Biometrics and Stress Detection: How Does It Work?

Author: Adelisa Pelinkovic

Introduction

Whether it is due to morning rush-hour traffic or an upcoming job interview, stress is a part of our everyday lives. While some forms of stress, such as short-term stress, have been found to be beneficial, stress has overall received a bad reputation for its effects on our health. Based on past research, stress has been linked to serious physical and mental health issues like anxiety, depression, cardiovascular diseases, and immune system problems. With health on the line, stress detection is crucial for maintaining a healthy, well-balanced life.

Physiologically, stress is associated with activation of the sympathetic nervous system. This is the rapid response system in our brain that facilitates immediate motor action, more commonly known as our “fight or flight” response. Increased sympathetic activity  is  correlated with arousal, which can be interpreted as stress. By detecting stress, we can identify situations that cause high levels of stress, so people can learn to manage them in these situations. Tools such as Galvanic Skin Response (GSR), Heart Rate Variability (HRV), vocal cues, and facial cues can provide physiological insights to support long-term stress management.

Galvanic Skin Response

GSR is a measure of changes in skin conductance. This essentially means it measures someone’s sweat activity, usually from their finger. The more sweat activity, the greater the skin conductivity, and the higher the GSR response detected. This gives insight into the subject’s emotional engagement, arousal, stress, and excitement. GSR is a great tool for investigating stress detection because it can show peaks and peaks per minute in response to a stimulus. It is also an easy-to-use, wearable sensor that can be utilized in biometric labs. GSR provides real-time data and is less intrusive than other biometric tools.

One interesting study that involved GSR and stress detection focused on stressful situations faced by car drivers. In the study, the researchers used both hand and foot GSR sensors to identify which driving situations stressed drivers the most. Results from this study were later implemented into a dataset that is now “used to train adaptive driver assistance systems that monitor stress levels and suggest interventions for safer driving” (Lakshmanan et al., 2025). Because GSR detects real-time stress responses so well, it is an incredibly valuable tool for detecting physiological stress.

With all the positives brought about by GSR, there are still some limitations associated with it. For one, since it is a wearable device, it is sensitive to movement. This means that 

stress detection studies that wish to use GSR data must take place in a lab, with the participant remaining as still as possible. Any movement will affect the results by potentially changing the data. The lab temperature should also be considered. If the room is warmer, the person may sweat more. This can lead to misinterpretation of study results, since greater sweat activity means higher skin conductivity, and higher skin conductivity indicates higher arousal.

Heart Rate Variability

Heart Rate Variability, or HRV, measures the fluctuations that occur between heartbeats. Researchers can use HRV to find the connection between heart rate and the sympathetic nervous system. The way HRV can be interpreted is that lower variability indicates greater stress and greater reliance on the sympathetic nervous system. Alternatively, when variability is higher, there is less stress and greater reliance on the parasympathetic nervous system (Crecelius, 2024). HRV has been used in research as an “objective assessment of stress and mental health” (Kim et al., 2018).

A pitfall of HRV is that, because it reflects only physiological data, it should be paired with self-reported surveys and other lifestyle and behavioral research to paint the full picture of stress detection. Another limitation to consider is the reliability of the sensors.  Some  rely  on  optical sensors or colored lights to measure someone’s pulse either at the finger or wrist. Differences in skin type and skin tone of subjects can have an impact on the accuracy of the HRV’s results.

Vocal Cues

Voice analysis is another biometric tool that provides insights into stress (Scherer, 2003). Voice analysis has been used previously for emotion detection, identifying perceived gender and age, emotional dimensions, and prosody. Using these metrics, researchers can identify physiological data correlated with stress. One interesting tool I found for vocal analysis and stress detection is from a company called Sonde. Their goal, per their website, is to “fill the gap left by traditional health trackers by providing ‘above-the-neck’ insights that reveal how stress shows up in everyday life” (Sonde Health). They use voice recordings to detect changes in tone, strain, and flow of speech. They then give the person a “Stress Score” that compares the voice recording to one from a person who is stressed. As a company, Sonde markets this product to consumers, other companies, and app developers to help people detect and manage stress, especially when it is high.

