Heart Rate Variability: From Standard ECG Monitors to Wearables

Evangelia Athanasiou

Monitoring Heart Rate Variability (HRV) allows for exploring human activity through the heart, which can help us gain insight into health issues and behaviors. For example, fatigue and stress are daily characteristics that we can obtain by measuring HRV. In general, low HRV data can indicate fatigue, overtraining, cardiovascular diseases, or other health issues. It is also associated with harmful lifestyle behaviors such as smoking, physical inactivity, poor diet, etc [1][2], and monitoring heart activity can even prevent car accidents related to drowsiness just by looking at the HRV. However, you may notice that gathering accurate heart data in order to prevent these outcomes requires flexible data collection tools due to its longitudinal and/or mobile nature.

Heart Rate Variability and ECG

Historically, the iMotions “gold standard” of measuring heart rate variability has been with an electrocardiogram (ECG) device such as the BIOPAC system. The lab-based iMotions software has long been compatible with ECG studies, but we have been hard at work building a Mobile Research Platform to support wearables and activity tracking on the go, for which tracking heart data is a natural fit. Wearable technology has seen a huge increase in interest in recent years, from both a personalized data and health perspective. Because of the breadth of human behavior research application possibilities with and beyond HRV, promoting wearable technologies can help to overcome and predict unwanted diseases and capture the human experience in real-time. So we set out to study how BIOPAC compares to the Polar H10 ECG wearable device as an alternative to traditional HRV monitoring. We used the iMotions Mobile Platform to collect data in the hopes of providing an appealing new avenue for researchers that champions movement and flexibility.

Why wearable technologies + iMotions?

iMotions builds research tools that can be used in a wide variety of settings: the highly controlled environment in a lab, virtual reality environments using head-mounted displays, semi-controlled physical environments, and environments where the participant is acting completely naturally. This latter category of experiments sets requirements and limitations to what can be measured due to the threat of noisy data/artefacts, the longer period of data collection, or even access to participants’ everyday lives and routines.

iMotions’ solutions support a range of wearable modalities such as mobile eye tracking and physiological sensors, and wearable sensors are additionally available to measure heart rate in a natural environment over long periods of time. We chose the Polar H10 ECG wearable device for the study because it appears to be one of the most accurate wearable devices [3][4][5]. One of the most important advantages is the affordable price of the sensor. Moreover, it is more practical and comfortable so it can be used for various studies. Apart from being a high quality heart rate sensor that is used in both sports and research, the device also features a GPS sensor and is waterproof, which makes it even more applicable under different conditions.

Investigating wearables with iMotions Software & Mobile Research Platform

To explore how close wearable devices are to our “gold standard”, we compared the recordings from the BIOPAC system with the iMotions Mobile Research Platform. This study correlates the R-R intervals recorded through electrodes from the BIOPAC system with the recorded signals from the wearable Polar H10 belt through the iMotions Mobile app. It’s also possible to connect the Polar H10 to the iMotions Lab software, but the smartphone makes it possible to use internal phone sensors, apply surveys, enable GPS tracking, and more. With this integration, we improve the quality of the system by capturing real-time human physiological signals. This study aimed to investigate whether wearable devices are capable of replacing standard ECG monitors. Electrodes for BIOPAC and the wearable Polar belt are attached to the participant as shown below.

ecg polar heart placement
Figure 1: Experimental setup: BIOPAC system MP160 (left) and Polar H10 becolt HRV measurements (right)

From the analysis, the first graph shows how the two signals from BIOPAC and the Polar H10 belt are perfectly aligned and the second shows the correlation analysis results on average, using Pearson’s correlation analysis.

R-R intervals BIOPAC vs Polar H10 graph
Figure 2: R-R intervals of BIOPAC and Polar H10 sensor versus timestamp.
HRV correlation plot
Figure 3: Correlation analysis between BIOPAC system and Polar H10 belt in average using Pearson correlation analysis.

Results: Implications for multimodal mobile research

Due to the highly aligned R-R Interval signals between the Polar H10 and the BIOPAC, we conclude that wearable devices can provide a promising alternative solution for measuring HRV. This low-power device could transmit the continuous vital signs of patient activity to our app and then from this extraction researchers can tell useful things about people’s emotions and behaviors. iMotions software and our mobile platform provide the ability for continuous recordings through wearables and extraction of real-time data. The mobile platform even allows us to integrate even more sensors that are built in the smartphone as well as the sensors that are provided from the wearables.

Thus, collecting multiple data — over longer periods of time and through the ease of a smartphone — provides researchers better insights into people’s lives. This opens the possibility for personalizing their health state by addressing many clinical trial challenges like patient retention and engagement. By combining data collected from heart rate monitors such as Polar H10 with eye tracking glasses [6], we can even benefit from new ways of studying consumer behavior, preferences, and more. Finally, behavior analysis fuels research into more human-centered designs and better user experiences, all of which can benefit from multimodal data collection. We’ll leave you with a video that exemplifies this, as inspiration!

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[1] Speer, K.E., Semple, S., Naumovski, N. and McKune, A.J., 2020. Heart rate variability for determining autonomic nervous system effects of lifestyle behaviors in early life: A systematic review. Physiology & behavior, 217, p.112806.

[2] Rennie, K.L., Hemingway, H., Kumari, M., Brunner, E., Malik, M. and Marmot, M., 2003. Effects of moderate and vigorous physical activity on heart rate variability in a British study of civil servants. American journal of epidemiology, 158(2), pp.135-143.

[3] Gilgen-Ammann, R., Schweizer, T. and Wyss, T., 2019. RR interval signal quality of a heart rate monitor and an ECG Holter at rest and during exercise. European journal of applied physiology, 119(7), pp.1525-1532.

[4] Umair, M., Chalabianloo, N., Sas, C. and Ersoy, C., 2021. HRV and Stress: A Mixed-Methods Approach for Comparison of Wearable Heart Rate Sensors for Biofeedback. IEEE Access, 9, pp.14005-14024.

[5] Hinde, K., White, G. and Armstrong, N., 2021. Wearable Devices Suitable for Monitoring Twenty Four Hour Heart Rate Variability in Military Populations. Sensors, 21(4), p.1061.

[6] Spicer, C., Khwaounjoo, P. and Cakmak, Y.O., 2021. Human and Human-Interfaced AI Interactions: Modulation of Human Male Autonomic Nervous System via Pupil Mimicry. Sensors, 21(4), p.1028.

About the author

Evangelia Athanasiou

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