Results

The SDK produces final results once the required success conditions have been met for the chosen measurement duration. By default (if you don’t change any settings), the SDK uses a 1-minute measurement. However, you can configure shorter or custom durations (see Measurement Preset).

Success conditions

Regardless of measurement length, two key conditions must be met for a successful result:

• The extracted photoplethysmographic signal must remain above a required quality threshold.
• The user’s face must be stable and properly positioned within the camera frame.

If these conditions aren’t consistently satisfied, the measurement may take longer (for example, if the user moves out of view or is in a dark environment).

Final metrics

Below are the metrics the SDK computes from the detected heart cycles once the measurement finishes:

HR (Heart Rate)

HR is the number of heartbeats per minute (bpm), reflecting the heart’s activity and overall cardiovascular function. It is a fundamental physiological parameter influenced by various factors, including age, fitness level, stress, medications, and overall health.

We estimate heart rate by analyzing changes in light reflection from the skin, which are caused by blood volume variations with each heartbeat.

In adults, a normal resting heart rate typically ranges between 60–100 bpm, with lower values generally indicating better cardiovascular efficiency. However, a resting HR above 80 bpm has been associated with an increased risk of cardiovascular diseases.

HR can fluctuate throughout the day due to physical activity, emotions, or autonomic nervous system regulation. While a consistently low or high HR may be normal for some individuals, significant deviations from typical ranges can signal potential health concerns and should be evaluated in a clinical context.

Heart Rate Variability (HRV)

HRV measures the variation in time intervals between consecutive heartbeats, reflecting the current state of the heart and the autonomic nervous system. Here, we assess HRV using the SDNN index (Standard Deviation of Normal-to-Normal Interbeat Intervals), a widely recognized indicator of autonomic regulation.

In general, higher HRV is associated with better adaptability and cardiovascular health, while lower HRV suggests reduced autonomic flexibility and is considered an independent risk factor for cardiovascular events and mortality.

However, HRV is influenced by several factors, including age, gender, time of day, lifestyle, and fitness level, meaning there is no universal “normal” range for HRV values, including SDNN.

It’s also important to note that SDNN values depend on the duration of measurement. The HRV results obtained from this 1-minute measurement should not be directly compared with results from longer or shorter recordings, as measurement duration significantly impacts the outcome.

To track HRV trends effectively, it’s best to compare readings taken at the same time of day. HRV typically peaks in the early morning (around 6–7 AM) and reaches its lowest levels in the early evening. A drop in HRV compared to your usual values may indicate fatigue, stress, poor fitness, or overtraining. You can work on improving your HRV by focusing on better sleep, recovery, and regular physical activity.

Resource:

1. Olivieri F, Biscetti L, Pimpini L, Pelliccioni G, Sabbatinelli J, Giunta S. Heart rate variability and autonomic nervous system imbalance: Potential biomarkers and detectable hallmarks of aging and inflammaging. Ageing Res Rev. 2024 Nov;101:102521. doi: 10.1016/j.arr.2024.102521. Epub 2024 Sep 27. PMID: 39341508.

Breathing Rate (BR)

BR is the frequency of respiratory cycles, expressed in breaths per minute (bpm). Respiration is monitored by detecting chest or upper body movements during breathing, or by analyzing subtle variations in skin color associated with oxygenation and blood flow. Breathing rate is estimated using spectral analysis by selecting the signal with the most distinct peak within the 0.1–0.5 Hz range (6–30 breaths per minute), ensuring it represents a true peak rather than a slight variation from neighboring values.

Normal ranges for breathing rate:

• Adults: 12–20 bpm
• Adults over 65 years: 12–28 bpm
• Adults over 80 years: 10–30 bpm

Stress Index (SI)

SI is a metric derived from heart rate variability (HRV) analysis, reflecting the overall state and functional reserve of cardiovascular regulatory systems, particularly the balance between the sympathetic and parasympathetic branches of the autonomic nervous system.

SI is calculated using a modified version of Baevsky’s method, which analyzes the distribution of heartbeat intervals rather than relying solely on traditional statistical measures. This approach incorporates quartile-based dispersion measures and the overall shape of the histogram of interbeat intervals.

The Stress Index is scaled from 0 to 10, with values between 0 and 4 considered normal. Mild emotional or physical stress may cause a moderate increase, while significant stress can lead to much higher values:

• Values above 5 may indicate a high level of stress, often linked to severe psycho-emotional strain, overwork, or cardiovascular dysfunction.
• Values above 9 may suggest a critical state, potentially indicating a preinfarction condition.

It’s important to note that both the Stress Index and the subjectively perceived level of stress are highly individual, varying based on personal physiological and psychological factors.

