Connected-health algorithm: Development and evaluation
Journal
Journal of medical systems
Date Issued
2016-04
Author(s)
Vlahu-Gjorgievska, Elena
Koceski, Sasho
Trajkovik, Vladimir
Abstract
Nowadays, there is a growing interest towards the adoption of novel ICT technologies
in the field of medical monitoring and personal health care systems. This paper proposes design of
a connected health algorithm inspired from social computing paradigm. The purpose of the
algorithm is to give a recommendation for performing a specific activity that will improve user’s
health, based on his health condition and set of knowledge derived from the history of the user and
users with similar attitudes to him. The algorithm could help users to have bigger confidence in
choosing their physical activities that will improve their health. The proposed algorithm has been
experimentally validated using real data collected from a community of 1000 active users. The
results showed that the recommended physical activity, contributed towards weight loss of at least
0.5 kg, is found in the first half of the ordered list of recommendations, generated by the algorithm,
with the probability > 0.6 with 1% level of significance.
in the field of medical monitoring and personal health care systems. This paper proposes design of
a connected health algorithm inspired from social computing paradigm. The purpose of the
algorithm is to give a recommendation for performing a specific activity that will improve user’s
health, based on his health condition and set of knowledge derived from the history of the user and
users with similar attitudes to him. The algorithm could help users to have bigger confidence in
choosing their physical activities that will improve their health. The proposed algorithm has been
experimentally validated using real data collected from a community of 1000 active users. The
results showed that the recommended physical activity, contributed towards weight loss of at least
0.5 kg, is found in the first half of the ordered list of recommendations, generated by the algorithm,
with the probability > 0.6 with 1% level of significance.
Subjects
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