The Biometrics of Flow State: Measuring Peak Human Performance in Real Time

Author: Miguel Picazo-Marin

Introduction

 When I was younger, I was a passionate trumpet player. When I'd play, it seemed that time would vanish, and the thought of playing the instrument itself was the last thing on my mind; it was almost as if I was playing effortlessly. Flow state is a psychological condition of complete absorption in an activity, characterized by intense focus, less self-consciousness, and a sense of effortless control. First defined by psychologist Mihály Csíkszentmihályi, it occurs when someone exudes full effort while equally engaged in overcoming a meaningful challenge, creating an optimal experience where time perception alters, and peak performance is reached. Flow state is measurable and manifests through measurable physiological patterns, including balanced heart rate variability, stabilized gaze patterns, and moderate emotional arousal. What if we could look at the specific involuntary human responses that occur when someone is in flow and use that data to predict or even induce a flow state?

Throughout this exploration into the biometrics of flow state, I want to focus on several questions. The first is: What physiological or even neurological factors are present when someone is in a flow state? Second, how is biometric technology able to detect flow state? Finally, is there any setting where this data can make an impact?

What is Flow State?

Flow is a state of mind in which you complete a challenging yet fulfilling task, and your focus and performance feel automatic (Metin et al., 2017). Due to the rapid advancement of biometric technology, we can now measure biometric information that may reveal this flow state in both consumer wearables and lab-grade technology.  But how exactly is this done?

What Can Be Used to Measure Flow?

1. Heart Rate Variability (HRV):

●     Measures subtle variations in the timing of a heartbeat.

●     Reveals the balance between focus and relaxation. The high-intensity focus typically causes an elevated heart rate and vice versa.

●     Professional athletes utilize this technology to identify ideal training states.

HRV analysis has also recently become more widely accessible outside of a clinical setting. In research settings, the Polar H10 chest strap (electrocardiogram-based) is commonly used to measure heartbeat intervals with medical-grade precision. These measurements reflect how the autonomic nervous system oscillates between stress (sympathetic) and recovery (parasympathetic) modes, which play a central role in an individual achieving a flow state while completing a task.

2. Eye Tracking Technology:

Another tool that is used widely to capture biometric data is eye tracking technology like that from Tobii, Pupil Labs, or SmartEye, which uses infrared cameras and the reflection of the cornea in eye tracking to capture gaze patterns with an accuracy of 0.5 in a positive or negative direction. These systems reveal how flow states manifest as long fixations (usually 300-500 ms longer than the baseline), reduced saccadic movements, and  "visual tunneling" patterns. Even some more accessible webcam-based possibilities, like screen-based eye trackers, are able to catch the 40-60% decrease in blink rate that denotes flow state.

●     It shows where someone looks and how long they are fixated on an object.

●     When someone is in a flow state, their gaze becomes more sustained, less distracted, and highly targeted (fewer blinks per minute).

3. Galvanic Skin Response (GSR):

In lab-controlled tests, lab equipment such as the Shimmer3 GSR+ device measures skin conductance with fingertip or palm electrodes to a microscopic level of accuracy. The readings show flow states typically remain in a medium range of arousal (2-5 μS)—unlike the spikes of stress and flatlines of disengagement. The Shimmer3 GSR+  device is actively used in our UX & UI biometric labs for various experiments to show how much can be done with GSR data. Consumer wearables have also seen a significant shift in capabilities. Even the EDA (Electrodermal Activity) sensor on the Fitbit Sense can detect the stable level of moderate excitement flow point that comes with the flow whenever the watch is worn and recording data.

●     GSR can detect emotional arousal by detecting subtle increases in sweat production in a participant.

●     Different physiological responses can be used to determine whether one is actively engaged with a stimulus, stressed, or even entirely disconnected from the task at hand.

●     Mild excitement (not bored, not stressed) is the most common description of what someone in a flow state connected to GSR would reveal.

Benefits & Potential Drawbacks

By monitoring heart rate variability (HRV) patterns during everyday tasks, wearables such as the Apple Watch and Polar H10 chest strap may now identify flow states and provide athletes and professionals with real-time feedback to maintain optimal performance. Eye-tracking devices can detect workplace focus gaps, which enable immediate contextual modifications to preserve productivity. Consumer Fitbit devices combined with lab-grade GSR sensors (like Shimmer3) allow consumers to reproduce optimal settings by revealing the precise arousal levels corresponding to creative breakthroughs. It is clear that there can be some positive change brought about by utilizing this technology to identify potential areas for improvement, but are there any drawbacks? There are some current limitations, including Apple Watch's 15-20% inaccuracy in HRV against clinical ECG during movement and eye trackers' vulnerability to outdoor glare. GSR cannot differentiate flow from minor stress. There are impending privacy dangers: HRV may expose mental fatigue to the employer, whereas eye tracking is able to uncover unconscious bias. Most importantly, there is currently no device on the market that combines all three measures (HRV + gaze + arousal) for thorough flow analysis.

Conclusions

The capability to measure flow states in real-time through biometrics, both lab and commercial grade, marks a shift in the understanding of what humans are capable of. We can utilize these tools to capture the many physiological signatures of one in a flow state, but how can this information be applied today? Well, there are some applications already emerging, such as in athletics. One such case is that professional athletes actively utilize HRV monitoring technology to optimize their training regimens and know when they are at an ideal heart rate or "flow zone." Some of the biggest challenges that this would need to overcome to move forward are concerns over sensor accuracy in dynamic situations, data privacy, and the automation of life as we know it. The key to the future is carefully combining these metrics—HRV, gaze, and arousal data to develop individualized flow-achieving tactics while preserving the inherent satisfaction of effortless mastery. The objective must be clear as we improve these tools: to create environments that allow flow to flourish naturally, not to engineer it. Despite the strength of the data, the human aspect cannot be replaced. The question remains: how can we use this technology and data to deliver something impactful? Feel free to leave your thoughts below; thank you for reading!

Further Links to Explore:

1. Patrick Mahomes: The Data Behind a Season with WHOOP - WHOOP

2. Brain Scans of Jazz Musicians Reveal How to Reach a Creative ‘Flow State’ | Scientific American

3. https://med.stanford.edu/news/all-news/2023/12/smartwatch-children-heart.html

References

1. Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. Harper and Row.

2. Harris, D. J., Vine, S. J., & Wilson, M. R. (2017). Flow and quiet eye: The role of attentional control in flow experience. Cognitive Processing, 18(3), 343–347. https://doi.org/10.1007/s10339-017-0794-9 

3. Metin, B., & Goktepe, A. K. (2017, January). EEG findings during Flow State. Research Gate. https://www.researchgate.net/publication/318390955_EEG_findings_during_flow_state 

4. Rácz, M., Becske, M., Magyaródi, T., Kitta, G., Szuromi, M., & Márton, G. (2025, April 7). Physiological assessment of the psychological flow state using wearable devices. Nature News. https://www.nature.com/articles/s41598-025-95647-x 

5. Tozman, T., Magdas, E. S., MacDougall, H. G., & Vollmeyer, R. (2015). Understanding the psychophysiology of flow: A driving simulator experiment to investigate the relationship between flow and heart rate variability. Computers in Human Behavior, 52, 408–418. https://doi.org/10.1016/j.chb.2015.06.023