Artificial Intelligence Development in Sensors and Computer Vision for Health Care and Automation Application E-Book
Minh Long Hoang
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- Herausgeber: Bentham Science Publishers
- Kategorie: Fachliteratur
- Sprache: Englisch
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.
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Seitenzahl: 242
Veröffentlichungsjahr: 2024
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FOREWORD
The book titled " Artificial Intelligence Development in Sensors and Computer Vision for Health Care and Automation Application" is an essential resource for anyone who wants a thorough understanding of the significant impact of artificial intelligence (AI) in electronics, specifically in sensor technology, computer vision, and machine learning. It provides comprehensive insights into the transformative role of AI in these areas, making it a valuable asset in the rapidly evolving area of AI. I wholeheartedly recommend this book for its insightful exploration of cutting-edge technologies and their applications.
In this well-organized research, Dr. Minh Long Hoang successfully leads readers through an illuminating exploration that encompasses subjects ranging from inertial measurement unit (IMU) sensors to light detection and ranging (lidar) and radio detection and ranging (radar). Through the lens of machine learning models, the author demonstrates how IMU data can be utilized for diverse purposes, such as process optimization, risk prevention, fault diagnosis, and human activity recognition. The integration of lidar and radar sensors into self-driving cars and AI robotic systems adds an extra layer of depth to the discussion, providing real-world examples of how these technologies are reshaping our future.
Moreover, the exploration of computer vision is equally captivating, focusing on image recognition, motion tracking, and object classification. The book also introduces robust AI algorithms like convolutional neural networks (CNN) and you only look once (YOLO), showcasing their applications in healthcare and automated vehicle control. Additionally, the book sheds light on the role of deep learning in human pose estimation (HPE) for rehabilitation support and also examines the uncertainty of deep neural network (DNN) predictions, particularly in IMU data.
The concluding chapter seamlessly ties together the comprehension gained from the earlier discussions, exploring the incorporation of machine learning into augmented reality (AR) within the automotive industry. It highlights the significant potential of AI in enhancing the design process, manufacturing, and customer experience in the automotive sector.
Overall, this book is highly recommended for professionals, researchers, and students seeking a comprehensive and up-to-date knowledge of the symbiotic relationship between AI, sensors, and computer vision. The book not only demystifies complex concepts but also inspires readers to explore the limitless possibilities that arise at the intersection of these transformative technologies.
PREFACE
Nowadays, artificial intelligence is playing an essential role in electronics, which demands potential innovations to enhance the performance and quality of digital applications. This book focuses on sensor technology and computer vision, where machine learning (ML) and deep learning (DL) are able to utilize input data and images for prediction, classification, and data visualization.
The initial chapters discuss the indepth research on data utilization in AI from various sensors, especially IMU (Inertial Measurement Unit), light detection and ranging (lidar), and radio detection and ranging (radar). IMU sensor is a common and powerful sensor providing motion data from accelerometers, gyroscopes, and magnetometers. With MEMS (Micro-electromechanical Systems) technology, the IMU sensors are compacted in a small size, with lower power consumption and high-quality factors. ML models handle these IMU data for process optimization, risk prevention, product improvement, fault diagnosis, human activity recognition, and automation. Furthermore, IMU data can be combined with Lidar and radar sensors to detect objects and navigate their surroundings in self-driving cars and AI robotic systems to avoid obstacles or pick up the demanded items. In addition, reinforcement learning algorithms play an important role in self-driving robots, together with simultaneous localization and mapping (SLAM) technology for high-resolution 3D maps of the environment.
On the other hand, computer vision has been developed for image recognition, motion tracking, and object classification. Many electronic devices can implement robust AI algorithms, such as convolutional neural networks (CNN), you only look once (YOLO), etc., to support healthcare and automated vehicle control. Moreover, deep learning also provides solutions for human pose estimation (HPE), which evaluates human posture to support people in rehabilitation.
