Pulca's advanced health measurement technology has been rigorously tested and validated through clinical studies and scientific research. Below, we detail the scientific basis for each metric. Below, we detail the scientific basis and validation for each health metric currently available in the app.

Clinical Validation Study

Pulca's core technology was clinically validated by Wroclaw Medical University in a comprehensive study. The validation demonstrated high accuracy for contactless measurement of heart rate, heart rate variability, and breathing rate using facial video analysis (remote photoplethysmography):

• Heart Rate measurements demonstrated 99.9% accuracy (≤1 bpm error) compared to ECG

• Heart Rate Variability (HRV) was validated with RMSE of 6.3ms for SDNN compared to ECG

• Breathing Rate showed 97% of measurements with error ≤5 bpm compared to impedance pneumography

View the complete Clinical Validation Report

Scientific Publications

The remote photoplethysmography technology used in Pulca is supported by peer-reviewed research:

• "Remote photoplethysmography for cardiovascular monitoring" View on PubMed

Heart Rate

Measures the average number of heartbeats per minute, which reflects the current state of the autonomic nervous system and may be indicative of cardiovascular fitness.
Resource: Clinical validation by Wroclaw Medical University (2023). Demonstrated 99.9% accuracy (≤1 bpm error) compared to ECG measurements.
Citation: Okupnik T, Paleczny B. "Contactless measurement of heart rate, heart rate variability and breathing rate based on facial video analysis." Wroclaw Medical University, 2023.

Heart Rate Variability (HRV)

Analyzes variations between consecutive heartbeats, providing insights into autonomic nervous system function and stress resilience.
Resource: Clinical validation by Wroclaw Medical University (2023). RMSE of 6.3ms for SDNN and 0.38 for InRMSSD compared to ECG.
Citation: Okupnik T, Paleczny B. "Contactless measurement of heart rate, heart rate variability and breathing rate based on facial video analysis." Wroclaw Medical University, 2023.

Breathing Rate

Measures breaths per minute, reflecting respiratory status and indirectly stress level.
Resource: Clinical validation by Wroclaw Medical University (2023). 97% of measurements with error ≤5 bpm compared to impedance pneumography.
Citation: Okupnik T, Paleczny B. "Contactless measurement of heart rate, heart rate variability and breathing rate based on facial video analysis." Wroclaw Medical University, 2023.

Stress Index

Our Stress Index is an indirect measure of the physiological stress experienced by the body as assessed by the way the heart beats. Its values are usually in the range 0–10, although higher values, corresponding to particularly high levels of physiological stress, are also possible.

Stress Index mainly reflects the state of the autonomic nervous system (ANS), which controls the heart rhythm as well as various other involuntary physiological processes. It is determined based on the analysis of heart rate during our video-based measurement. In general, the lower the Stress Index, the better, as it suggests a relaxed state of a well-rested body, a good level of fitness, overall health, and/or young biological age.

Resource: Modified version of Baevsky's method, analyzing the distribution of heartbeat intervals derived from HRV.
Citation: "Remote photoplethysmography for cardiovascular monitoring." PubMed, PMID: 29897880.

Parasympathetic Activity

Parasympathetic Activity is measured through spectral analysis of heart rate variability (HRV) data. The calculation involves:

• Signal Processing: RR interval filtering to remove high-frequency noise

• Spectral Analysis: Computing power spectral density in key frequency bands (LF: 0.04-0.15 Hz and HF: 0.1-0.4 Hz)

• Final Calculation: Determining the relative contribution of HF power (associated with parasympathetic activity) to total power

Higher values indicate greater parasympathetic(rest-and-digest) influence, reflecting better recovery and relaxation states.
Measures the influence of the "rest and digest" branch of the autonomic nervous system, indicating recovery and relaxation states.
Resource: Spectral analysis methodology for HRV calculation as validated in clinical research.
Citation: "Remote photoplethysmography for cardiovascular monitoring." PubMed, PMID: 29897880.

Blood Pressure (systolic and diastolic)

Blood Pressure (BP) – the force per unit area exerted by circulating blood on the walls of arteries, expressed in millimeters of mercury (mmHg). It is defined by two values:
systolic blood pressure (SBP), which represents the maximum arterial pressure during left ventricular systole, and
diastolic blood pressure (DBP), which corresponds to the minimum arterial pressure during diastole, when the heart relaxes and refills with blood.
BP is a critical parameter in cardiovascular health, influenced by factors such as vascular resistance, cardiac output, autonomic regulation, and individual health conditions.