Parasympathetic Activity (PA)

PA is a measure of parasympathetic nervous system activity based on spectral analysis of heart rate variability (HRV).

It is calculated as follows:

1. Signal Processing – The RR interval tachogram is low-pass filtered (up to 0.4 Hz) to remove high-frequency noise.
2. Spectral Analysis – Power spectral density (PSD) is computed, and power is extracted from two frequency bands:
   • Low Frequency (LF): 0.04–0.15 Hz
   • High Frequency (HF): 0.1–0.4 Hz
3. PA Calculation – The relative contribution of HF power (associated with parasympathetic activity) to the total power in both bands: HF / (LF + HF) × 100%

PA reflects the dominance of parasympathetic modulation in heart rate control, with higher values indicating greater parasympathetic influence.

Resource:

1. Olivieri F, Biscetti L, Pimpini L, Pelliccioni G, Sabbatinelli J, Giunta S. Heart rate variability and autonomic nervous system imbalance: Potential biomarkers and detectable hallmarks of aging and inflammaging. Ageing Res Rev. 2024 Nov;101:102521. doi: 10.1016/j.arr.2024.102521. Epub 2024 Sep 27. PMID: 39341508.

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.

Resources:

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;143:1294-301.
2. Westerhof N. Cardiac work and efficiency. Cardiovasc Res. 2000;48:4-7.
3. Baller D, Bretschneider HJ, Hellige G. Validity of myocardial oxygen consumption parameters. Clin Cardiol. 1979;2:317-27.
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.

Blood Pressure (BP)

BP is 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. Shen.AI’s approach leverages 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

These values are used to guide treatment decisions and monitor patient outcomes, with an emphasis on Home Blood Pressure Monitoring (HBPM) for a more reliable assessment.

Resource:

1. 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 Scientific 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.

Body Mass Index (BMI) classification

Body Mass Index (BMI) is a widely used method for assessing an individual’s body weight in relation to their height. It is calculated by dividing a person’s weight in kilograms by the square of their height in meters (kg/m²). BMI provides a simple and effective way to classify individuals by weight, helping identify potential health risks associated with being underweight or overweight, including cardiovascular diseases, diabetes, and metabolic disorders.

We now classify BMI based on the extended WHO-defined classes, which offer a more precise understanding of health risk levels:

• Underweight (Severe thinness): BMI < 16
• Underweight (Moderate thinness): BMI 16.0–16.9
• Underweight (Mild thinness): BMI 17.0–18.4
• Normal range: BMI 18.5–24.9
• Overweight (Pre-obese): BMI 25.0–29.9
• Obese (Class I): BMI 30.0–34.9
• Obese (Class II): BMI 35.0–39.9
• Obese (Class III): BMI ≥ 40

BMI is estimated using facial scans, applying advanced image processing algorithms that analyze facial features and proportions correlated with body mass index.

Resources:

1. World Health Organization. (2000). Obesity: Preventing and managing the global epidemic (WHO Technical Report Series No. 894). https://apps.who.int/iris/handle/10665/42330
2. World Health Organization. (2005). The SuRF Report 2: The Surveillance of Risk Factors Report Series. World Health Organization. p. 22.https://iris.who.int/handle/10665/43190

Accessing the results

Call getMeasurementResults() to retrieve final metrics from the most recently completed measurement. If no measurement has finished successfully yet, a null value will be returned. Below is a summary of the returned structure:

class MeasurementResults {
  double heart_rate_bpm;                 // Heart rate, rounded to 1 BPM
  double? hrv_sdnn_ms;                   // HRV, SDNN metric, rounded to 1 ms
  double? hrv_lnrmssd_ms;                // HRV, lnRMSSD metric, rounded to 0.1 ms
  double? stress_index;                  // Stress Index, rounded to 0.1
  double? parasympathetic_activity;      // Parasympathetic activity, rounded to 1%
  double? breathing_rate_bpm;            // Breathing rate, rounded to 1 BPM
  double? systolic_blood_pressure_mmhg;  // Systolic blood pressure, rounded to 1 mmHg
  double? diastolic_blood_pressure_mmhg; // Diastolic blood pressure, rounded to 1 mmHg
  double? cardiac_workload_mmhg_per_sec; // Cardiac workload, rounded to 1 mmHg/s
  double? age_years;                     // Estimated age, rounded to 1 year
  double? bmi_kg_per_m2;                 // Estimated BMI, rounded to 0.01
  BmiCategory? bmi_Category;             // Estimated BMI category
  List<Heartbeat?> heartbeats;           // List of detected heartbeats
  double average_signal_quality;         // Average signal quality metric
}
 
class Heartbeat {
  double start_location_sec;  // exact start time in seconds
  double end_location_sec;    // exact end time in seconds
  double duration_ms;         // heartbeat duration, rounded to 1 ms
}
 
