Application of data fusion techniques and technologies for wearable health monitoring

Rachel C. King, Emma Villeneuve, Ruth J. White, R. Simon Sherratt, William Holderbaum, William S. Harwin

Research output: Contribution to journalArticlepeer-review


Technological advances in sensors and communications have enabled discrete integration into everyday objects, both in the home and about the person. Information gathered by monitoring physiological, behavioural, and social aspects of our lives, can be used to achieve a positive impact on quality of life, health, and well-being. Wearable sensors are at the cusp of becoming truly pervasive, and could be woven into the clothes and accessories that we wear such that they become ubiquitous and transparent. To interpret the complex multidimensional information provided by these sensors, data fusion techniques are employed to provide a meaningful representation of the sensor outputs. This paper is intended to provide a short overview of data fusion techniques and algorithms that can be used to interpret wearable sensor data in the context of health monitoring applications. The application of these techniques are then described in the context of healthcare including activity and ambulatory monitoring, gait analysis, fall detection, and biometric monitoring. A snap-shot of current commercially available sensors is also provided, focusing on their sensing capability, and a commentary on the gaps that need to be bridged to bring research to market.
Original languageEnglish
Pages (from-to)1-12
JournalMedical Engineering and Physics
Early online date23 Feb 2017
Publication statusPublished - 1 Apr 2017

Bibliographical note

© 2017 The Authors. Published by Elsevier Ltd on behalf of IPEM.This is an open access article under the CC BY license.(


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