Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/16264
Title: Wearable data analysis, visualisation and recommendations on the go using android middleware
Authors: Angelides, MC
Wilson, LAC
Echeverría, PLB
Keywords: Wearable technology;Visual information systems;Machine learning
Issue Date: 2018
Publisher: Springer Verlag
Citation: Multimedia Tools and Applications
Abstract: Wearable technology comes with the promise of improving one’s lifestyles thru data mining of their physiological condition. The potential to generate a change in daily or routine habits thru these devices leaves little doubt. Whilst the hardware capabilities of wearables have evolved rapidly, software apps that interpret and present the physiological data and make recommendations in a simple, clear and meaningful way have not followed a similar pattern of evolution. Existing fitness apps provide routinely some information to the wearer by mining personal data but the subsequent analysis is limited to supporting ad hoc personal goals. The information and recommendations presented are often either not entirely relevant or incomplete and often not easy to interpret by the wearer. The primary motivation behind this research is to address this wearable technology software challenge by developing a middleware mobile app that is supported by data analytics and machine learning to assist with interpretation of wearer data and with making of personal lifestyle improvement recommendations on the go which may then be used to feedback to the wearer’s daily goals and activities. The secondary motivation is to correlate and compare with trends in the wearer’s peer community.
URI: https://bura.brunel.ac.uk/handle/2438/16264
https://link.springer.com/article/10.1007/s11042-018-5867-y
DOI: https://doi.org/10.1007/s11042-018-5867-y
ISSN: 0942-4962
1432-1882
Appears in Collections:Dept of Electronic and Electrical Engineering Research Papers

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