enum BmiCategory {
  underweightSevere,
  underweightModerate,
  underweightMild,
  normal,
  overweight,
  obeseClassI,
  obeseClassII,
  obeseClassIII,
}
 
var results = await ShenaiSDK.getMeasurementResults();

import { getMeasurementResults } from 'react-native-shenai-sdk';
 
interface Heartbeat {
  startLocationSec: number;  // exact start time in seconds
  endLocationSec: number;    // exact end time in seconds
  durationMs: number;        // heartbeat duration, rounded to 1 ms
}
 
export const enum BmiCategory {
  UNDERWEIGHT_SEVERE = 0,
  UNDERWEIGHT_MODERATE,
  UNDERWEIGHT_MILD,
  NORMAL,
  OVERWEIGHT,
  OBESE_CLASS_I,
  OBESE_CLASS_II,
  OBESE_CLASS_III,
}
 
interface MeasurementResults {
  heartRateBpm: number;                           // Heart rate, rounded to 1 BPM
  hrvSdnnMs: number | null;                       // HRV, SDNN metric, rounded to 1 ms
  hrvLnrmssdMs: number | null;                    // HRV, lnRMSSD metric, rounded to 0.1 ms
  stressIndex: number | null;                     // Stress Index, rounded to 0.1
  parasympatheticActivity: number | null;         // Parasympathetic activity, rounded to 1%
  breathingRateBpm: number | null;                // Breathing rate, rounded to 1 BPM
  systolicBloodPressureMmhg: number | null;       // Systolic blood pressure, rounded to 1 mmHg
  diastolicBloodPressureMmhg: number | null;      // Diastolic blood pressure, rounded to 1 mmHg
  cardiacWorkloadMmhgPerSec: number | null;       // Cardiac workload, rounded to 1 mmHg/s
  ageYears: number | null;                        // Estimated age, rounded to 1 year
  bmiKgPerM2: number | null;                      // Estimated BMI, rounded to 0.01
  bmiCategory?: BmiCategory | null;               // BMI category
  heartbeats: Heartbeat[];                        // Array of detected heartbeats
  averageSignalQuality: number;                   // Average signal quality metric
}
 
async function example() {
  const results: MeasurementResults = await getMeasurementResults();
}

class MeasurementResults {
  var heartRateBpm: Double                   // Heart rate, rounded to 1 BPM
  var hrvSdnnMs: NSNumber?                   // HRV, SDNN metric, rounded to 1 ms
  var hrvLnrmssdMs: NSNumber?                // HRV, lnRMSSD metric, rounded to 0.1 ms
  var stressIndex: NSNumber?                 // Stress Index, rounded to 0.1
  var parasympatheticActivity: NSNumber?     // Parasympathetic activity, rounded to 1%
  var breathingRateBpm: NSNumber?            // Breathing rate, rounded to 1 BPM
  var systolicBloodPressureMmhg: NSNumber?   // Systolic blood pressure, rounded to 1 mmHg
  var diastolicBloodPressureMmhg: NSNumber?  // Diastolic blood pressure, rounded to 1 mmHg
  var cardiacWorkloadMmhgPerSec: NSNumber?   // Cardiac workload, rounded to 1 mmHg/s
  var ageYears: NSNumber?                    // Estimated age, rounded to 1 year
  var bmiKgPerM2: NSNumber?                  // Estimated BMI, rounded to 0.01
  var bmiCategory: BmiCategory?              // BMI category
  var heartbeats: [Heartbeat]                // Array of detected heartbeats
  var averageSignalQuality: Double           // Average signal quality metric
}
 
enum BmiCategory {
  case underweightSevere
  case underweightModerate
  case underweightMild
  case normal
  case overweight
  case obeseClassI
  case obeseClassII
  case obeseClassIII
};
 
class Heartbeat {
  var startLocationSec: Double  // exact start time in seconds
  var endLocationSec: Double    // exact end time in seconds
  var durationMs: Double        // heartbeat duration, rounded to 1 ms
}
 
let results = ShenaiSDK.getMeasurementResults()

