A hybrid model for predicting human physical activity status from lifelogging data
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{NI:2020:EJOR,
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author = "Ji Ni and Bowei Chen and Nigel M. Allinson and
Xujiong Ye",
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title = "A hybrid model for predicting human physical activity
status from lifelogging data",
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journal = "European Journal of Operational Research",
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volume = "281",
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number = "3",
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pages = "532--542",
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year = "2020",
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note = "Featured Cluster: Business Analytics: Defining the
field and identifying a research agenda",
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ISSN = "0377-2217",
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DOI = "doi:10.1016/j.ejor.2019.05.035",
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URL = "http://www.sciencedirect.com/science/article/pii/S0377221719304655",
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keywords = "genetic algorithms, genetic programming, Machine
learning, Physical activity status prediction,
Multi-objective genetic programming, Hidden Markov
model",
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abstract = "One trend in the recent healthcare transformations is
people are encouraged to monitor and manage their
health based on their daily diets and physical activity
habits. However, much attention of the use of
operational research and analytical models in
healthcare has been paid to the systematic level such
as country or regional policy making or organisational
issues. This paper proposes a model concerned with
healthcare analytics at the individual level, which can
predict human physical activity status from sequential
lifelogging data collected from wearable sensors. The
model has a two-stage hybrid structure (in short,
MOGP-HMM) - a multi-objective genetic programming
(MOGP) algorithm in the first stage to reduce the
dimensions of lifelogging data and a hidden Markov
model (HMM) in the second stage for activity status
prediction over time. It can be used as a decision
support tool to provide real-time monitoring,
statistical analysis and personalized advice to
individuals, encouraging positive attitudes towards
healthy lifestyles. We validate the model with the real
data collected from a group of participants in the UK,
and compare it with other popular two-stage hybrid
models. Our experimental results show that the MOGP-HMM
can achieve comparable performance. To the best of our
knowledge, this is the very first study that uses the
MOGP in the hybrid two-stage structure for individuals'
activity status prediction. It fits seamlessly with the
current trend in the UK healthcare transformation of
patient empowerment as well as contributing to a
strategic development for more efficient and
cost-effective provision of healthcare",
- }
Genetic Programming entries for
Ji Ni
Bowei Chen
Nigel M Allinson
Xujiong Ye
Citations