TY - JOUR
T1 - Application of data fusion techniques and technologies for wearable health monitoring
AU - King, Rachel C.
AU - Villeneuve, Emma
AU - White, Ruth J.
AU - Sherratt, R. Simon
AU - Holderbaum, William
AU - Harwin, William S.
N1 - © 2017 The Authors. Published by Elsevier Ltd on behalf of IPEM.This is an open access article under the CC BY license.(http://creativecommons.org/licenses/by/4.0/)
PY - 2017/4/1
Y1 - 2017/4/1
N2 - 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.
AB - 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.
UR - https://linkinghub.elsevier.com/retrieve/pii/S1350453317300152
U2 - 10.1016/j.medengphy.2016.12.011
DO - 10.1016/j.medengphy.2016.12.011
M3 - Article
SN - 1350-4533
VL - 42
SP - 1
EP - 12
JO - Medical Engineering and Physics
JF - Medical Engineering and Physics
ER -