Estimation of Energy Expenditure and Physical Activity Classification With Wearables

Brief Summary

Detailed Description

Physical activity (PA) is defined as any bodily movement produced by skeletal muscle that requires energy expenditure. The scientific evidence for the beneficial effects are irrefutable. Regular PA is proven to help prevent and treat several non-communicable diseases such as heart disease, stroke, diabetes and different forms of cancer. PA is a complex behaviour that is characterized by frequency, intensity, time and type (FITT). In order to understand the effect of PA on health and our general well-being, it is essential to monitor all four characteristics of PA. A PA classification algorithm can assess the amount of time spent in different body postures and activity. Making it possible to assess frequency, time and type. In order to completely characterize PA, intensity needs to be estimated. This can be done by the estimation of energy expenditure (EE). Wearables play a crucial role in the monitoring of PA. They are practical way to collect objective PA data in daily life, in an unobtrusive way, at a relatively low cost. Furthermore they can be applied as a motivational tool to increase PA. Accelerometry has been routinely used to quantify PA and to predict EE using linear and non-linear models. However, the relationship between EE and acceleration differs from one activity to another. For example, cycling can generate the same acceleration amplitude as running, but the EE may differ greatly. It is clear that acceleration alone has a limited accuracy to estimate EE from different activities. Improving the estimation of EE could be achieved by first classifying the activity type. For each type of activity, different estimations can be used. There are numerous methods to classify PA and estimate EE. Literature describes the use of regression based equations combined with cut-points, linear models, non-linear models, decision trees, artificial neural networks, etc. It is still unclear what would be the best method to estimate EE, not to mention which features would contribute to the model. Another possibility is to add a relevant bio-signal to the estimation model. Heart rate, breathing rate, temperature are all signals that have a response related to an increase in PA. Heart rate has been used previously to improve the EE estimation in combination with accelerometry. The breathing rate and temperature could contribute to the estimation of EE is still unclear. Therefore, the goal of the current study is twofold. Firstly, to explore the contribution of different variables (physiological signals) to the estimation of EE and the classification of PA. Secondly, develop and validate a model to estimate EE and classify PA in simulated free-living conditions based on the relevant variables.

Primary Outcomes

Secondary Outcomes

• Heart rate (variability) algorithm(1.5 years)
• Contribution of different bio signals to the estimation of energy expenditure(1.5 years)
• Instantaneous energy expenditure(1.5 years)