RMSSD: Emotional Quantification Variable ?

In this article, we explore the validity of using RMSSD and other time-domain variables in quantifying emotions. Sources are attached.


10/20/20224 min read


Quoting Dr. Bryn Farnsworth, Ph.D from IMotions,

"It is of course an understatement to say that the heart is critical for our well-being. But while we may often think of our hearts in relation to our health, there is also much more to these beats that can tell a deeper story of who we are.

There has in recent times been an increased use of ECG devices (electrocardiogram – also known as EKG, all of which are the same thing) and a proliferation of advanced techniques that offer the tantalizing possibility of new discoveries once applied to new data.

While largely motivated by medical concerns, ECG is being used more and more by researchers and investigators seeking to gain a better understanding of physiological arousal (often as an adjunct to other biosensor methods). As the heart rate is coupled to autonomic nervous system activity, it provides a suitable proxy for examining how we feel."

As a follow-up, we'd like to add that not only ECG but also PPG (Photoplethysmography) can be used to get the same results if the setup & monitoring is properly conducted.

The case for PPG

"Our results support this proposition by demonstrating that HRV analysis of signals derived from 3-lead ECG and earlobe PPG recordings are almost identical; the pulse sensor is therefore a reliable means of recording a signal from which HRV measures can be derived, at least in healthy subjects at rest" [1]

“The advantage of using the earlobe as location for measuring heart rate data using the photoplethysmographic technique has been linked to an equally good read out values compared to ECG. Correlations with r > 0.9 were found between ECG-HRV and PPG-HRV measures. The customized pulse photoplethysmographic device shows a potential surrogate for ECG in the evaluation of heart rate variability measures. Although it is not meant to be a replacement in clinical applications... It can also be easily implemented in a miniaturized form and is wearable on the ear, which is a peripheral region with good vascularization and perfusion properties. It thus ensures a good-quality signal, robustness and the very low risk of errors and artifacts in both short-term and long-term monitoring applications.[2]”

“According to MIT Technology Review ‘Using your ear to track your heart’, The ear, according to LeBoeuf (co-founder of Valencell) is a much better source of data than the wrist because it offers an area where blood flows neatly in and out, providing a much stronger signal and less noise. Blood also flows to different parts of the ear at different rates, which can be used to measure different metrics. And because we don’t move our ears as much as our arms, it can be easier to sort out intentional motions from unintentional ones.[3]”

Emotion quantification via PPG-monitoring

Now we've established that PPG signals are reliable as a measurement of HRV and related variables. Also, not all PPG signals are created equal, the earlobe provides a more responsive signal with respect to activity conducted. With CHIRON, our patent-pending earlobe sensor design further increases the stability of the of the reading by prevent any unwanted motion or slippage. It can be worn comfortably for up to 3 hours, without any issues (lengthiest monitoring sessions so far).

Let's see the research validating the use of RMSSD and derived variables from heart rate measurement for emotional response and quantification.

To sum up, amusement elicited by watching video clips led to a decrease in heart rate and increase in HRV, when compared with angry, fearful, and neutral emotions. This suggests that amusement is related to the activation of the parasympathetic nervous system. Furthermore, anger led to increased HRV when compared with fear, suggesting that HRV could discriminate certain kinds of negative emotions. These results indicate that amusement, anger and fear can be differentiated by the ECG signals, contributing to the literature that objectively measures subjective experiences of emotions. Further research is still needed to differentiate between the peripheral physiologies of other specific emotions.” [4]

"...the use of RMSSD is to be recommended. The time-domain variable RMSSD is adequate and most unproblematic also in longer-term recordings and more real-life conditions implicating varying activity levels, which can be adequately interpreted in adults as well as in children." [5]

"To conclude, our experimental and modeling paradigm, combined with the measurement of the resting HRV, represents a valuable tool for measuring how an emotional event can influence the higher-level behavior of individuals, such as decision making, rather than simple emotional reactions to events." [6]

"We deployed this recognized emotion to automate the music system associated with its emotion. To bring this, we built an Android app to communicate with the smart wearable utilized. Totally 150 members from both genders have participated. The accuracy of 91.81% is achieved. This emotion recognition system can be used in various fields like robotics, medicine, virtual reality, and gaming, advertising, education, automotive working conditions and safety, home appliances" [7]

As shown above, the use of PPG data along with RMSSD extraction can be used to quantify emotion. At CHIRON, we have a propriety hardware and software to ensure we can record and analyze data more accurately. Contact us for more information.


[1] "A comparison of photoplethysmography and ECG recording to analyse heart rate variability in healthy subjects", G. LU, F. YANG, J. A. TAYLOR and J. F. STEIN, Journal of Medical Engineering & Technology, Vol. 33, No. 8, November 2009, 634–641

[2] "Comparison between Electrocardiographic and Earlobe Pulse Photoplethysmographic Detection for Evaluating Heart Rate Variability in Healthy Subjects in Short- and Long-Term Recordings”, Basilio Vescio, Maria Salsone, Antonio Gambardella, Aldo Quattrone, Sensors (Basel), 2018

[3] “Using Your Ear to Track Your Heart”, Rachel Metz, MIT Technology Review, 2014

[4] “How Do Amusement, Anger and Fear Influence Heart Rate and Heart Rate Variability?” Yan Wu, Ruolei Gu, Qiwei Yang, Yue-jia Luo, Front. Neurosci., 18 October 2019 | https://doi.org/10.3389/fnins.2019.01131

[5] "How to Use Heart Rate Variability: Quantification of Vagal Activity in Toddlers and Adults in Long-Term ECG", Helmut Karl Lackner, Marina Tanja Waltraud Eglmaier, Sigrid Hackl-Wimmer, Manuela Paechter, Christian Rominger, Lars Eichen, Karoline Rettenbacher, Catherine Walter-Laager, and Ilona Papousek, Sensors (Basel). 2020 Oct; 20(20): 5959.

[6] "Individual differences in heart rate variability are associated with the avoidance of negative emotional events", Kentaro Katahira, Tomomi Fujimura, Yoshi-Taka Matsuda, Kazuo Okanoya and Masato Okada, Biological Psychology - Volume 103, December 2014, Pages 322-331

[7] "Emotion based Media Playback System using PPG Signal", Preethi M., Nagaraj S., and Madhan Mohan P, 2021 Sixth International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)

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