Detecting heart rate using the camera

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// 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

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.

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.