After deep analysis and research on classification and computer vision, ML regression can be taken into account in terms of prediction uncertainty. The aim is to examine the uncertainty of deep neural network (DNN) prediction, specifically in MEMS IMU data in this case. From this study, we are able to have a profound view of ML applications for high-technology sensors.
The last chapter discusses the incorporation of ML into augmented reality (AR) in the automotive industry. AR adopts the existing real-world environment and transfers virtual information to the top, practically enhancing the car industry in terms of the design process, manufacturing, and customer experience. The techniques discussed in previous chapters will be linked to this part via AI applications in AR, such as object recognition, SLAM, HPE, gesture recognition, and DL models.
Based on the above contents, this book includes the following chapters:
Current State, Challenges, and Data Processing of AI in Sensors and Computer Vision.Human Activity Recognition and Health Monitoring by Machine Learning Based on IMU SensorsReinforcement Learning in Robot Automation by Q-learning.Deep Learning Techniques for Visual Simultaneous Localization and Mapping Optimization in Autonomous RobotDeep Learning in Object Detection for the Autonomous CarHuman Pose Estimation for Rehabilitation by Computer VisionPrediction Uncertainty of Deep Neural Network in Orientation Angles from IMU SensorsMachine Learning in Augmentation Reality for Automotive Industry.This book depicts the input data processing, AI model structure, training process, model test/validation, and final performance of the whole system in use. After reading this book, readers will comprehend the working principles, pros, and cons of AI technology in the highly trending topics of the scientific field.
Current State, Challenges, and Data Processing of AI in Sensors and Computer Vision
Minh Long HoangAbstract
The first chapter of the book explores the transformative applications of artificial intelligence (AI) in sensor technology and computer vision, focusing on human activity recognition, health monitoring, medical imaging, and autonomous vehicles within the automotive industry. It highlights the substantial advancements AI brings to these fields, particularly emphasizing the roles of machine learning (ML) and deep learning (DL), a subset of ML. In the field of human activity recognition and health monitoring, AI's ability to enhance accuracy and efficiency is thoroughly examined. The discussion extends to medical imaging, where ML and DL techniques significantly improve diagnostic processes and patient outcomes. The chapter also delves into the automotive industry, showcasing AI's impact on enabling self-driving cars and optimizing manufacturing processes. Each section provides detailed insights into the potential capabilities of ML and DL, illustrating AI's role as a game-changer that revolutionizes traditional methods. The narrative underscores the transformative power of these technologies, driving innovation and creating new opportunities across various domains. Additionally, the chapter addresses the challenges faced in the construction and operation of ML models. It analyzes difficulties such as data quality issues, computational resource demands, and algorithmic training complexities, offering a balanced perspective on the promises and hurdles of AI deployment. The chapter concludes with an in-depth discussion on sensor data collection and processing and case studies to demonstrate AI applications in real life. This section covers methodologies for gathering high-quality sensor data, pre-processing techniques, and integrating this data into AI frameworks, setting the stage for understanding AI's profound impact and technical intricacies.
INTRODUCTION
Recently, the integration of AI [1-4] with sensors has completely changed the potential of many industries. Sensors collect massive volumes of physical world data, and AI algorithms can process this data to derive insightful conclusions and make prompt judgments. For instance, in the manufacturing industry, sensors and AI might provide predictive maintenance by spotting irregularities in the behavior of the machinery and foreseeing probable failures.
Computer vision [5, 6] is a subfield of AI that is primarily concerned with endowing machines with the capacity to analyze and understand visual information derived from their surroundings. This technology has found utility in various industries, including healthcare (specifically in medical image analysis), automotive (particularly in the development of autonomous vehicles), retail (specifically in the establishment of cashier-less stores), agriculture (specifically in crop monitoring), and other sectors. AI-enabled computer vision algorithms have the capability to discern objects, patterns, and contextual information inside images and videos.
