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School of Engineering and Informatics (for staff and students)

Wearable Technologies (867H1)

Wearable Technologies

Module 867H1

Module details for 2021/22.

15 credits

FHEQ Level 7 (Masters)

Module Outline

In this module you will learn about the fundamentals of wearable technologies, including technological (computing, communication, sensing, energy), algorithmic (signal processing and machine learning) and applicative aspects through a combination of theoretical analysis and hands-on experimentation. You will learn what are the unique characteristics offered by wearable technologies, such as accurate sensing of body and physiological parameters which enables a "smart assistant" capable of reacting to the user's activities and needs. You will also learn about the unique challenges posed by their development alongside choice of technologies and human factors. Throughout the module you will apply the theory in experimental laboratories using one of our custom wearable platforms.

AHEP4 Learning Outcomes
M1, M2, M3, M4, M5, M6, M12, M13, M17

Module learning outcomes

Evaluate critically the unique characteristics of wearable technologies and the novel possibilities they offer from a theoretical and practical perspective

Design wearable devices from requirement gathering to their evaluation and evaluate the implementation challenges and design tradeoffs

Employ state of the art approaches to realise sensor-based context-aware wearable systems within power, memory, speed, latency and performance targets

Describe and analyse the signal processing and applied machine learning techniques used for activity and context awareness

TypeTimingWeighting
Coursework100.00%
Coursework components. Weighted as shown below.
EssayA2 Week 2 100.00%
Timing

Submission deadlines may vary for different types of assignment/groups of students.

Weighting

Coursework components (if listed) total 100% of the overall coursework weighting value.

TermMethodDurationWeek pattern
Spring SemesterLaboratory4 hours01111111111

How to read the week pattern

The numbers indicate the weeks of the term and how many events take place each week.

Dr Carlo Tiseo

Assess convenor
/profiles/555178

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School of Engineering and Informatics (for staff and students)

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