensive guide provides essential insights for researchers, engineers, and professionals in AI, robotics, and sensor technology. Key Features:- In-depth coverage of AI-driven sensor innovations for healthcare and automation.- Applications of SLAM and reinforcement learning in autonomous systems.- Use of computer vision in rehabilitation and vehicle automation.- Techniques for managing prediction uncertainty in AI models. Readership:Graduate, undergraduate students, researchers, working professionals, and general readers.

Contents:

Cover

Title

Copyright

End User License Agreement

Contents

Foreword

Preface

Current State, Challenges, and Data Processing of AI in Sensors and Computer Vision

INTRODUCTION

ML IN HUMAN ACTIVITY RECOGNITION AND HEALTH MONITORING

ML IN AUTONOMOUS VEHICLES

AUTOMOTIVE INDUSTRY

CHALLENGES OF THE MACHINE LEARNING APPLICATION

Data Quality and Availability

Model Interpretability

Generalization and Overfitting

Scalability and Resource Constraints

Continuous Learning and Adaptation

Ethical and Fair Use of Machine Learning

SENSOR DATA COLLECTION AND PROCESSING FOR INTELLIGENT MODELS

CASE STUDY

CONCLUSION

REFERENCES

Human Activity Recognition and Health Monitoring by Machine Learning Based on IMU Sensors

DATA ACQUISITION AND INPUT FEATURES

REGULAR ML MODELS FOR CLASSIFICATION

Logistic Regression

Linear Discriminant Analysis

K-Nearest Neighbor Classification

Classification and Regression Trees

Naive Bayes

Support Vector Machines

Random Forest

SUITABLE ALGORITHM SELECTION

DATA SPLITTING

TRAINING, VALIDATION, AND TEST

CONFUSION MATRIX AND PERFORMANCE ESTIMATION

Reinforcement Learning in Robot Automation by Q-learning

Q-LEARNING WORKING PRINCIPLE

AMR TRAINING WITH Q-LEARNING

Best Route Learning

Obstacle Avoidance

REWARD

TERMINATION CONDITION FOR AN EPISODE

Deep Learning Techniques for Visual Simultaneous Localization and Mapping Optimization in Autonomous Robots

VSLAM ARCHITECTURE

Sensor Data

Visual Odometry

The Backend Optimization

Map Reconstruction

Loop Closure and Optimization

RELATED WORKS

Feature-based Method

The Direct Method

DN in VSLAM

CNN FOR VISUAL PERCEPTION

Input Layer.

Convolution Layer

Activation Layer

Pooling Layer

Flattening

Fully Connected Layers

Output Layer

LONG SHORT-TERM MEMORY IN VSLAM

NEURAL NETWORK IN POSE ESTIMATION

GCNS IN VSLAM

MPNNS IN VSLAM

MPNNs Structure

MPNNs Advantages in VSLAM

GIN IN VSLAM

GINs Structure

GINs Applications in VSLAM

Deep Learning in Object Detection for the Autonomous Car

LIDAR

GLOBAL DATA AUGMENTATION

POINTPILLARS IN PRACTICAL CASE

RADAR FOR PEDESTRIAN AND BICYCLIST CLASSIFICATION USING DEEP LEARNING

CNN MODEL

YOLO APPLICATION TO CAMERA ON AUTONOMOUS CAR

Convolutional Operation

Filters

Stride

Padding

Activation Function

Output Volume

Residual Blocks

Bounding Box Regression

Intersection Over Unions (IOU)

Output with Confidence Score

Human Pose Estimation for Rehabilitation by Computer Vision

BLAZEPOSE

ML Pipeline for Pose Tracking

BlazePose Working Principle

Machine Learning for Activity Recognition

OPENPOSE

MOVENET

Architecture

Training data

Evaluation Data

OPENPIFPAF

HUMAN POSE METRICS

REAL CASE EVALUATION

Prediction Uncertainty of Deep Neural Network in Orientation Angles from IMU Sensors

MONTE CARLO DROPOUT

DATA ANALYSIS

Pearson Correlation Coefficient Formula

Unique Value Number Per Feature

DEEP LEARNING MODEL

REAL-WORLD APPLICATIONS

Machine Learning in Augmented Reality for Automotive Industry

AUGMENTATION REALITY CONCEPT

MACHINE LEARNING IN AR FOR CAR INDUSTRY

Car Design Process

Gesture Recognition in AR

Semantic Segmentation Models

Automotive Manufacturing

Automotive Customer Experience.

CHALLENGES OF MACHINE LEARNING IN AUGMENTATION REALITY FOR THE AUTOMOTIVE INDUSTRY

CHALLENGES AND FUTURE OF ML AND AR

Subject Index

Back Cover.

Notes:

Description based on publisher supplied metadata and other sources.

Other Format:

Print version: Hoang, Minh Long Artificial Intelligence Development in Sensors and Computer Vision for Health Care and Automation Application

ISBN:

9789815313055

OCLC:

1481990898