FDA Estimator

NOTE

Web SDKs Only Realtime estimation is currently available for web-based SDKs only (JavaScript, React, Vue). It is not available for mobile SDKs (iOS, Android, Flutter, React Native).

Overview

The FDA Estimator is a medical-grade heart rate measurement system that uses advanced signal processing to analyze photoplethysmography (PPG) signals from facial video. It provides FDA-level accuracy without requiring external AI models, making it ideal for medical applications and instant initialization.

IMPORTANT

Proprietary Technology The FDA Estimator is proprietary software developed by PanopticAI. The core algorithm runs in WebAssembly for intellectual property protection.

Key Features

✅ Medical-Grade: FDA-level accuracy
✅ Instant Init: No model loading required
✅ Zero Dependencies: No external files needed
✅ Deterministic: Same input → same output
✅ Lightweight: < 200 KB memory footprint
✅ Adaptive: Early results in 5 seconds, optimal in 10

Basic Usage

typescript

import { createVitalSignCamera, RealtimeEstimatorType } from 'ts-vital-sign-camera';

const camera = createVitalSignCamera({
  realtimeEstimationConfig: {
    estimatorType: RealtimeEstimatorType.Fda,
    earlyEstimation: true,
    minDuration: 10,
    minConfidence: 0.6
  }
});

camera.onVideoFrameProcessed = (event) => {
  const estimation = event.realtimeEstimation;
  if (!estimation) return;
  
  console.log(`HR: ${estimation.heartRate} BPM`);
  console.log(`Confidence: ${estimation.confidence}`);
  console.log(`SNR: ${estimation.snr}`);
});

Configuration

For Consumer Applications

typescript

{
  estimatorType: RealtimeEstimatorType.Fda,
  earlyEstimation: true,   // Show early results
  minDuration: 5,          // Faster feedback
  minConfidence: 0.4,      // Lower threshold
  debug: false
}

For Medical Applications

typescript

{
  estimatorType: RealtimeEstimatorType.Fda,
  earlyEstimation: false,  // Wait for full accuracy
  minDuration: 10,         // Full 10 seconds
  minConfidence: 0.7,      // High confidence threshold
  debug: false
}

SDK Behavior

Initialization

typescript

// FDA estimator initializes instantly (no async loading)
const camera = createVitalSignCamera({
  realtimeEstimationConfig: {
    estimatorType: RealtimeEstimatorType.Fda
  }
});

// Ready immediately - no waiting required
camera.startScan();

Processing Timeline

0s ────► 5s ────► 10s ────► 30s
│        │        │         │
│        │        │         └─ Scan complete
│        │        └─ Optimal accuracy (10s window)
│        └─ First estimate (5s window)
└─ Scan starts (instant init)

Adaptive Time Window

The FDA estimator uses an adaptive time window that grows as more data arrives:

Time Elapsed	Window Size	Accuracy	Status
5 seconds	5s	±5-8 BPM	Early estimate
7 seconds	7s	±3-5 BPM	Improving
10+ seconds	10s	±2-3 BPM	Optimal ✓

typescript

camera.onVideoFrameProcessed = (event) => {
  const elapsed = getCurrentScanTime();
  
  if (elapsed < 7) {
    showHeartRate(estimation.heartRate, 'Early estimate');
  } else if (elapsed < 10) {
    showHeartRate(estimation.heartRate, 'Refining...');
  } else {
    showHeartRate(estimation.heartRate, 'Stable ✓');
  }
});

Performance Characteristics

Metric	Value
Initialization	Instant (< 10ms)
First Result	~5 seconds
Optimal Accuracy	~10 seconds
Processing Speed	5-10ms per frame
Memory Usage	< 200 KB
Bundle Size	~100 KB (WASM)
Typical Error	±2-3 BPM
Heart Rate Range	50-140 BPM

Quality Metrics

Signal-to-Noise Ratio (SNR)

The FDA estimator provides SNR values to indicate signal quality:

SNR	Confidence	Quality	Interpretation
10+	1.0	Excellent	Trust completely ✓
8-10	0.8-1.0	Very Good	Highly reliable ✓
6-8	0.6-0.8	Good	Reliable
4-6	0.4-0.6	Fair	Acceptable
2-4	0.2-0.4	Poor	Consider retrying
< 2	0.0-0.2	Very Poor	Retry required

typescript

camera.onVideoFrameProcessed = (event) => {
  const estimation = event.realtimeEstimation;
  if (!estimation) return;
  
  if (estimation.snr && estimation.snr > 6) {
    showQualityIndicator('excellent');
  } else if (estimation.snr && estimation.snr > 4) {
    showQualityIndicator('good');
  } else {
    showQualityIndicator('poor');
    showTip('Improve lighting or reduce movement');
  }
});

Advantages

🏥 Medical-Grade Quality

FDA-level accuracy
Matches validated server implementation
Suitable for medical applications
Regulatory compliance friendly

📦 Zero Dependencies

No model files to download
Instant initialization
Works offline immediately
Smaller bundle size

⚡ Fast Initialization

No async loading required
Ready to use immediately
No waiting for models
Simpler error handling