ML IN HUMAN ACTIVITY RECOGNITION AND HEALTH MONITORING
The potential for ML applications in human activity identification [6, 7] to reveal information about a person's behavior, health, and well-being has given these applications much relevance. ML and DL [8, 9] are essential for recognizing human activities for the following reasons:
• Accuracy and Precision: ML and DL algorithms can recognize various human behaviors with high degrees of accuracy. Since they can distinguish between various activities that have comparable sensor signals, identification is more accurate and dependable.
• Human actions can be complicated and entail many phases or variants. These complex patterns may be recognized by ML algorithms, which can then adjust to various activity circumstances.
• Real-Time Monitoring: Systems that can recognize ML activities may analyze data in real time, enabling quick feedback and action. Applications, including sports training, rehabilitation, and emergency response, can all benefit from this.
• Customization: ML algorithms may be trained to detect user-specific activity patterns, personalizing and adapting the recognition process to each user's requirements and habits.
• Health and Well-being: Wearable technology and smartphones with activity detection capabilities may track daily activities, workout regimens, sleep habits, and more. People can use this information to guide better lifestyle decisions and enhance their general well-being.
• Care for the Elderly: ML-based activity recognition is necessary for remote supervision of older people who live alone. Caregivers and family members can ensure seniors' safety by being informed of any odd or possibly hazardous actions.
• Fall Detection: It is essential for the care of older people that ML algorithms be able to identify the patterns connected to falls. Early diagnosis of falls can result in quicker medical intervention and better results.
• Physical Rehabilitation: Activity identification and DL coupled can assist patients in recuperating from accidents or operations and create individualized rehabilitation regimens. It guarantees that workouts are carried out correctly and track progress. The Human Pose Estimation [10] technique has been used widely in rehabilitation to monitor whether the patient moves correctly.
• Safety at Work: ML-powered activity recognition can track employees' movements and behaviors in commercial settings to spot possible risks and avert mishaps.
• Sports and fitness: ML-based activity detection is helpful in tracking fitness and training in sports. Athletes may get feedback on their performance, monitor their development, and make data-driven adjustments.
Overall, ML applications in human activity identification offer a wide variety of advantages, from strengthening many sectors and research domains to improving personal health and safety. The capability to identify human activity reliably and effectively has the potential to change how we engage with technology, keep track of our actions, and enhance our general quality of life.
In wearable technology, ML and DL have ushered in a new era of innovation. These cutting-edge techniques are crucial in transforming straightforward wearables into intelligent companions that adapt to our wants, monitor our health, and improve our general well-being as technology integrates seamlessly into every aspect of our lives. ML is unlocking the potential for wearables to track physical activity, predict health outcomes, offer individualized recommendations, and enable new levels of user interaction and engagement. These technologies have the capability to efficiently process and interpret extensive quantities of data that are gathered by sensors that are integrated within these devices. The integration of wearable technologies and advanced artificial intelligence is revolutionizing our interactions with the surrounding environment.
ML IN AUTONOMOUS VEHICLES
The automobile industry is seeing a sharp increase in demand for AI-powered computer vision systems. Autonomous cars rely on cameras and sensors to navigate, understand their environment, and make split-second decisions. These systems require real-time detection of pedestrians, other cars, traffic signs, and ba- rriers. Enhancing the security and dependability of self-driving automobiles requires advances in AI.
Due to the complex and dynamic nature of driving situations, ML technologies are crucial for creating and operating autonomous cars [11, 12]. For human-autonomous cars, machine learning is essential for a number of reasons:
• Perception and Sensor Fusion: Autonomous cars use a variety of sensors to detect their environment, including cameras, LiDAR, radar, and ultrasonic sensors. In order to effectively identify and distinguish objects, pedestrians, cars, road signs, and barriers in real time, ML algorithms can interpret and combine data from various sensors.
• Environmental Understanding: ML algorithms are capable of deciphering complicated sceneries and circumstances, allowing the car to comprehend various road conditions, weather, illumination, and traffic scenarios and react appropriately.
