## Part IV: Decomposition of heart rate

**Intro**

Today, we arrive at the third and last real-time stress monitoring technique that we will discuss in this series of blogposts. It is the method of decomposing the total heart rate, as measured by for instance a Polar, into smaller components. These components relate to the different components in the energy equation. This will become more clear in the next sections about physiology, calculation and applications.

**Physiology**

As discussed in the introduction, the energy equation is key. This equation describes how much of the available energy of the body is used by which processes. Based on this equation, the total energy used by the body can be broken down in it’s components, each making up for a part of it. The energy equation looks as follows:

E(total) = E(basalmetabolism) + E(mental) + E(activity) + E(thermoregulation) + E(growth)

But what does this have to do with heart rate? Well, heart rate is known to follow the same equation. This can intuitively be known, if you look at what happens during a stressful presentation or when you run the stairs. Indeed, heart rate increases! The same way, heart rate will change when you’re not in your thermoneutral zone, when you’ve just eaten or when you grow fast.

So basically if you would like to know the mental heart rate, you’ll either have to measure many variables, or try to to keep certain components constant. Luckily, we don’t grow that fast usually, and during research, all components except the physical and mental can be kept constant by designing an experiment in a clever way. Doing this would leave us with two variables we then have to measure: total heart rate and activity.

**Calculation**

There are more complicated ways of calculating mental stress, where more variables are taken into account, but here I will discuss the most basic way of calculating mental stress, which is usually used during research. It is based on a mathematical model that describes the relationship between the total heart rate and the activity that you measured. Basically you tune a model to fit the total heart rate as good as possible on a subset of the data of which you know that the individual was not stressed. If you then simulate the model using the activity data of the whole dataset, you obtain an estimate of the physical component of heart rate, even on these moments that stress was reported. You can then subtract the physical component from the total heart rate, which gives you an estimate of the mental component of heart rate, since the other components are kept constant during experiments.

**Applications**

The first application of this weeks blogpost is one by Piette et al. (2017). Here, candidate mounted police horses were classified for their long-term suitability based on heart rate and activity measurements. Nowadays, it common to select horses for mounted police based on intuition. Since this is a subjective way of deciding, it sometimes happens that the wrong horses are selected and that time and money is wasted on a horse that is not suited for the job.

Mounted police horses need to be as calm as possible in stressful situations, and since stress influences the mental component of heart rate, the long-term suitability of the candidate horses is believed to be measurable and quantifyable. For this reason, four experimental protocols were developed together with the mounted police of Brussels and 17 horse-rider pairs were selected. The horses were divided into four categories: bad beginner, good beginner, good experienced and bad experienced. During every protocol, the relative stress of the horses was calculated with wearable technology (heart rate and activity measurements). The time percentage spent over 20% relative stress by the horse was found to be significantly lower for good compared to bad beginner horses (p-value = 0.0277). Which made the authors conclude that *“real-time stress detection with wearable technology in mounted police horses provides information on the longer term suitability of police horses*”.

You can request the paper here.

A second application that I would like to mention is stress level monitoring in racing based on the principles discussed above. It is known that for each person there is an optimal stress level zone, for which they perform best (and race best). If the stress level of the drivers deviates from the optimal stress level zone, then there is a higher risk for driving mistakes. This way, the performance by drivers could be improved by preventing them from entering the non-opimal stress level zones. In case you are interested in more information (the system is also used to measure stress@work), then you can find it here.

**Discussion and conclusion**

See next weeks blogpost 🙂

This post was originally published here.