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

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.

The study of human activity recognition (HAR) holds significant importance within wearable technology and ubiquitous computing, driven by the increasing ubiquity of inertial measurement unit (IMU) sensors embedded in devices like smartphones, smartwatches, and fitness trackers. The effective classification and recognition of human actions are crucial for various applications, including health monitoring, fitness tracking, and personalized user experiences. This study comprehensively examines the advancements in HAR by applying machine learning (ML) methodologies to data collected from IMU sensors. We explore seven powerful ML algorithms that have been pivotal in transforming raw sensor data into actionable insights for activity classification. These algorithms include decision trees, random forests, support vector machines (SVM), k-nearest neighbors (KNN), artificial neural networks (ANN), convolutional neural networks (CNN), and long short-term memory networks (LSTM). Each algorithm is assessed based on its ability to accurately process and classify various human activities, highlighting their strengths and limitations in different scenarios. Moreover, the study delves into the critical role of evaluation metrics and the confusion matrix in validating the performance of these ML models. Metrics such as accuracy, precision, recall, F1 score, and specificity are examined to provide a holistic view of the model's efficacy. The confusion matrix is emphasized as a tool for understanding the true positive, false positive, true negative, and false negative rates, offering insights into the practical performance of the models in realworld applications. Through this detailed investigation, we aim to shed light on the current state of HAR and the potential future directions for research and development in this dynamic field.

This chapter demonstrates the pivotal role of reinforcement learning (RL), specifically employing the Q-learning algorithm, in enhancing the capabilities of autonomous mobile robots (AMRs) for transportation tasks. The focus is on enabling the robot to learn and execute two critical tasks autonomously. The first task involves the robot learning the optimal path to transport an object from its current location to a specified destination. The second task requires the robot to adeptly avoid obstacles encountered along the way, ensuring safe and efficient navigation. The robot is equipped with advanced sensors, including light detection and ranging (Lidar) and inertial measurement unit (IMU) sensors, to accomplish these tasks. The Lidar sensor provides detailed scanning of the surrounding environment, allowing the robot to detect and map obstacles, while the IMU sensors aid in precise positioning and movement tracking. These sensory inputs are crucial for the robot to understand its environment and make informed decisions accurately. The chapter elucidates the working principles of the Q-learning algorithm, a model-free RL technique that enables the robot to learn optimal actions through trial-and-error interactions with its environment. The training process involves the robot being rewarded for successful task completion and penalized for undesirable actions, gradually refining its policy to maximize cumulative rewards. Through detailed explanations and practical demonstrations, this research showcases how Q-learning facilitates the robot's learning process, enabling it to master the tasks of path planning and obstacle avoidance. The insights gained from this study highlight the potential of RL in advancing the autonomy and efficiency of mobile robots in transportation and other applications, paving the way for further innovations in the field.

In the previous chapter, we explored the application of reinforcement learning to autonomous robots, focusing on the indoor maps constructed using the Simultaneous Localization and Mapping (SLAM) technique. Visual SLAM (VSLAM) is highlighted as a cost-effective SLAM system that leverages 3D vision to execute location and mapping functions without limitations on distance detection range. VSLAM can also incorporate inertial measurement unit (IMU) measurements to enhance the accuracy of the device's pose estimation, particularly in scenarios where visual data alone is insufficient, such as during rapid movements or temporary visual obstructions. This chapter shifts the focus to integrating deep learning (DL) with VSLAM to boost its accuracy and performance. DL can significantly enhance VSLAM by providing semantic understanding, object detection, and loop closure detection, improving the system's overall situational awareness. We delve into six DL models that are pivotal in advancing VSLAM capabilities: Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, Neural Networks (NNs), Graph Convolutional Networks (GCNs), Message Passing Neural Networks (MPNNs), and Graph Isomorphism Networks (GINs). Each of these models offers unique advantages for VSLAM. CNNs are adept at processing visual information and extracting spatial features, while LSTMs excel in handling temporal dependencies, making them suitable for dynamic environments. NNs provide a flexible framework for various learning tasks, and GCNs effectively capture spatial relationships in graph-structured data. MPNNs and GINs enhance the ability to process and analyze complex graph-based data, improving the robot's understanding of its environment. This chapter provides a comprehensive overview of how these DL models can be integrated with VSLAM to achieve more robust and efficient autonomous navigation. Through detailed explanations and practical examples, we illustrate the potential of combining DL with VSLAM to advance the field of autonomous robotics.