• Adaptive Behavior: ML enables self-driving cars to change their actions in response to their surroundings and context. In order to drive safely and effectively in everyday settings, one must be flexible.
• Predictive Analysis: ML algorithms can examine previous data to forecast possible dangers and predict the behavior of other road users. These predictive capabilities improve the vehicle's capacity to take proactive action.
• Map-making and localization: ML can help with high-definition map creation and upkeep as well as precise localization of the vehicle in its surroundings. For accurate navigation and safe movement, this is essential.
• Path Planning and Decision-Making: ML algorithms can provide the best pathways and trajectories while taking into account variables like traffic laws, road geometry, and other drivers' actions. Decision-making becomes effective and secure as a result.
• Real-Time Response: ML provides real-time data processing and prompt decision-making, enabling the car to respond to circumstances that are changing quickly, including unanticipated obstructions or sudden lane changes. Maneuvers that need complexity include merging onto roads, changing lanes, and negotiating junctions, which are all tasks that autonomous cars must complete. These moves may be learned and carried out by ML-based systems by examining a large quantity of training data.
• Human-Like Behavior: By training ML algorithms to imitate human-like driving behavior, the activities of the vehicle become more predictable and relatable to other human drivers, pedestrians, and cyclists.
• Safety and Redundancy: By seeing probable system failures or sensor faults and taking the necessary precautions to ensure safe operation, ML-based systems can improve safety.
• Vehicles may continually learn from fresh data and experiences thanks to ML. The system as a whole will become safer and more effective as more autonomous cars operate on the road.
• Accessibility: Autonomous cars driven by ML have the potential to offer transportation options for people who are unable to drive due to age, physical limitations, or other factors.
ML can considerably lessen the influence of human mistakes, which primarily contribute to traffic accidents. ML-equipped autonomous cars can reduce human error-related accidents such as distracted driving, weariness, and drunk driving.
AUTOMOTIVE INDUSTRY
AI-powered sensors and computer vision simplify manufacturing and industrial manufacturing processes. Some applications of these technologies include process optimization, quality control, and predictive maintenance. Real-time defect detection, downtime reduction, and general manufacturing efficiency can all be enhanced by AR technology [13-15] to improve different areas of the production process within the automobile sector is referred to as “real-time augmentation applications in automotive manufacturing”. To increase productivity, accuracy, and teamwork in industrial processes, these apps make use of real-time data and computer-generated information. Examples of real-time enhancement applications in the automobile industry are as follows:
• Assembly Guidance: AR may show real-time visual instructions and guidance to assembly line employees superimposed on actual components. Each stage of the assembly process is carried out correctly, which helps to lower mistakes and boost productivity.
• Quality Control: As components travel down the assembly line, AR systems may overlay virtual inspection locations on top of them. Workers are better able to visually detect flaws or abnormalities and take immediate remedial action to improve the final product's quality.
• Repair and upkeep: AR can help technicians complete maintenance and repair chores. Technicians can adequately identify problems and carry out repairs quickly by superimposing step-by-step instructions on their field of vision.
• Design validation: Using augmented reality, engineers and designers may see virtual prototypes superimposed on real cars or parts that enable people to evaluate design ideas, make changes, and spot possible design problems in real time.
• Layout designing: By superimposing digital models of machinery, manufacturing lines, and supplies onto the actual environment, AR may help design and optimize factory layouts, which ensures that space and resources are used effectively.
• Collaborative Design Reviews: Using AR technologies, teams may work together to design vehicles while each member can simultaneously see and interact with the virtual design aspects. As a result, real-time feedback and decision-making are made more accessible.
• Supply Chain Management: Real-time tracking and management of the flow of materials and components can be aided by augmented reality (AR). Digital data may be overlaid on real-world items to simplify logistics and lower mistake rates.
• Vehicle customization: In sectors where vehicle customization is widespread, AR can assist consumers in making decisions by allowing them to see various configurations and choices in real time.
