我正试图在我正在开发的应用程序中实现心跳记录功能.
实现这一点的首选方法是使用iPhone的摄像头，打开灯，让用户将手指放在镜头上，并检测视频馈送中的波动，这与用户的心脏相对应.
我从下面的堆栈溢出问题中找到了一个很好的起点
这个问题提供了绘制心跳时间图的有用代码.
它显示了如何启动AVCaptureSession并打开摄像头的指示灯，如下所示:
session = [[AVCaptureSession alloc] init];
AVCaptureDevice* camera = [AVCaptureDevice defaultDeviceWithMediaType:AVMediaTypeVideo];
if([camera isTorchModeSupported:AVCaptureTorchModeOn]) {
[camera lockForConfiguration:nil];
camera.torchMode=AVCaptureTorchModeOn;
// camera.exposureMode=AVCaptureExposureModeLocked;
[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];
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];
本例中的Self必须是<AVCaptureVideoDataOutputSampleBufferDelegate>
- (void)captureOutput:(AVCaptureOutput *)captureOutput didOutputSampleBuffer:(CMSampleBufferRef)sampleBuffer fromConnection:(AVCaptureConnection *)connection {
static int count=0;
count++;
// only run if we're not already processing an image
// 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);
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];
// a+=buf[x+3];
}
buf+=bprow;
}
r/=255*(float) (width*height);
g/=255*(float) (width*height);
b/=255*(float) (width*height);
float h,s,v;
RGBtoHSV(r, g, b, &h, &s, &v);
// simple highpass and lowpass filter
static float lastH=0;
float highPassValue=h-lastH;
lastH=h;
float lastHighPassValue=0;
float lowPassValue=(lastHighPassValue+highPassValue)/2;
lastHighPassValue=highPassValue;
//low pass value can now be used for basic heart beat detection
}
RGB被转换成HSV，并对色调的波动进行监控.
RGB到HSV的实现如下
void RGBtoHSV( float r, float g, float b, float *h, float *s, float *v ) {
float min, max, delta;
min = MIN( r, MIN(g, b ));
max = MAX( r, MAX(g, b ));
*v = max;
delta = max - min;
if( max != 0 )
*s = delta / max;
else {
// r = g = b = 0
*s = 0;
*h = -1;
return;
}
if( r == max )
*h = ( g - b ) / delta;
else if( g == max )
*h=2+(b-r)/delta;
else
*h=4+(r-g)/delta;
*h *= 60;
if( *h < 0 )
*h += 360;
}
在capureOutput:中计算的低通值最初提供不稳定的数据，但随后稳定为以下值:
2013-11-04 16:18:13.619 SampleHeartRateApp[1743:1803] -0.071218
2013-11-04 16:18:13.719 SampleHeartRateApp[1743:1803] -0.050072
2013-11-04 16:18:13.819 SampleHeartRateApp[1743:1803] -0.011375
2013-11-04 16:18:13.918 SampleHeartRateApp[1743:1803] 0.018456
2013-11-04 16:18:14.019 SampleHeartRateApp[1743:1803] 0.059024
2013-11-04 16:18:14.118 SampleHeartRateApp[1743:1803] 0.052198
2013-11-04 16:18:14.219 SampleHeartRateApp[1743:1803] 0.078189
2013-11-04 16:18:14.318 SampleHeartRateApp[1743:1803] 0.046035
2013-11-04 16:18:14.419 SampleHeartRateApp[1743:1803] -0.113153
2013-11-04 16:18:14.519 SampleHeartRateApp[1743:1803] -0.079792
2013-11-04 16:18:14.618 SampleHeartRateApp[1743:1803] -0.027654
2013-11-04 16:18:14.719 SampleHeartRateApp[1743:1803] -0.017288
最初提供的不稳定数据示例如下:
2013-11-04 16:17:28.747 SampleHeartRateApp[1743:3707] 17.271435
2013-11-04 16:17:28.822 SampleHeartRateApp[1743:1803] -0.049067
2013-11-04 16:17:28.922 SampleHeartRateApp[1743:1803] -6.524201
2013-11-04 16:17:29.022 SampleHeartRateApp[1743:1803] -0.766260
2013-11-04 16:17:29.137 SampleHeartRateApp[1743:3707] 9.956407
2013-11-04 16:17:29.221 SampleHeartRateApp[1743:1803] 0.076244
2013-11-04 16:17:29.321 SampleHeartRateApp[1743:1803] -1.049292
2013-11-04 16:17:29.422 SampleHeartRateApp[1743:1803] 0.088634
2013-11-04 16:17:29.522 SampleHeartRateApp[1743:1803] -1.035559
2013-11-04 16:17:29.621 SampleHeartRateApp[1743:1803] 0.019196
2013-11-04 16:17:29.719 SampleHeartRateApp[1743:1803] -1.027754
2013-11-04 16:17:29.821 SampleHeartRateApp[1743:1803] 0.045803
2013-11-04 16:17:29.922 SampleHeartRateApp[1743:1803] -0.857693
2013-11-04 16:17:30.021 SampleHeartRateApp[1743:1803] 0.061945
2013-11-04 16:17:30.143 SampleHeartRateApp[1743:1803] -0.701269
只要有心跳，低通值就会变为正值.所以我try 了一个非常简单的实时检测算法，它基本上看当前值，看它是否为正值，它也看以前的值，如果为负值，它会检测到负值变为正值，并发出哔哔声.
问题是数据并不总是像上面那样完美，有时在负读数中会出现异常正读数，反之亦然.
低通值的时间曲线图如下所示:
有趣的是，上面的异常非常常见，如果我记录一段时间，我会多次看到形状非常相似的异常.
在我非常简单的节拍检测算法中，如果出现如上所示的异常，检测周期(10秒)内计数的节拍数可能会增加4或5次.这使得计算的BPM非常不准确.但是，尽管它很简单，但在大约70%的时间里，它确实有效.
为了解决这个问题，我try 了以下方法.
1.开始记录数组中的最后3个低通值
2.然后观察中间值前后是否有两个较小的值.(基本峰值检测)
3.将此场景计算为一个节拍，并将其添加到给定时间内的 run 总节拍中.
然而，这种方法和其他方法一样容易受到异常情况的影响.实际上，这似乎是一种更糟糕的方法.(在检测后播放实时蜂鸣音时，它们似乎比正转负算法更不稳定)
My question is can you help me come up with an algorithm that can reliably detect when a heart beat occurs with reasonable accuracy.
我意识到我必须解决的另一个问题是检测用户的手指是否在镜头上.
我曾想过检测不稳定的低通值，但问题是低通滤波器会考虑不稳定的值，并随着时间的推移进行平滑处理.因此，在那里的帮助也将被感激.
谢谢你抽出时间.