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The topic of EE estimation or physical activity assessment is gaining more and more interest lately, with the release of many activity trackers in the consumer market, some of them claiming higher accuracy due to a combination of accelerometer and physiological data (e.g. Bodymedia, Basis or the Apple watch). However, simply combining multiple signals, without personalization, provides suboptimal results, as I'll show in this post.
Let's take heart rate (HR) as an example. HR is the most commonly used physiological parameter to monitor physical activity and is getting used more and more with the introduction of many wrist-based HR monitors. HR can be key in providing accurate, personalized estimates at the individual level due to the strong relation between oxygen consumption, HR and EE within one individual. Here we can see how EE and HR evolve during different activities performed by one individual. The signals follow a similar trend. Pearson's correlation coefficient between HR and EE is 0.98, clearly, HR can be used as a predictor of EE. 
This post is about machine learning for energy expenditure (EE) estimation. More specifically, I'll show how to model the relation between accelerometer, physiological data and EE using Bayesian models and hierarchical regression.
During my PhD I've been working on developing EE models combining accelerometer and physiological data acquired using wearable sensors. I mainly focused on developing personalization techniques able to normalize physiological data across individuals, without the need for individual calibration.
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However, this individual-specific relation does not hold across individuals, challenging standard population-based approaches for EE estimation. As a result, individual calibration and laboratory tests are needed to normalize HR. The rationale behind the need for normalization is that individuals with similar body size expend similar amounts of energy during a certain activity, however their HR differs depending on other factors, for example, fitness. Let's look at another example to clarify this point. Here we have walking, running and biking data from two participants, the similar body size (weight P1: 57 and P2: 52 kg, height P1: 166 and P2: 169 cm), results in similar levels of EE for the same activities, as shown in the two plots on the left side. However, the different fitness level (VO2max P1: 2100 ml/min and P2: 3130 ml/min) results in higher HR for the unfit participant, as shown in the two plot on the right end side. Thus, estimation models relying on HR to predict EE will result in underestimations and overestimations of EE. 
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The main focus of my research was then to define methods and models able to take into account variability in physiological signals between individuals without the need for individual calibration. Let's take a step back, and start with the basics.
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cardiovascular endurance and fitness
Moving on, let’s introduce the concept of cardiovascular endurance or cardiorespiratory fitness. Cardiorespiratory fitness (from now on just fitness) is defined as the ability of the circulatory and respiratory systems to supply oxygen during sustained physical activity. Fitness is not only an objective measure of habitual physical activity, but also a useful diagnostic and prognostic health indicator for patients in clinical settings, as well as healthy individuals [1]. Fitness is considered among the most important determinants of health and wellbeing.
In this post, my interest is purely related to performance in sports. So everything that follows should be considered in this context. |
Marco ALtiniFounder of HRV4Training, Advisor @Oura , Guest Lecturer @VUamsterdam , Editor @ieeepervasive. PhD Data Science, 2x MSc: Sport Science, Computer Science Engineering. Runner Archives
April 2022
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