Biomedical Robots and Devices in Healthcare
Opportunities and Challenges for Future Applications
- 1st Edition - November 14, 2024
- Editors: Faiz Iqbal, Pushpendra Gupta, Vidyapati Kumar, Dilip Kumar Pratihar
- Language: English
- Hardback ISBN:9 7 8 - 0 - 4 4 3 - 2 2 2 0 6 - 1
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 2 2 0 7 - 8
Biomedical Robots and Devices in Healthcare: Opportunities and Challenges for Future Applications explores recent advances and challenges involved in using these techniques in hea… Read more
Purchase options
Institutional subscription on ScienceDirect
Request a sales quoteBiomedical Robots and Devices in Healthcare: Opportunities and Challenges for Future Applications explores recent advances and challenges involved in using these techniques in healthcare and biomedical engineering, offering insights and guidance to researchers, professionals, and graduate students interested in this area. The book covers key topics such as the current state-of-the-art in biomedical robotics and devices, the role of emerging technologies like artificial intelligence and machine learning, rehabilitation robotics, and the optimization techniques for optimal design and control. The book concludes by exploring the potential future developments and trends in the field of biomedical robotics and devices and their healthcare implications.
- Provides a comprehensive overview of the current state-of-the-art in biomedical robotics and devices, including a discussion of the various types of devices and robots that are being developed and used in healthcare settings
- Highlights the role of computational intelligence techniques such as artificial intelligence, machine learning, Fuzzy Logic, and evolutionary algorithms in the design, development, and the use of biomedical robots and devices, offering insights and guidance to professionals and students on these technologies
- Explores the potential future developments and trends in the field of biomedical robotics and devices and their implications for healthcare professionals and patients, providing a valuable resource for those looking to stay up-to-date on advancements in the field
Professionals and students in biomedical engineering and healthcare, including graduate/postgraduate, Ph.D., postdoctoral, and professors who are interested in investigating the application of computational intelligence techniques to healthcare and biomedical engineering in the context of Industry 4.0 advancements, Researchers, clinicians, and other professionals working in hospitals, research institutions, or academic environments, as well as students studying biomedical engineering or related subjects who wish to learn more about the role of computational intelligence in healthcare
- Biomedical Robots and Devices in Healthcare
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Chapter 1 Soft robotics and computational intelligence_ Transformative technologies reshaping biomedical engineering
- Abstract
- Keywords
- 1.1 Introduction
- 1.2 Soft robotics: Brief introduction
- 1.3 Potential and ongoing applications of soft robots
- 1.3.1 Surgical applications for soft robots
- 1.3.2 Exosuits
- 1.3.3 Prosthetics
- 1.3.4 Artificial organs
- 1.3.5 Assistive technologies
- 1.4 Limitations in soft robotics
- 1.5 Summary
- References
- Chapter 2 Advancements in biomedical devices: A comprehensive review
- Abstract
- Keywords
- 2.1 Introduction
- 2.1.1 Background
- 2.1.2 Objective
- 2.2 Automation in biomedical devices
- 2.2.1 Definition and scope
- 2.2.2 Overview and emergence of automation
- 2.3 Technological advancements in automation—Cyber physical systems, robots, and integration with digital platforms, and their advantages
- 2.3.1 Implantable devices
- 2.3.2 Wearable devices
- 2.3.3 Equipment for diagnosis and treatment
- 2.4 Case studies and applications
- 2.5 Regulatory and safety considerations
- 2.5.1 Compliance with medical device regulations
- 2.5.2 Standardization and validation
- 2.6 Drawbacks and challenges
- 2.6.1 Initial cost and investment
- 2.6.2 Specialized training and workforce adaptation
- 2.6.3 Technical failures and malfunctions
- 2.6.4 Cyber security and cyber-attacks
- 2.7 Conclusion
- References
- Chapter 3 Mathematics in biomedical robotics and devices and its role in healthcare delivery
- Abstract
- Keywords
- 3.1 Introduction
- 3.2 Mathematical foundations of robotics and biomedical robotics
- 3.2.1 Motion control
- 3.2.2 Force control
- 3.2.3 Mathematics in biomedical robotics
- 3.3 Applications of mathematics in biomedical robotics and devices
- 3.3.1 Mathematics in surgical robotics
- 3.3.2 Mathematics in rehabilitation robotics
