Unit information: Sensing Technologies for Diagnostics and Monitoring in 2020/21

Unit name	Sensing Technologies for Diagnostics and Monitoring
Unit code	EENGM0031
Credit points	10
Level of study	M/7
Teaching block(s)	Teaching Block 1 (weeks 1 - 12)
Unit director	Professor. Piechocki
Open unit status	Not open
Pre-requisites	Undergraduate Degree in Engineering
Co-requisites	None
School/department	School of Engineering Mathematics and Technology
Faculty	Faculty of Engineering

Description including Unit Aims

Low-cost, connected, digital technologies are increasingly seen as vital to the understanding, prevention, diagnosis and management of numerous health conditions over months and years in residential settings and in the community. These technologies, such as smartphone apps, wearables, blood glucose monitors – and ever growing Internet of Things (IoT) devices such as smart home systems (e.g. Echo), smart meters and connected appliances – all offer an unprecedented opportunity to characterise a person’s health condition. With the data processed by AI, they will deliver decision support to health and care professionals, predict a patient’s exacerbations, support independent living, deliver behavioural or even pharmaceutical interventions and allow the efficacy of treatments to be monitored. This unit will discuss nascent technologies and solutions for sensing human vital signs and physical behaviour encompassing entire data capture transmission/processing pipelines: from body worn and biosensors to low power wireless networks and energy constraint data processing.

Syllabus

• Introduction to sensing and biosensing to detect and monitor diseases
• Characterisation of operation of sensors and biosensors: sensitivity, specificity, clinical range etc.
• Principles and transduction approaches for biochemical sensors: electrochemical, MEMs, optical etc.
• Biomarker detection and bio-receptors e.g. antibodies, enzymes, DNA
• Sensor system development e.g. data capture, sample preparation
• Physiological measurement e.g. ECG, EMG, EEG;
• Basic elements of the wireless channel and radio wave propagation.
• Low Power IoT wireless networks (IEEE 802.15.4, BLE, 6LoWPAN).
• Reliability in data transmission
• Efficient signal representation, compression, and ultimately classification/regression tasks.

Intended Learning Outcomes

On successful completion of this module students will be able to:

1. Critically evaluate and discuss the role of sensors in home diagnostics and monitoring applications
2. Evaluate a range of elements involved in constructing and operating a biosensor, and select and apply the optimum combination for a given application
3. Analyse a diagnostic or monitoring scenario, and devise and evaluate an effective measurement system from sample/signal collection to user interface
4. Explain the challenges of reliable communications over unreliable channels and basic IoT networking standards.
5. Design and prototype algorithms for data analysis of sensory signals such as step counters, classification and regression

Teaching Information

Teaching will be delivered through a combination of synchronous and asynchronous sessions, including lectures, practical activities supported by drop-in sessions, problem sheets and self-directed exercises.

Assessment Information

The unit will be assessed through two coursework assessments weighted at 50% each.

Reading and References

Biosensors: An Introductory Textbook by Jagriti Narang, C.S. Pundir Jenny Stanford Publishing (2017) ISBN-13: 978-9814745949

Biosensors and Bioelectronics by Chandran Karunakaran, Kalpana Bhargava, Robson Benjamin Elsevier (2015) ISBN: 9780128031018

Biosensors for Medical Applications Edited by Séamus Higson, Woodhead publishing (2012) ISBN 978-1-84569-935-2

G.Z. Yang, Body Sensor Networks, 2nd edition, Springer, 2014.

J. Proakis, Digital Communications, 4th edition, McGraw Hill, 2000.