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如何在iOS sdk中不使用任何仪器来检测心率?

[英]How to detect heart pulse rate without using any instrument in iOS sdk?

I am right now working on one application where I need to find out user's heartbeat rate. 我现在正在开发一个应用程序,我需要找出用户的心跳率。 I found plenty of applications working on the same. 我发现有很多应用程序都在运行。 But not able to find a single private or public API supporting the same. 但是无法找到支持相同的私有或公共API。

Is there any framework available, that can be helpful for the same? 有没有可用的框架,对它们有帮助? Also I was wondering whether UIAccelerometer class can be helpful for the same and what can be the level of accuracy with the same? 另外我想知道UIAccelerometer类是否可以对同样有用,什么可以达到相同的准确度?

How to implement the same feature using : putting the finger on iPhone camera or by putting the microphones on jaw or wrist or some other way? 如何使用以下功能实现相同的功能:将手指放在iPhone相机上或将麦克风放在下巴或手腕上或其他方式?

Is there any way to check the blood circulation changes ad find the heart beat using the same or UIAccelerometer? 有没有办法检查血液循环变化广告找到心跳使用相同或UIAccelerometer? Any API or some code?? 任何API或一些代码? Thank you. 谢谢。

There is no API used to detect heart rates, these apps do so in a variety of ways. 没有用于检测心率的API ,这些应用程序以各种方式进行检测。 Some will use the accelerometer to measure when the device shakes with each pulse. 有些人会使用加速度计来测量设备每次脉冲时的抖动。 Other use the camera lens, with the flash on, then detect when blood moves through the finger by detecting the light levels that can be seen. 其他人使用相机镜头,在闪光灯打开的情况下,然后通过检测可以看到的光线水平来检测血液何时通过手指移动。

Various DSP signal processing techniques can be used to possibly discern very low level periodic signals out of a long enough set of samples taken at an appropriate sample rate (accelerometer or reflected light color). 可以使用各种DSP信号处理技术来可能从以足够的采样率(加速度计或反射光颜色)获取的足够长的样本集中辨别出非常低水平的周期性信号。

Some of the advanced math functions in the Accelerate framework API can be used as building blocks for these various DSP techniques. Accelerate框架API中的一些高级数学函数可用作这些各种DSP技术的构建块。 An explanation would require several chapters of a Digital Signal Processing textbook, so that might be a good place to start. 解释将需要数字信号处理教科书的几个章节,因此这可能是一个好的起点。

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