🔒 IP Protection

Core algorithm in WebAssembly
Proprietary implementation protected
Binary format prevents reverse engineering

🎯 Deterministic

Same input → same output
No AI randomness
Reproducible results
Easier to debug

When to Use

✅ Best For

Medical and healthcare applications
Regulatory compliance requirements
Instant initialization needed
Minimal bundle size required
Offline-first applications
Embedded systems and IoT devices

❌ Avoid If

Users will be moving significantly
Lighting conditions are uncontrolled
Maximum motion tolerance needed
Open-source licensing required
AI-powered features preferred

Troubleshooting

Low Confidence Scores

Symptoms: Confidence < 0.6, unstable readings

Solutions:

typescript

// 1. Improve environmental conditions
const instructions = [
  "Use natural daylight",
  "Avoid backlighting",
  "Ensure even illumination",
  "Remove shadows from face"
];

// 2. Reduce motion
const motionTips = [
  "Stay completely still",
  "Don't talk during measurement",
  "Use phone stand if possible",
  "Maintain steady distance"
];

// 3. Wait longer
{
  minDuration: 10,  // Allow full 10-second window
  minConfidence: 0.6
}

// 4. Check face detection - note: face detection events vary by SDK
// For web SDKs, check scanConditions in VideoFrameProcessedEvent
camera.onVideoFrameProcessed = (event) => {
  if (event.scanConditions && !event.scanConditions.centered) {
    showWarning('Keep face centered and stable');
  }
});

Unstable Readings

Symptoms: Heart rate jumps around

Solutions:

typescript

// Increase minimum confidence threshold
{
  minConfidence: 0.7  // Higher threshold for stability
}

// Wait for full window
{
  earlyEstimation: false,  // Disable early results
  minDuration: 10
}

// Guide user
camera.onVideoFrameProcessed = (event) => {
  const estimation = event.realtimeEstimation;
  if (!estimation) return;
  
  if (estimation.confidence < 0.6) {
    showTip('Please stay still and improve lighting');
  }
});

Best Practices

1. Progressive Feedback

typescript

camera.onVideoFrameProcessed = (event) => {
  const estimation = event.realtimeEstimation;
  if (!estimation) return;
  
  const elapsed = getCurrentScanTime();
  
  if (elapsed < 5) {
    showMessage("Analyzing... Please stay still");
  } else if (elapsed < 10) {
    if (estimation.confidence > 0.4) {
      showHeartRate(estimation.heartRate, "Refining...");
    } else {
      showMessage("Adjusting... Improve lighting if possible");
    }
  } else {
    if (estimation.isStable) {
      showHeartRate(estimation.heartRate, "Stable ✓");
    } else {
      showMessage("Low confidence. Try again with better conditions.");
    }
  }
});

2. Quality Indicators

typescript

function getQualityIndicator(estimation: RealtimeEstimation): string {
  if (estimation.confidence >= 0.8) return "Excellent ⭐⭐⭐⭐⭐";
  if (estimation.confidence >= 0.6) return "Very Good ⭐⭐⭐⭐";
  if (estimation.confidence >= 0.4) return "Good ⭐⭐⭐";
  if (estimation.confidence >= 0.2) return "Fair ⭐⭐";
  return "Poor ⭐";
}

camera.onVideoFrameProcessed = (event) => {
  const estimation = event.realtimeEstimation;
  if (!estimation) return;
  
  updateQualityDisplay(getQualityIndicator(estimation));
});

3. Retry Logic

typescript

let retryCount = 0;
const MAX_RETRIES = 3;

camera.onVideoFrameProcessed = (event) => {
  const result = event.healthResult;
  if (!result) return;
  
  if (result.confidence < 0.6 && retryCount < MAX_RETRIES) {
    retryCount++;
    showMessage(`Low confidence. Retrying (${retryCount}/${MAX_RETRIES})...`);
    camera.reset();
    camera.startScan();
  } else {
    retryCount = 0;
    displayFinalResult(result);
  }
});

4. User Guidance

typescript

const guidanceMessages = {
  lowConfidence: "Improve lighting or stay still",
  noFace: "Position your face in the center",
  unstable: "Keep your face steady",
  success: "Measurement complete ✓"
};

camera.onVideoFrameProcessed = (event) => {
  const estimation = event.realtimeEstimation;
  if (!estimation) return;
  
  if (estimation.confidence < 0.4) {
    showGuidance(guidanceMessages.lowConfidence);
  } else if (estimation.isStable) {
    showGuidance(guidanceMessages.success);
  }
});

Comparison with ME-rPPG Estimator

Feature	FDA	ME-rPPG
Accuracy	⭐⭐⭐⭐⭐ Medical-grade	⭐⭐⭐⭐⭐ State-of-the-art
Motion Tolerance	⭐⭐⭐⭐ Good	⭐⭐⭐⭐⭐ Excellent
Lighting Tolerance	⭐⭐⭐⭐ Good	⭐⭐⭐⭐⭐ Excellent
Initialization	⭐⭐⭐⭐⭐ Instant	⭐⭐⭐ (1-2s)
Bundle Size	⭐⭐⭐⭐⭐ ~100 KB	⭐⭐⭐ ~10 MB
Memory Usage	⭐⭐⭐⭐⭐ < 200 KB	⭐⭐⭐ ~4 MB
Licensing	Proprietary	Open-source
First Result	~5 seconds	~3 seconds
Best For	Medical apps, embedded	Consumer apps, challenging conditions