- 3.3.3 Mathematics in medical robotics for diagnosis and monitoring
- 3.4 Future perspectives and limitations
- 3.5 Conclusion
- References
- Chapter 4 Advancing ankle–foot orthosis design through biomechanics, robotics, and additive manufacturing: A review
- Abstract
- Keywords
- 4.1 Introduction
- 4.2 Biomechanics of biological ankle and foot
- 4.3 Design of Orthoses for lower limb
- 4.3.1 Ankle–foot orthoses
- 4.3.2 Knee–ankle–foot orthosis
- 4.3.3 Hip–knee–ankle–foot orthosis
- 4.3.4 Advancements in mobility-enhancing orthotic systems
- 4.4 Advances in orthotic manufacturing
- 4.4.1 3D anatomical data acquisition technologies in orthotic device design
- 4.4.2 Rapid prototyping technologies transforming orthotic device manufacturing
- 4.5 Technical challenges
- 4.5.1 HRI challenges
- 4.5.2 Sensors and controllers challenges
- 4.5.3 Structural design challenges
- 4.5.4 Actuators and batteries challenges
- 4.5.5 Materials and safety challenges
- 4.5.6 Ergonomics challenges
- 4.6 Summary
- 4.7 Future directions
- References
- Chapter 5 Application of multibistatic frequency-domain measurements for enhanced medical sensing and imaging
- Abstract
- Keywords
- 5.1 Introduction
- 5.2 Advantages of integrating multibistatic antennas in the development of detecting hemorrhagic brain strokes using a scanner
- 5.2.1 Advancement of a microwave cerebral imaging trial product for the detection of stroke
- 5.2.2 HP in imaging: Algorithms and procedures
- 5.2.3 Design (methodology)
- 5.2.4 Description of signal preprocessing techniques
- 5.3 Experimental validation
- 5.3.1 Imaging results
- 5.4 Discussion
- References
- Chapter 6 Sleep posture analysis: state-of-the-art and opportunities of wearable technologies from clinical, sensing and intelligent perception perspectives
- Abstract
- Keywords
- 6.1 Introduction
- 6.2 Health complications: A sleep behavior perspective
- 6.2.1 The musculoskeletal system: A biomarker of and risk factor for sleep-related pathologies
- 6.2.2 Clinical assessment of sleep disorders
- 6.2.3 Management and treatment practices for sleep disorders
- 6.2.4 Clinical needs and challenges in sleep posture analysis
- 6.3 Sleep posture analysis: Current state of research
- 6.3.1 Human sleep posture sensing technologies
- 6.3.2 Algorithmic trends in wearable sensor-based sleep posture analysis
- 6.4 Monitoring sleep postures using wearable sensors
- 6.4.1 The standard four sleep postures
- 6.4.2 Beyond the standard four sleep postures
- 6.5 Temporal analysis of in-bed postural activity using wearable sensors
- 6.6 Conclusion
- References
- Chapter 7 Comparative evaluation of deep learning techniques for multistage Alzheimer's prediction from magnetic resonance images
- Abstract
- Keywords
- 7.1 Introduction
- 7.2 Methodology
- 7.2.1 Data set description
- 7.2.2 Data augmentation
- 7.2.3 Machine learning models
- 7.2.4 Evaluation metrics
- 7.3 Results and discussion
- 7.4 Conclusion
- References
- Chapter 8 Machine learning brain activation topography for individual skill classification: Need for leave-one-subject-out (LOSO) cross-validation
- Abstract
- Keywords
- 8.1 Background
- 8.2 Methods
- 8.2.1 Data collection: Electroencephalography
- 8.2.2 Data preprocessing
- 8.2.3 Processing for topography-preserving 3D-CNN
- 8.3 Results
- 8.3.1 Fivefold cross-validation results
- 8.3.2 Leave-one-subject-out cross-validation results
- 8.4 Discussion
- References
- Chapter 9 Transfer learning-based disease prognosis in Biomedicine 4.0: Challenges and opportunities
- Abstract
- Keywords
- 9.1 Introduction
- 9.2 Challenges in disease prognosis
- 9.2.1 Limited data availability
- 9.2.2 Disease complexity and variability
- 9.2.3 Lack of standardization in prognostic models
- 9.3 AI/ML in disease prognosis
- 9.3.1 Efficient analysis of medical data
- 9.3.2 Forecasting disease outcomes
- 9.4 Transfer learning for disease prognosis
- 9.4.1 Understanding transfer learning
- 9.4.2 Advantages over traditional ML methods
- 9.4.3 Eliminating the need to build models from scratch
- 9.5 Enhancing disease prognosis models
- 9.5.1 Enhanced accuracy in prognostic models
- 9.5.2 Superior results than other ML methods
- 9.5.3 Improved treatment outcomes
- 9.5.4 Reduced healthcare costs
- 9.6 Conclusion
- References
- Chapter 10 Voice and chatbot: A hybrid framework using XAI for improving mental health
- Abstract
- Keywords
- Declaration of data availability
- Declaration of conflicts of interest statement
- 10.1 Introduction
- 10.2 Problem statement
- 10.3 Literature survey
- 10.4 Preliminaries
- 10.4.1 RNN
- 10.4.2 CNN
- 10.4.3 Integrated stacked ensemble
- 10.4.4 NLP
- 10.4.5 Voice engines
- 10.4.6 XAI
- 10.5 Dataset description