While there are revolutionary developments in stress-related vocal analysis, some limitations remain. If stress detection based on voice analysis is conducted in a lab, the conditions may cause subjects to speak differently than they would on any other day. Similarly, if emotional dimensions in the voice signal arousal, researchers may misinterpret stress as other dimensions, such as excitement. This is why using multiple types of biometric measures  is  necessary.

Facial Expression Analysis

Facial expression analysis is another valuable biometric tool for stress detection. This type of analysis measures facial muscle movements and classifies expressions into seven basic emotions: joy, sadness, anger, disgust, surprise, fear, and contempt. Facial expressions are classified using the Facial Analysis Coding System (Ekman and Friesen 1978), which distinguishes between them. One study found that facial expression analysis was a strong indicator of stress scores (Rupp et al. 2025). This study specifically points to how sadness, anger, and fear are associated with high arousal and, therefore, linked to stress. It also mentions how movement in the musculus  corrugator supercilii (or the muscles between your eyebrows that create the eleven lines when you furrow your eyebrows) is associated  with  stress and negative facial expressions, such as anger.

There are still some l imitations in facial expression analysis and stress detection. For one, as with voice analysis, people may emote differently in lab conditions than in the wild. Similarly, stress may not always be picked up by the FACS the way that the seven basic emotions are. More interpretation will also be needed, rather than relying solely on facial expression analysis.

Conclusions

Stress is something everyone will have to deal with repeatedly in their lifetime. It comes about differently for everyone, and everyone deals with stress in their own ways. Since it varies so much from person to person, collecting physiological data with biometric tools is a great way to gain more information about an individual’s stress levels. By combining biometric data from tools such as GSR, HRV, voice analysis, and facial expression analysis with other factors such as medical history, biological data, and self-reported surveys, researchers will have a clearer picture of how to help people better manage their stress. This is crucial because detecting and managing stress is critical to living a healthy lifestyle. With the help of biometrics, the stress of trying to figure out what is causing your stress is reduced.

Interested in Learning More?

1. A New Era in Stress Monitoring: A Review of Embedded Devices and Tools for Detecting Stress in the Workplace

2. Stress Detection Using Machine Learning and Deep Learning Techniques: A Systematic Review and Meta-Analysis

3. Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques

4. Stress and Heart Rate Variability: A Meta-Analysis and Review of the Literature

5. Sonde Health

6. Stress can be detected during emotion-evoking smartphone use: a pilot study using machine learning

7. https://www.audeering.com/

References

1. Crecelius, Anne R. “Heart Rate Variability – What To Know About This Biometric Most Fitness Trackers Measure.” The Conversation, 12 Nov. 2024,

2. Ekman, Paul, and Wallace V. Friesen. "Facial action coding system." Environmental Psychology & Nonverbal Behavior (1978).

3. Kim, Hye-Geum, et al. “Stress and Heart Rate Variability: A Meta-Analysis and Review of the Literature.” Psychiatry Investigation, U.S. National Library of Medicine, 28 Feb. 2018, pmc.ncbi.nlm.nih.gov/articles/PMC5900369/#sec14.

4. Lakshmanan, Logesh Kumar Kulanthaivel, et al. “Stress Detection Using Machine Learning and Deep Learning Techniques: A Systematic Review and Meta-Analysis .” SpringerLink, Springer Netherlands, 24 Oct. 2025, link.springer.com/article/10.1007/s11831-025-10429-y.

5. Rupp, Lydia Helene, et al. “Stress Can Be Detected during Emotion-Evoking Smartphone Use: A Pilot Study Using Machine Learning.” Frontiers, Frontiers, 29 Apr. 2025, www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2025.1578917/full.

6. “Stress Monitoring through Vocal Biomarker Analysis.” Sonde Health, 2025, www.sondehealth.com/stress.

7. Scherer, Klaus R. "Vocal communication of emotion: A review of research paradigms." Speech communication 40.1-2 (2003): 227-256.