Example: Complete Implementation

typescript

import { createVitalSignCamera, RealtimeEstimatorType } from 'ts-vital-sign-camera';

// Create camera with FDA estimator
const camera = createVitalSignCamera({
  realtimeEstimationConfig: {
    estimatorType: RealtimeEstimatorType.Fda,
    earlyEstimation: true,
    minDuration: 10,
    minConfidence: 0.6,
    debug: false
  }
});

// FDA is ready immediately (no async loading)
document.getElementById('start-btn').disabled = false;

// Handle realtime updates
camera.onVideoFrameProcessed = (event) => {
  const estimation = event.realtimeEstimation;
  if (!estimation) return;
  
  updateHeartRateDisplay(estimation.heartRate);
  updateConfidenceBar(estimation.confidence);
  
  // Show SNR if available
  if (estimation.snr) {
    updateSNRDisplay(estimation.snr);
  }
  
  // Quality indicator
  if (estimation.isStable && estimation.confidence > 0.7) {
    showQualityIndicator('excellent');
  } else if (estimation.confidence > 0.5) {
    showQualityIndicator('good');
  } else {
    showQualityIndicator('poor');
    showTip('Improve lighting or reduce movement');
  }
});

// Handle scan completion
// Check for final results
let scanCompleted = false;
camera.onVideoFrameProcessed = (event) => {
  const results = event.healthResult;
  if (results && !scanCompleted) {
    scanCompleted = true;
    if (results.confidence > 0.7) {
      displayFinalResults(results);
    } else {
      showRetryOption();
    }
  }
});

// Start scan
document.getElementById('start-btn').onclick = () => {
  camera.startScan();
};

Example: Medical Application

typescript

// Medical-grade configuration
const camera = createVitalSignCamera({
  realtimeEstimationConfig: {
    estimatorType: RealtimeEstimatorType.Fda,
    earlyEstimation: false,  // Wait for full accuracy
    minDuration: 10,         // Full 10 seconds
    minConfidence: 0.7,      // High threshold
    debug: false
  }
});

// Only record high-confidence measurements
camera.onVideoFrameProcessed = (event) => {
  const estimation = event.realtimeEstimation;
  if (!estimation) return;
  
  if (estimation.isStable && estimation.confidence > 0.7) {
    recordMeasurement({
      heartRate: estimation.heartRate,
      confidence: estimation.confidence,
      snr: estimation.snr,
      timestamp: new Date()
    });
  }
});

// Validate before saving
// Validate before saving
let validationCompleted = false;
camera.onVideoFrameProcessed = (event) => {
  const results = event.healthResult;
  if (results && !validationCompleted) {
    validationCompleted = true;
    if (results.confidence > 0.7 && results.snr > 5) {
      saveMedicalRecord(results);
    } else {
      requestRetry('Measurement quality below medical threshold');
    }
  }
});

Next Steps

ME-rPPG Estimator - Compare with AI approach
Selection Guide - Choose the right estimator
Signal Visualizer - Visualize PPG signal
API Reference - Complete API docs

FDA Estimator ​

Overview ​

Key Features ​

Basic Usage ​

Configuration ​

For Consumer Applications ​

For Medical Applications ​

SDK Behavior ​

Initialization ​

Processing Timeline ​

Adaptive Time Window ​

Performance Characteristics ​

Quality Metrics ​

Signal-to-Noise Ratio (SNR) ​

Advantages ​

🏥 Medical-Grade Quality ​

📦 Zero Dependencies ​

⚡ Fast Initialization ​

🔒 IP Protection ​

🎯 Deterministic ​

When to Use ​

✅ Best For ​

❌ Avoid If ​

Troubleshooting ​

Low Confidence Scores ​

Unstable Readings ​

Best Practices ​

1. Progressive Feedback ​

2. Quality Indicators ​

3. Retry Logic ​

4. User Guidance ​

Comparison with ME-rPPG Estimator ​

Example: Complete Implementation ​

Example: Medical Application ​

Next Steps ​

FDA Estimator

Overview

Key Features

Basic Usage

Configuration

For Consumer Applications

For Medical Applications

SDK Behavior

Initialization

Processing Timeline

Adaptive Time Window

Performance Characteristics

Quality Metrics

Signal-to-Noise Ratio (SNR)

Advantages

🏥 Medical-Grade Quality

📦 Zero Dependencies

⚡ Fast Initialization

🔒 IP Protection

🎯 Deterministic

When to Use

✅ Best For

❌ Avoid If

Troubleshooting

Low Confidence Scores

Unstable Readings

Best Practices

1. Progressive Feedback

2. Quality Indicators

3. Retry Logic

4. User Guidance

Comparison with ME-rPPG Estimator

Example: Complete Implementation

Example: Medical Application

Next Steps