- 10.6 Proposed method
- 10.7 Result and analysis
- 10.7.1 Experimental setup
- 10.7.2 Time complexity analysis
- 10.7.3 Optimizer analysis
- 10.7.4 Result
- 10.7.5 Comparison
- 10.7.6 Benefits
- 10.8 Limitation and conclusion
- References
- Further Reading
- Chapter 11 Wearable sensors: The pathway to applications of on-body electronics
- Abstract
- Keywords
- 11.1 RFID wearables: Design consideration
- 11.1.1 RFID basic principles of design
- 11.1.2 Human tissue considerations for tag design
- 11.1.3 Ultra-lower power live streaming through UHF RFID
- 11.2 RFID wearable sensor fabrication and applications
- 11.2.1 Wearable sensor stretchability
- 11.2.2 Wearable sensors and variable human tissue dielectrics
- 11.3 Wearable sensor mass fabrication
- 11.4 Conclusion
- References
- Index
- No. of pages: 350
- Language: English
- Edition: 1
- Published: November 14, 2024
- Imprint: Academic Press
- Hardback ISBN: 9780443222061
- eBook ISBN: 9780443222078
FI
Faiz Iqbal
Dr. Faiz Iqbal received his B.Tech in Mechanical and Automation Engineering in 2011, M.Tech in Mechatronics Engineering in 2013, and PhD in Manufacturing Automation in 2019 from India. He is currently a Lecturer in the School of Engineering at the University of Lincoln in the UK. He has published several research papers and holds four patents, and has received multiple awards for his contributions in research and innovation. His research interests include Mechatronics, Industrial Automation, Smart Manufacturing, Fluidic logic/Soft Robotics, and Industry 4.0. He successfully delivered a COVID-19 project and was awarded the COVID-19 Engineering Medal by the School of Engineering at the University of Edinburgh.
Affiliations and expertise
Lecturer of Mechanical Engineering, School of Engineering, University of Lincoln, UKPG
Pushpendra Gupta
Pushpendra Gupta is pursuing a Ph.D. in Mechanical Engineering at the Indian Institute of Technology Kharagpur. He received his M.Tech in Production Engineering (Gold Medalist) in 2015 and B.Tech in Mechanical and Automation Engineering in 2011 from India. He has also worked as a faculty member for 2.5 years at MITRC Alwar before joining IIT Kharagpur. His research interests include humanoid robots, soft computing, evolutionary optimization, and their application in multi-criterion optimization, modeling, and machine learning.
Affiliations and expertise
Research Scholar, Mechanical Engineering Department, Indian Institute of Technology Kharagpur, IndiaVK
Vidyapati Kumar
Vidyapati Kumar is a Ph.D. candidate in the Department of Mechanical Engineering at the Indian Institute of Technology, Kharagpur. He completed his Master’s in Engineering in Production Engineering in 2018 and Bachelor's in Technology in Mechanical Engineering in 2016. Before starting his Ph.D., he worked for 1.7 years as a project assistant at the CSIR-Central Institute of Mining and Fuel Research. He has published research in SCI/Scopus indexed journals and conference proceedings, and has obtained both an Australian and Indian patent for his innovative research. His research area includes Rehabilitation Robotics, Biomedical devices, and the application of soft computing tools in solving optimization and decision-making problems.
Affiliations and expertise
Research Scholar, Mechanical Engineering Department, Indian Institute of Technology Kharagpur, IndiaDP
Dilip Kumar Pratihar
Dr. Dilip Kumar Pratihar is a Professor (HAG Scale) in the Mechanical Engineering Department at IIT Kharagpur, India and former Professor-in-charge of the Centre for Excellence in Robotics. He received his BE (Hons.), M.Tech. in Mechanical Engineering, and Ph.D. from various Indian institutions, as well as post-doctoral study at universities in Japan and Germany. He has received multiple awards, including the University Gold Medal, INSA Teachers' Award, Technologist of the Year Award, and Distinguished Alumnus Award. He has made significant contributions to Artificial Intelligence (AI) and its applications in fields such as Industrial Automation, Robotics, Health Care, and Education Technology. He has published several books and hundreds of publications in reputed journals, conferences, and book chapters. He is a regular reviewer for several international journals and a member of the editorial board of several international journals. He has filed patents and received 1 copyright. He is a Fellow of Institution of Engineers, a Senior Member of IEEE, a Member of ASME, and a Member of AMM.
Affiliations and expertise
Professor, Mechanical Engineering Department, Indian Institute of Technology Kharagpur, IndiaRead Biomedical Robots and Devices in Healthcare on ScienceDirect