We estimate blood pressure by analyzing pulse wave velocity (PWV) derived from facial video data. The method utilizes pulse transit time (PTT) or pulse arrival time (PAT), which refers to the time it takes for the arterial pulse to travel between two points in the body. This is indirectly measured using facial scans. Changes in facial skin color, heart rate, and pulse are analyzed to compute systolic and diastolic BP values without the need for traditional cuff measurements.
By leveraging Multi Tonal Sensing (MTS) technology, making it possible to measure BP accurately across various lighting conditions, minor head movements, and skin tones, ensuring a non-invasive, camera-based solution for BP monitoring.

2024 ESC Guidelines on BP Classification
The 2024 ESC guidelines have updated the BP classification to support more accurate diagnosis and management:

• Normal BP (Non-elevated): SBP 90–120 mmHg, DBP 60–70 mmHg

• Elevated BP: SBP 121–135 mmHg, DBP 71–85 mmHg

• Hypertension: SBP ≥135 mmHg, DBP ≥85 mmHg

Citation:
McEvoy JW, McCarthy CP, Bruno RM, Brouwers S, Canavan MD, Ceconi C, Christodorescu RM, Daskalopoulou SS, Ferro CJ, Gerdts E, Hanssen H, Harris J, Lauder L, McManus RJ, Molloy GJ, Rahimi K, Regitz-Zagrosek V, Rossi GP, Sandset EC, Scheenaerts B, Staessen JA, Uchmanowicz I, Volterrani M, Touyz RM; ESC Scientifi c Document Group.
2024 ESC Guidelines for the management of elevated blood pressure and hypertension.
Eur Heart J. 2024 Oct 7;45(38):3912-4018. doi: 10.1093/eurheartj/ehae178. PMID: 39210715.

Cardiac Workload

Cardiac workload is calculated as the product of heart rate and systolic blood pressure (measured in mmHg/s), also known as the rate-pressure product (RPP) – a key indicator of cardiac oxygen consumption.
The higher the systolic arterial blood pressure, the harder the heart must work to eject a given amount of blood with each heartbeat. Similarly, at higher heart rates, cardiac oxygen demand increases, even if the work performed per heartbeat remains unchanged, as the cardiac muscles consume more oxygen during their excitation-contraction processes.

A lower heart rate and systolic blood pressure reduce the overall stress on the heart, while higher values indicate increased cardiac demand. The normal range for cardiac workload is 90-216 mmHg/s, derived from standard reference values for heart rate and systolic blood pressure.

Citations:

1. Hetzenecker A, Buchner S, Greimel T, Satzl A, Luchner A, Debl K, et al. Cardiac workload in patients with sleep-disordered breathing early after acute myocardial infarction. Chest. 2013;143:1294-3011.

2. Westerhof N. Cardiac work and efficiency. Cardiovasc Res. 2000;48:4-71.

3. Baller D, Bretschneider HJ, Hellige G. Validity of myocardial oxygen consumption parameters. Clin Cardiol. 1979;2:317-271.

4. Kitamura K, Jorgensen CR, Gobel FL, Taylor HL, Wang Y. Hemodynamic correlates of myocardial oxygen consumption during upright exercise. J Appl Physiol. 1972:32:516-22

Body Mass Index (BMI)

Your BMI is a measure of body weight relative to height and is commonly used to classify underweight, healthy weight, overweight, and obesity in adults.
BMI is calculated using the classic formula with user-input height and weight:
BMI = weight(kg) / height(m)2

BMI Classification Ranges (WHO/CDC):

Underweight	<18.5
Normal weight	18.5–24.9
Overweight	25.0–29.9
Obesity class I	30.0–34.9
Obesity class II	35.0–39.9
Obesity class III	≥40.0

Learn more:
- Keys, A., Fidanza, F., Karvonen, M. J., Kimura, N., & Taylor, H. L. (1972). Indices of relative weight and obesity. Journal of Chronic Diseases, 25(6), 329–343. https://doi.org/10.1016/0021-9681(72)90027-6
- World Health Organization
- CDC