class MeasurementResults {
  public float hrBpm;                                // Heart rate, rounded to 1 BPM
  public Optional<Float> hrvSdnnMs;                  // HRV, SDNN metric, rounded to 1 ms
  public Optional<Float> hrvLnrmssdMs;               // HRV, lnRMSSD metric, rounded to 0.1 ms
  public Optional<Float> stressIndex;                // Stress Index, rounded to 0.1
  public Optional<Float> parasympatheticActivity;    // Parasympathetic activity, rounded to 1%
  public Optional<Float> brBpm;                      // Breathing rate, rounded to 1 BPM
  public Optional<Float> systolicBloodPressureMmhg;  // Systolic blood pressure, rounded to 1 mmHg
  public Optional<Float> diastolicBloodPressureMmhg; // Diastolic blood pressure, rounded to 1 mmHg
  public Optional<Float> cardiacWorkloadMmhgPerSec;  // Cardiac workload, rounded to 1 mmHg/s
  public Optional<Float> ageYears;                   // Estimated age, rounded to 1 year
  public Optional<Float> bmiKgPerM2;                 // Estimated BMI, rounded to 0.01
  public Optional<BmiCategory> bmiCategory;          // BMI category
  public Heartbeat[] heartbeats;                     // Array of detected heartbeats
  public float averageSignalQuality;                 // Average signal quality metric
}
 
public enum BmiCategory {
    UNDERWEIGHT_SEVERE,
    UNDERWEIGHT_MODERATE,
    UNDERWEIGHT_MILD,
    NORMAL,
    OVERWEIGHT,
    OBESE_CLASS_I,
    OBESE_CLASS_II,
    OBESE_CLASS_III;
}
 
class Heartbeat {
  public float startLocationSec;  // exact start time in seconds
  public float endLocationSec;    // exact end time in seconds
  public float durationMs;        // heartbeat duration, rounded to 1 ms
}
 
MeasurementResults results = shenaiSDKHandler.getMeasurementResults();

interface MeasurementResults {
  heart_rate_bpm: number;                           // Heart rate, rounded to 1 BPM
  hrv_sdnn_ms: number | null;                       // HRV, SDNN metric, rounded to 1 ms
  hrv_lnrmssd_ms: number | null;                    // HRV, lnRMSSD metric, rounded to 0.1 ms
  stress_index: number | null;                      // Stress Index, rounded to 0.1
  parasympathetic_activity: number | null;          // Parasympathetic activity, rounded to 1%
  breathing_rate_bpm: number | null;                // Breathing rate, rounded to 1 BPM
  systolic_blood_pressure_mmhg: number | null;      // Systolic blood pressure, rounded to 1 mmHg
  diastolic_blood_pressure_mmhg: number | null;     // Diastolic blood pressure, rounded to 1 mmHg
  cardiac_workload_mmhg_per_sec: number | null;     // Cardiac workload, rounded to 1 mmHg/s
  age_years: number | null;                         // Estimated age, rounded to 1 year
  bmi_kg_per_m2: number | null;                     // Estimated BMI, rounded to 0.01
  bmiCategory: BmiCategory | null;                  // BMI category
  heartbeats: Heartbeat[];                          // Array of detected heartbeats
  average_signal_quality: number;                   // Average signal quality metric
}
 
interface Heartbeat {
  start_location_sec: number;  // exact start time in seconds
  end_location_sec: number;    // exact end time in seconds
  duration_ms: number;         // heartbeat duration, rounded to 1 ms
}
 
 
const results = await shenaiSDK.getMeasurementResults();

Additional outputs

Some additional outputs are provided, which may be used to better instruct the user about how the measurement process works. Note that the outputs will only be available after a successful measurement.

rPPG signal

You can access the final rPPG signal from the measurement by calling the getFullPpgSignal() method:

var ppgSignal = await ShenaiSDK.getFullPpgSignal();

import { getFullPpgSignal } from 'react-native-shenai-sdk';
 
const ppgSignal = await getFullPpgSignal();

let ppgSignal = ShenaiSDK.getFullPPGSignal()

double[] ppgSignal = shenaiSDKHandler.getFullPpgSignal();

const ppgSignal = await shenaiSDK.getFullPpgSignal();

The signal will be returned as a list of floating point values, where each value represents the intensity of the signal at a given point in time. The signal is sampled at the camera frame rate, which is usually 30 FPS.

Facial regions visualizations

You can access an image of the region of the face which was used to extract the rPPG signal, as well as the signal intensity map.

var faceImage = await ShenaiSDK.getFaceTexturePng();
var signalImage = await ShenaiSDK.getSignalQualityMapPng();

import { getFaceTexturePng, getSignalQualityMapPng } from 'react-native-shenai-sdk';
 
const faceImage = await getFaceTexturePng();
const signalImage = await getSignalQualityMapPng();

let faceImage = ShenaiSDK.getFaceTexturePng()
let signalImage = ShenaiSDK.getSignalQualityMapPng()

byte[] faceImage = shenaiSDKHandler.getFaceTexturePng();
byte[] signalImage = shenaiSDKHandler.getSignalQualityMapPng();

const faceImage = await shenaiSDK.getFaceTexturePng();
const signalImage = await shenaiSDK.getSignalQualityMapPng();

The images will be returned as PNG-encoded byte arrays which you can decode and display as you wish, for example along with explaining the measurement results.