• Efficiency Monitoring: AR systems may show employees and supervisors on the manufacturing floor real-time performance metrics, production rates, and key performance indicators. This feature encourages openness and enables prompt modifications to maximize output.
• Remote Support: Using AR technology, specialists may support on-site personnel remotely. They may train staff on difficult jobs or troubleshooting by sharing their viewpoint and annotating them with instructions.
• Real-time augmentation apps are used in the automobile industry to improve several production process phases. AR helps the automobile manufacturing sector be more efficient, accurate, collaborative, and productive by offering real-time information, visual instructions, and interactive experiences.
Generally, by combining cutting-edge AI technologies with wearable technology, we are changing how we view and use technology and bridging previously unthinkable gaps between human interaction and digital intelligence. In this investigation, we look into the interesting confluence of deep learning, machine learning, and wearable technology, revealing the synergies that are advancing us toward a time when our devices will be worn and fully comprehended.
CHALLENGES OF THE MACHINE LEARNING APPLICATION
Over the past few decades, machine learning, a branch of artificial intelligence, has made enormous strides. Its uses have spread to numerous fields, including industry, banking, healthcare, and entertainment. Despite the revolutionary advances it has brought about, machine learning still faces significant challenges. The usefulness and dependability of these applications may be hampered by various difficulties businesses and researchers must overcome as they continue incorporating machine learning into their applications. This chapter will examine some major issues with machine learning applications [16-20] and how they affect different domains.
Data Quality and Availability
High-quality data is one of the critical cornerstones of practical machine learning. The caliber and volume of data that a machine learning model is trained on significantly impact how well it performs. Garbage in, garbage out – the model's predictions will probably be unreliable or deceptive if the training data is erratic, lacking, or skewed. Finding accurate and pertinent data may be difficult, mainly when working with real-world datasets that are frequently disordered and unstructured.
Data accessibility can also be problematic, especially in fields where data gathering is costly or time-consuming. For instance, due to privacy issues and the requirement for professional annotation in the medical industry, acquiring labeled medical pictures for training deep learning models might be challenging. In order to make sure the data truly represents the situation at hand, addressing data quality and availability concerns frequently entails data pretreatment, augmentation, and collaboration with domain experts.
Model Interpretability
Many machine learning models, particularly deep neural networks, are regarded as “black boxes” because of their intricate designs and the difficulty in comprehending how they make decisions. This lack of interpretability can be a significant barrier in situations where it is essential to comprehend the rationale behind a model's predictions. Model interpretability becomes crucial in industries like banking and healthcare, where decisions can have large real-world repercussions.
Methods to improve the interpretability of machine learning models are continuously being researched. Methods such as feature significance analysis, attention processes, and model-specific interpretability algorithms have been developed to show how models make decisions. Model performance and interpretability are difficult to reconcile, and the particular application and its needs frequently influence the decision.
Generalization and Overfitting
In order to produce precise predictions on novel, unforeseen data, machine learning models are created to discover patterns from data. Overfitting, a condition when models remember training data instead of discovering its underlying patterns, can harm models. As a result, the model performs poorly on fresh data since it is unable to generalize its expertise. Conversely, underfitting happens when the model is too straightforward to account for the complexity of the data.
The bias-variance trade-off is a problem where underfitting and overfitting must be balanced. Some tactics to address these issues include cross-validation, regularization approaches, and cautious feature selection. For a model to be useful in practical applications, it must be able to generalize well to many contexts.
Scalability and Resource Constraints
Complex machine learning models frequently need a lot of computer power to train and deploy. This presents problems when real-time applications are implemented or models are deployed on devices with limited resources, such as smartphones or edge devices.
Model compression methods like pruning and quantization, which shrink the model's size without significantly sacrificing speed, are used to address these issues. In order to overcome resource limitations, edge computing, in which processing takes place closer to the data source, is gaining popularity. Cloud services and distributed computing frameworks provide scalable solutions, but balancing model complexity and resource usage is still challenging.
