iOS Heart rate detection Algorithm

Question

I\'m trying to implement heart beat recording functionality in an app i\'m developing.

The preferred method of doing this is by using the iPhone\'s camera with the light

Answer 1

I presume you're using your own finger. Are you sure you don't have an irregular heartbeat? Plus, you're going to want to handle people with irregular heartbeats. In other words, you should test with a wide variety of input values. Definitely try it on your parents or other older relatives, as they might be more likely to have heart issues. Other than that, your basic problem is that your input source is going to be noisy; you're basically trying to recover signal from that noise. Sometimes that will be impossible, and you're going to have to decide if you want to bake noise into your report or just ignore the data stream when it's too noisy.

Keep trying different filter values; maybe you need an even lower pass filter. From the comments, it sounds like your low pass filter was not good; there's tons of resources on filtering out there in the web. If you've got good visualization tools that will be the best way to test your algorithm.

You can try down-sampling the data, which will smooth it out. You might also want to discard samples which lie outside a valid range, either by discarding the value altogether, replacing it with the average of the previous and next sample, and/or by clamping it to some predetermined or calculated maximum.

My biggest beef with these sorts of applications is that hiccups are treated as real, live data. One of the bikes at my gym gives useless bpm readings because, every so often, it can't find my pulse and suddenly thinks my heart's going at 300 bpm. (Which it isn't; I've asked my doctor.) For a 20-minute session the average it has is useless. I think it's more useful to see the average of the (e.g.) last ten normal beats plus the anomaly rate rather than "I tried to cram the last 20 seconds into this algorithm and here's the garbage it spat out". If you can create a separate filter that indicates anomalies, I think you'll have a much more useful application.