Design and development of AI-driven biomedical sensors and devices and their optimization for continuous health monitoring
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A. Aalan Babu
Abstract
Thanks to the developments in the biomedical spherebiomedical sphere, sensor technologies has advanced rapidly, thus making the monitoring of continuous and real-time physiological parameters possible. This chapter reviews continuous health monitoring biomedical sensors created through the use of AI, the major advancements, and the trends. It goes further and outlines the key kinds of sensors such as the optical, chemical, and mechanical types, and their uses, all the while explaining the innovations bringing about change. The chapter also analyzes the main characteristics of hybrid as well as multimodal sensors that are composed of various sensing modes for the purposes of providing complete monitoring solutions. Particular attention is paid to the prospecting long-term monitoring solutions needed for geriatric care, as well as issues that are related to chronic illness management in elderly people. The discussion includes considerations of the applicability and affordability of sensor technologies, the two aspects that often define the universality and implementation of these technologies. By outlining these aspects in considerable detail, the chapter is intended to present useful information for the further development of biomedical sensor for enhancing health treatment results.
Abstract
Thanks to the developments in the biomedical spherebiomedical sphere, sensor technologies has advanced rapidly, thus making the monitoring of continuous and real-time physiological parameters possible. This chapter reviews continuous health monitoring biomedical sensors created through the use of AI, the major advancements, and the trends. It goes further and outlines the key kinds of sensors such as the optical, chemical, and mechanical types, and their uses, all the while explaining the innovations bringing about change. The chapter also analyzes the main characteristics of hybrid as well as multimodal sensors that are composed of various sensing modes for the purposes of providing complete monitoring solutions. Particular attention is paid to the prospecting long-term monitoring solutions needed for geriatric care, as well as issues that are related to chronic illness management in elderly people. The discussion includes considerations of the applicability and affordability of sensor technologies, the two aspects that often define the universality and implementation of these technologies. By outlining these aspects in considerable detail, the chapter is intended to present useful information for the further development of biomedical sensor for enhancing health treatment results.
Chapters in this book
- Frontmatter I
- Contents V
- List of contributors VII
- Blockchain technology to secure medical data sharing in machine learning applications ensure privacy and integrity 1
- AI-powered sensors and devices for sustained health tracking 39
- Development of AI-driven biomedical sensors and devices optimization for continuous health monitoring 89
- Design and development of AI-driven biomedical sensors and devices and their optimization for continuous health monitoring 131
- Machine learning-driven personalized medicine: customized drug delivery systems and patient-specific material applications 193
- Personalized medicine using customized drug delivery systems and patient-specific material solutions, enabled by machine learning algorithms 239
- AI-driven drug design exploring molecular docking and lead optimization using machine learning algorithms 297
- Machine learning models for predicting drug toxicity and side effects 335
- Machine learning innovations in biomedical materials from drug discovery to personalized medicine 395
- High-throughput screening for novel medical materials: machine learning-enabled approaches 445
- Automated materials characterization using machine learning for screening biocompatible materials 489
- Machine learning algorithms for enhanced medical image analysis and diagnostics 541
- Transforming healthcare with machine learning 585
- Revolutionizing healthcare 635
- Index 687
- De Gruyter Series in Advanced Mechanical Engineering
Chapters in this book
- Frontmatter I
- Contents V
- List of contributors VII
- Blockchain technology to secure medical data sharing in machine learning applications ensure privacy and integrity 1
- AI-powered sensors and devices for sustained health tracking 39
- Development of AI-driven biomedical sensors and devices optimization for continuous health monitoring 89
- Design and development of AI-driven biomedical sensors and devices and their optimization for continuous health monitoring 131
- Machine learning-driven personalized medicine: customized drug delivery systems and patient-specific material applications 193
- Personalized medicine using customized drug delivery systems and patient-specific material solutions, enabled by machine learning algorithms 239
- AI-driven drug design exploring molecular docking and lead optimization using machine learning algorithms 297
- Machine learning models for predicting drug toxicity and side effects 335
- Machine learning innovations in biomedical materials from drug discovery to personalized medicine 395
- High-throughput screening for novel medical materials: machine learning-enabled approaches 445
- Automated materials characterization using machine learning for screening biocompatible materials 489
- Machine learning algorithms for enhanced medical image analysis and diagnostics 541
- Transforming healthcare with machine learning 585
- Revolutionizing healthcare 635
- Index 687
- De Gruyter Series in Advanced Mechanical Engineering