This chapter explores the practical application of artificial intelligence (AI) techniques in self-driving cars, mainly focusing on object recognition. Deep learning has emerged as a powerful tool for object detection, playing a crucial role in processing data from lidar, radar, and video cameras. These three technologies are essential components of autonomous vehicles, providing critical obstacle information that enables the automatic system to execute appropriate actions based on the received data. We delve into three advanced techniques that enhance object detection capabilities in autonomous cars: PointPillars for Lidar, Convolutional Neural Networks (CNNs) for radar, and You Only Look Once (YOLO) for video cameras. PointPillars is a state-o- -the-art technique that efficiently processes lidar point cloud data to detect objects, offering high accuracy and real-time performance. This method transforms point cloud data into a structured format that is easier for neural networks to process, facilitating rapid and accurate object detection. For radar, Convolutional Neural Networks (CNNs) are employed to leverage their strength in processing grid-like data structures. CNNs can effectively handle the spatial information captured by radar sensors, enabling precise detection and classification of objects, even in challenging conditions such as poor visibility or adverse weather. In video camera applications, the YOLO (You Only Look Once) algorithm is utilized for its ability to detect and classify multiple objects within a single frame quickly. YOLO's real-time detection capability and high accuracy make it an ideal choice for video-based object detection in self-driving cars. This chapter provides a comprehensive overview of these cutting-edge deep learning techniques, demonstrating their pivotal role in advancing the object recognition capabilities of autonomous vehicles. Through detailed discussions and examples, we highlight how these methods contribute to the development of safer and more reliable self-driving car systems. 

Human pose estimation (HPE) is a valuable tool for rehabilitation, providing critical insights into the body's posture and movements. Both patients and therapists can significantly benefit from this technology, which enhances various aspects of the rehabilitation process by offering precise and real-time feedback on body mechanics. This research explores four well-known models in HPE: BlazePose, OpenPose, MoveNet, and OpenPifPaf. Each model is examined in detail, focusing on their architecture and working principles. BlazePose is renowned for its efficiency and accuracy, making it suitable for real-time performance applications. OpenPose is a comprehensive framework that detects multiple body parts, offering a detailed human posture analysis. MoveNet is designed for high-speed applications, providing quick and accurate pose estimation, while OpenPifPaf excels in producing precise keypoint detection, which is crucial for detailed posture analysis. The comparison between these models is demonstrated through practical cases of rehabilitation exercises. Since rehabilitation often requires exercises to be performed slowly and deliberately to ensure safety and effectiveness, this study emphasizes model accuracy over speed. We can assess the models in actual rehabilitation scenarios' reliability and suitability for different rehabilitation exercises. This research aims to provide a thorough understanding of how each HPE model operates and their respective strengths and limitations in rehabilitation. Through detailed analysis and real-world comparisons, we highlight the potential of HPE technology to improve rehabilitation outcomes by offering accurate, real-time feedback to both patients and therapists. This feature can lead to more effective rehabilitation programs tailored to the specific needs of individual patients. 

The chapter delves into how the Monte Carlo Dropout method is integrated into the neural network, enabling the network to estimate uncertainty by performing multiple forward passes during prediction. This technique allows for a probabilistic interpretation of the model's outputs, providing insight into the confidence levels associated with each prediction. Furthermore, the research examines the prediction uncertainties of Euler angles on the X, Y, and Z axes. The study aims to determine the deep learning model's confidence level for each orientation angle by analyzing these uncertainties. This point is particularly important in applications where precise orientation data is crucial, such as robotics, autonomous vehicles, and motion capture systems. The results are presented in a comparative format, highlighting the differences in uncertainty levels across the three axes. This comparison provides knowledge about the model's robustness and reliability in predicting orientation angles. The chapter underscores the importance of accounting for prediction uncertainty in neural networks, as it enhances the model's reliability and provides valuable information for decisionmaking processes. By providing a comprehensive analysis of uncertainty prediction in Inertial Measurement Unit (IMU) sensor data, this chapter contributes to the broader field of artificial intelligence (AI) by emphasizing the significance of uncertainty estimation in regression tasks. This approach not only improves model performance but also increases the trustworthiness of AI systems in various important applications.

The augmented reality (AR) field has experienced substantial progress in recent years, driven by breakthroughs in hardware, software, and computer vision techniques. Artificial intelligence (AI) integration has significantly enhanced AR, making it more accessible and expanding its practical applications across various industries, notably in automotive manufacturing. In this context, AR aids assembly processes by improving the efficiency and accuracy of assembly line workers. AR systems provide real-time guidance and feedback by incorporating object detection, tracking, and digital content overlay, increasing productivity and superior quality in automobile production. This chapter delves into the transformative role of AR in the automotive industry, highlighting its impact on the design process, manufacturing, and customer experience. Drawing on Machine Learning (ML) methodologies discussed in previous chapters, the chapter explores how AR technologies are employed to streamline complex assembly tasks, reduce human error, and enhance overall operational efficiency. The design process benefits from AR through enhanced visualization and prototyping, allowing for more precise and creative developments. In manufacturing, AR supports workers by overlaying critical information and instructions directly onto their field of view, facilitating faster and more accurate assembly operations. This real-time assistance boosts productivity and ensures that higher quality standards are met consistently. The chapter addresses the use of AR in enhancing the customer experience, from virtual showrooms to personalized, interactive user manuals, creating a more engaging and informative interaction with the product. By providing a comprehensive overview of AR's applications in the automotive sector, this chapter underscores the technology's potential to revolutionize industry practices. The integration of AI and AR not only enhances current manufacturing processes but also paves the way for innovative advancements in automotive design and customer engagement.