Detecting heart rate using the camera

I need the same functionality as the application Instant Heart Rate.

The basic process requires the user to:

1. Place the tip of the index finger gently on the camera lens.
2. Apply even pressure and cover the entire lens.
3. Hold it steady for 10 seconds and get the heart rate.

This can be accomplished by turning the flash on and watch the light change as the blood moves through the index finger.

How can I get the light level data from the video capture? Where should I look for this? I looked through the class AVCaptureDevice but didn't find anything useful.

I also found AVCaptureDeviceSubjectAreaDidChangeNotification, would that be useful?

Solution 1:

Check out this..

// switch on the flash in torch mode  
 if([camera isTorchModeSupported:AVCaptureTorchModeOn]) {  
 [camera lockForConfiguration:nil];  
 camera.torchMode=AVCaptureTorchModeOn;  
 [camera unlockForConfiguration];  
 }  

  [session setSessionPreset:AVCaptureSessionPresetLow];

   // Create the AVCapture Session  
   session = [[AVCaptureSession alloc] init];  

  // Get the default camera device  
   AVCaptureDevice* camera = [AVCaptureDevice defaultDeviceWithMediaType:AVMediaTypeVideo];  
  if([camera isTorchModeSupported:AVCaptureTorchModeOn]) {  
    [camera lockForConfiguration:nil];  
  camera.torchMode=AVCaptureTorchModeOn;  
    [camera unlockForConfiguration];  
 }  
 // Create a AVCaptureInput with the camera device  
    NSError *error=nil;  
     AVCaptureInput* cameraInput = [[AVCaptureDeviceInput alloc] initWithDevice:camera error:&error];  
   if (cameraInput == nil) {  
    NSLog(@"Error to create camera capture:%@",error);  
  }  

    // Set the output  
    AVCaptureVideoDataOutput* videoOutput = [[AVCaptureVideoDataOutput alloc] init];  

   // create a queue to run the capture on  
  dispatch_queue_t captureQueue=dispatch_queue_create("catpureQueue", NULL);  

   // setup our delegate  
   [videoOutput setSampleBufferDelegate:self queue:captureQueue];  

    // configure the pixel format  
    videoOutput.videoSettings = [NSDictionary dictionaryWithObjectsAndKeys:[NSNumber     numberWithUnsignedInt:kCVPixelFormatType_32BGRA], (id)kCVPixelBufferPixelFormatTypeKey,  
     nil];  
   // cap the framerate  
   videoOutput.minFrameDuration=CMTimeMake(1, 10);  
  // and the size of the frames we want  
  [session setSessionPreset:AVCaptureSessionPresetLow];  

   // Add the input and output  
   [session addInput:cameraInput];  
   [session addOutput:videoOutput];  

   // Start the session  

    [session startRunning];  

   - (void)captureOutput:(AVCaptureOutput *)captureOutput didOutputSampleBuffer:(CMSampleBufferRef)sampleBuffer fromConnection:(AVCaptureConnection *)connection {  



   // this is the image buffer  

  CVImageBufferRef cvimgRef = CMSampleBufferGetImageBuffer(sampleBuffer);  


   // Lock the image buffer  

  CVPixelBufferLockBaseAddress(cvimgRef,0);  


  // access the data  

  int width=CVPixelBufferGetWidth(cvimgRef);  
  int height=CVPixelBufferGetHeight(cvimgRef);  


  // get the raw image bytes  
  uint8_t *buf=(uint8_t *) CVPixelBufferGetBaseAddress(cvimgRef);  
  size_t bprow=CVPixelBufferGetBytesPerRow(cvimgRef);  


// get the average red green and blue values from the image  

 float r=0,g=0,b=0;  
 for(int y=0; y<height; y++) {  
 for(int x=0; x<width*4; x+=4) {  
  b+=buf[x];  
  g+=buf[x+1];  
  r+=buf[x+2];  
 }  
 buf+=bprow;  
 }  
  r/=255*(float) (width*height);  
  g/=255*(float) (width*height);  
  b/=255*(float) (width*height);  

  NSLog(@"%f,%f,%f", r, g, b);  
  }  

Sample Code Here

Solution 2:

In fact can be simple, you have to analyze the pixel values of the captured image. One simple algorithm would be: select and area in the center of the image, convert to gray scale, get the median value of the pixel for each image and you will end up with a 2D function and on this function calculate the distance between to minimums or maximum and problem solved.

If you have a look at the histogram of the acquired images over a period of 5 seconds, you will notice the changes of the gray level distribution. If you want a more robust calculation analyze the histogram.

Solution 3:

As a side note, you may be interested in this research paper. This method does not even require a finger (or anything) directly on the lens.