A hybrid model for predicting human physical activity status from lifelogging data

• Published in: European journal of operational research Vol. 281; no. 3; pp. 532 - 542
• Main Authors: Ni, Ji, Chen, Bowei, Allinson, Nigel M., Ye, Xujiong
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 16-03-2020
• Subjects: Hidden Markov model; Machine learning; Multi-objective genetic programming; Physical activity status prediction; Hidden Markov model; Multi-objective genetic programming; Physical activity status prediction; Machine learning
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2019.05.035

•A real-time monitoring system of individual lifelogging data using wearable sensors.•A novel hybrid model to predict find-grained physical activity status over time.•Competitive model performance with better generality and flexible non-linearity.•A personalised healthcare decision support tool allowing patient empowerment.•Contribute to the recent use of operations research, machine learning in healthcare.
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.