Applied Mathematical Modelling for Biomedical Robotics and Wearable Devices delves into the innovative convergence of mathematical frameworks and biomedical engineering. The book begins by exploring how advanced mathematical modelling underpins the development and optimization of robotic systems and wearable technologies tailored for medical applications. With a strong emphasis on practical implementation, it serves as a bridge between theoretical concepts and real-world engineering challenges in the healthcare sector. Readers will gain insights into the transformative role of mathematical techniques that drive precision, functionality, and human-centric design in cutting-edge medical technologies.
The book also covers interdisciplinary applications, integrating domains like biomechanics, sensor technology, and data analytics. By highlighting case studies and real-world scenarios, it showcases practical advancements in wearable devices that monitor health metrics and robotic systems that assist in surgical procedures.
1. Chapter 1. Introduction to biomedical robotics and wearable devices in healthcare - p. 1 2. Chapter 2. Mathematical modeling in healthcare engineering - p. 15 3. Chapter 3. Mathematical foundations and computational techniques for robotic motion: a unified approach - p. 37 4. Chapter 4. Advanced biosignal processing and emotion recognition through artificial intelligence - p. 59 5. Chapter 5. Optimization algorithms for design and control - p. 79 6. Chapter 6. Kinematics and dynamics of surgical robots - p. 99 7. Chapter 7. Sensor fusion and data processing for robot control - p. 117 8. Chapter 8. Motion planning and trajectory optimization for robots - p. 135 9. Chapter 9. Safety and stability analysis of robot systems - p. 157 10. Chapter 10. Rehabilitation robotics: transforming motor recovery and enhancing patient-centered therapies - p. 175 11. Chapter 11. Biosignal acquisition and processing for wearable sensors - p. 197 12. Chapter 12. Principal components with folds for feature extraction and anomaly detection in physiological data using deep neural network - p. 217 13. Chapter 13. A novel ensemble learning approach for leukemia prediction using hybrid machine learning techniques - p. 237 14. Chapter 14. Ethical considerations of biomedical robots and wearable devices - p. 253 15. Index - p. 273
Applied Mathematical Modelling for Biomedical Robotics and Wearable Devices delves into the innovative convergence of mathematical frameworks and biomedical engineering. The book begins by exploring how advanced mathematical modelling underpins the development and optimization of robotic systems and wearable technologies tailored for medical applications. With a strong emphasis on practical implementation, it serves as a bridge between theoretical concepts and real-world engineering challenges in the healthcare sector. Readers will gain insights into the transformative role of mathematical techniques that drive precision, functionality, and human-centric design in cutting-edge medical technologies.
The book also covers interdisciplinary applications, integrating domains like biomechanics, sensor technology, and data analytics. By highlighting case studies and real-world scenarios, it showcases practical advancements in wearable devices that monitor health metrics and robotic systems that assist in surgical procedures.
1. Chapter 1. Introduction to biomedical robotics and wearable devices in healthcare - p. 1 2. Chapter 2. Mathematical modeling in healthcare engineering - p. 15 3. Chapter 3. Mathematical foundations and computational techniques for robotic motion: a unified approach - p. 37 4. Chapter 4. Advanced biosignal processing and emotion recognition through artificial intelligence - p. 59 5. Chapter 5. Optimization algorithms for design and control - p. 79 6. Chapter 6. Kinematics and dynamics of surgical robots - p. 99 7. Chapter 7. Sensor fusion and data processing for robot control - p. 117 8. Chapter 8. Motion planning and trajectory optimization for robots - p. 135 9. Chapter 9. Safety and stability analysis of robot systems - p. 157 10. Chapter 10. Rehabilitation robotics: transforming motor recovery and enhancing patient-centered therapies - p. 175 11. Chapter 11. Biosignal acquisition and processing for wearable sensors - p. 197 12. Chapter 12. Principal components with folds for feature extraction and anomaly detection in physiological data using deep neural network - p. 217 13. Chapter 13. A novel ensemble learning approach for leukemia prediction using hybrid machine learning techniques - p. 237 14. Chapter 14. Ethical considerations of biomedical robots and wearable devices - p. 253 15. Index - p. 273
