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

## TL;DR

This paper introduces a novel hybrid model combining multi-objective genetic programming and hidden Markov models to predict individual physical activity status from wearable sensor data, supporting personalized health management.

## Contribution

It presents the first use of MOGP in a two-stage hybrid model for individual activity prediction, enhancing healthcare analytics at the personal level.

## Key findings

- Achieves comparable performance to existing models.
- Validates the model with real UK participant data.
- Supports real-time health monitoring and personalized advice.

## 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.