Aims & Objectives
Recent advances in ICT and MEMs have allowed the transition from traditional healthcare monitoring to smart and mobile healthcare systems. A plethora of miniaturized, wireless wearable or implantable devices are now at the service of healthcare professionals for personalizing and modernizing the care of patients with both acute as well as chronic conditions, such as diabetes, stroke recovery and rehabilitation, mobility impairments, dementia, etc. Wireless Body Area and Body Sensor Networks (WBAN and BSN respectively) are therefore an emerging and interdisciplinary topic that relies on the synergies between Engineering. Computer and Biomedical Science. This course focuses on the theory, design and development aspects of such networks, with the primary objectives to give a spherical introduction to this field and its extensions towards the standardization of novel healthcare standards and practices. In a nutshell, this course aspires to cover:
Biosensors design and energy-harvesting techniques for healthcare applications;
Wireless Communications, Network topologies and Standards;
Biomedical Sensor Fusion and context-aware sensing;
Integration with mobile healthcare systems and Ambient Assisted Living architectures.
Learning outcomes & Competences
Prospective students will have the opportunity to introduce themselves to a multidisciplinary and emerging topic that combines sensor design, low-power wireless transmission, signal processing and machine learning, context-awareness, and human-machine computer interaction, for biometrics monitoring and intervention. The related aspects will be treated from an engineering perspective, providing students with both the essential theoretical background, as well as the practical skills needed for the design and development of modern pervasive healthcare and telemedicine platforms. In this transitional era, wherein the WBAN and BSN have a leading role in reducing the cost of healthcare, whilst improving the quality of the services provided, the knowledge and the experience gained will act as the baseline for specializing in emerging scientific methods and the cutting edge of novel technological trends for healthcare. This course is therefore targeted to students who want to either pursue a successful Biomedical Engineering career, as well as follow a PhD training programme.
Computer Networks, Signal Processing (Signals and Systems), Applied Mathematics for Engineers, Principles of Programming and Programming Languages.
Biosensors: Electromechanical, Multiple Sensors and Microsensor Arrays, Nanoscale Sensors, Biocompatibility. Wireless Communications: Inductive Coupling and RF transmission, Network Topologies, IEEE Standards for Healthcare Applications / Zigbee, Interference / Coexistence and Quality of Service, ISO/IEEE 11073 Personal Health Device. Energy Harvesting: Inertial Energy Harvesters and Wireless Power Delivery. Signal Processing and Machine Learning: Feature Extraction, Selection and Dimensionality Reduction, Inference models for context extraction and extraction, Belief Networks and Markov Models. Privacy and Security. Wireless Sensor Development Platforms: Real Time Operating systems and pervasive architecture design. Case Studies: Gait monitoring case study, medication monitoring and reminder case study, and motion reconstruction case study.
Recommended reading / Bibliography
Body Sensor Networks, Yang G.Z. (Editor), 2nd Edition, Springer-Verlag, 2014
Wireless Sensor Networks for Healthcare Applications, Terrance J. Dishongh, Michael McGrath, and Ben Kuris, Artech House, 2009.
Teaching and learning methods
A set of lectures on the proposed thematic areas will be given, accompanied by 4 hands-on sessions (laboratory exercises) and equivalent homework sessions for students.
Written Exam (20% of final grade)
Homework assignments (40% of final grade)
Project assignment (40% of final grade)