Activity of the Heart

Emotional arousal controls heart rate by influencing the balance between the sympathetic and parasympathetic nervous systems [64, 61]. Both parts of the nervous system control the activity of the heart. Higher activity of the sympathetic nervous system is connected to higher arousal. Conversely, higher activity of the parasympathetic nervous system is connected to a lower arousal. Both systems are always active, but vary in their activation levels over time.

During physical activities, such as sports, the sympathetic nervous system is dominant, resulting in a higher heart rate. The blood moves

faster and can transport more oxygen to the muscle cells than during rest. The muscle cells can generate more energy to conduct the required movements. Physical activity is not the only factor influencing heart rate. The human brain attempts to anticipate physical activity and mediates this need by means of the sympathetic and parasympathetic nervous system to the heart. As a result, the heart rate is constantly adapting to the most current situation. Physical effects due to activity and emotional reactions overlap and result in a heart rate that is dependent on the physiological condition of the person.

The heart’s activity can be measured by an electrocardiogram (ECG). The electrical signals controlling the heart rate can be measured by elec- trodes placed on the skin. While Ag/AgCl electrodes with conductive gel are still dominant for medical applications, new dry and noncontact electrodes become available are nonmedical appliances [65]. The gel-based electrodes require careful gel application in the correct amount to guaran- tee good signal quality. Furthermore, long-term contact between the gel and the skin may cause skin irritations. New dry-electrode devices (e.g. [56]) can be worn as simple chest straps. Alternative methods measure the heart rate by measuring the pulse waves that spread through the body after each heartbeat. Using regular cameras, an MIT team has developed a method to visualize and measure how the pulse wave spreads through the body. Eulerian video magnification [66] is used. The heart rate can also be measured by observing small body movements induced by the pulse waves [67]. Different camera-based methods are used in research and mobile applications to measure the pulse at the finger [68].

The measured heart rate varies because there are changes in the inter- beat intervals due to physical activity and physiological, cognitive, and emotional processes. The resulting variability of the heart rate can be analyzed to discern the effects. The following paragraphs explain the most commonly used features to analyze heart rate variability (HRV). The features can be split into statistic, geometric, and frequency-domain methods [69]. The most commonly used methods for automatic analysis calculate statistic features or use features from the frequency-domain.

Hear rate related features are usually analyzed for specified window size. Malik [69] recommends a window size of 5 minutes. An analysis of smaller window sizes can be found in [70]. The analysis in the time domain is based on the time between two normal beats (N = normal beat) of the

heart, which is measured in the ECG signal by the NN interval. Typical features in the time domain are:

• SDNN [ms]: Standard deviation of NN intervals in a given interval. • RMSSD [ms]: The square root of the mean of the sum of the squares

of differences between adjacent NN

• pNNx: Count of NN below x divided by the total number of all NN intervals (most common x = 50) intervals.

The spectral analysis distinguishes four types of frequency bands shown in Table 2.1. The HF and LF frequency bands have been used for further analysis. The HF power is usually attributed to cardiac parasympathetic nerve activity. The LF power is associated with a dominant sympathetic component [69, 64, 71]. Therefore, the LF/HF ratio is seen as a measure to identify the currently dominant nerve [72, 73]. However, this clear assignment of frequency bands to the activation of the nervous system and the value of the LF/HF ratio for affect recognition are still debated [74]. The methods presented above are affected by physical activity. If users are moving, changes in heart activity are a combination of heart rate increases, which are due to arousal, and changes that result from a change of the physical activity. Therefore, HRV features cannot be interpreted during physical activity. Small changes that are due to movement can be mistaken for arousal reactions.

Myrtek [75] has presented an approach to overcome this challenge. The additional heart rate algorithm aims at isolating affect-related heart rate changes from changes that are induced by physical activity. The algorithm has been deduced from empirical data and uses only the heart rate value for each minute HRi and acceleration data. In an arousal reaction, the

Abbreviation Name Range in Hz

ULF Power in the ultra low frequency range <0.003 VLF Power in the very low frequency range 0.003 - 0.04 LF Power in the low frequency range 0.04 - 0.15 HF Power in the high frequency range 0.15 - 0.4

heart rate rises:

HRi> HRi−1

In the algorithm, the heart rate is compared to a sliding mean average

HRi of the last 3 minutes.

HRi= 1 3 i<4 X i=1 HRi

The data from the acceleration sensors have to be transformed into the expected increase in heart rate. The acceleration values are filtered and added and transformed into a value ACTi by using a logarithmic function. The resulting values of ACTirange between 0 and 200. If these activity values are too high in comparison to a 3-minute average, the calculation is stopped for this minute because the algorithm cannot analyze such sudden changes. The expected activity-related heart rate increase ∆HRi is calculated by using the variable CDIVi.

∆HRi=

90 + ACTi CDIVi

CDIVi is adjusted according to the number of arousal reactions that have been found in the last 20 minutes. If less than five reactions have been found, CDIV will be decremented. If more than 10 reactions have been found, CDIV will be incremented. CDIV can vary between 0 and 30 and

CDIV0= 23.

The increase in heart rate has to be higher than the increase that can be attributed to movement. The relation between the actual change in heart rate and the expected change due to physical activity is the additional heart rate ADH at minute i.

ADHi=

HRi− HR ∆HRi

This algorithm is limited to small changes in physical activity. Explicitly excluded are posture changes, such as sitting down or getting up from a chair. The algorithms adapts to the current type of activity by changing the CDIV value. After rapid changes, the algorithm will need time to

adjust to the new type of activity.

Kusserow [76] aimed at overcoming this challenge in his thesis and developed a number of prototypes for specific use cases. The selected use cases (ski jumping and cello concert) are well defined in their physical activity. For example, ski jumpers perform the same movement again and again. A cello player repeats similar movements of the arm during a concert. In these cases, multiple instances of an activity can be compared at different stress levels. The results confirm that“stress-arousal” influences the heart rate beyond the physical activity. The application to free-living daily activities showed only an overlap with reported arousal events of 7.8 percent. This result is attributed to the low salience of smaller arousal events.