PPG analysis beyond HR at rest

The following is a compressed summery of what I found out so far (No code from me so far. Credit goes to gordon, helmrich, myownself and others)
Please correct me where I am wrong or point me to good information I did't find so far.

Where is the problem?

• As all smartwatches, bangle usese optical sensing ("PPG" = "Photoplethysmography") not electrical as chestbelts do in order to measure heart related information.
  
• This type of sensor signal is much more vulnerable to disturbance, especially by movement, and therefore considerably more difficult to interpret.
  
• On the other hand it contains additional information that eg. could enables estimation of oxygen saturation (SpO2) and more.
  
• There is a proprietory interpretation software blob from the chip manufacturer, but we try to stay open source if possible.
  (http://forum.espruino.com/conversations/371899/#16355245)
  
• Here is some literature on how such algorithms work:
  http://forum.espruino.com/conversations/372681/#16387616
  http://forum.espruino.com/conversations/373033/?offset=25#16668809
  
  

What is the current state?

• Right now heart rate estimation is implemented in C within espruino.
  http://forum.espruino.com/conversations/­372681/#16388080
  
• For most people HR estimation works pretty ok in rest, but not good while moving. Some people also have wrong reading at rest.
  http://forum.espruino.com/conversations/373033/#comment16423860
  
• In addition to the HR espruino provides a confidence measure. The confidence is a pretty good indicator of the validity of the HR. You shouldn't trust HR below confidence of ~90. Some apps (eg daisy or circle clock) can use that and hide unreliable readings.
  http://forum.espruino.com/conversations/372681/#16387732
  
  
  

How can I have a quick look or start experimenting?

• For a quick start, you may want to just paste this code into left side of WebIDE
  http://forum.espruino.com/conversations/­372681/?offset=25#16390275
  Yellow is the raw input signal, red the improved filtered one and after a while you can see all recognised peaks marked in purple over the red graph.
  You can see how good the current algorithm works for you (and see that there is even low hanging fruit for improvement still)
  
• The "HRM Accelerometer event recorder" App allows a more detailed analyses by recording this data and more on a computer/phone into a csv file. https://banglejs.com/apps/?id=hrmacceven­ts
  With this csv you can start analysing and testing in whatever tool/language you prefer.
  As the name suggests also acceleration data is recorded, which so far is not used at all in espruino HRM. Including it should really make a difference.
  https://github.com/espruino/BangleApps/issues/1248
  This app is also a good example to learn how to get all the relevant sensor readings.
  
• Gorden has setup a test harness and repository to collect test data: https://github.com/gfwilliams/EspruinoHRMTestHarness http://forum.espruino.com/conversations/373033/#comment16554946
  
  

What is the plan?

• Set up a test harness where a potential algorithm can be optimized and reproducably evaluated on a train/validation/test set of recordings.
  
• Collect a good (increasing level of difficulty; from different persons) set of recordings of bangle PPL and Acc sensor data toghether with refernce data from a good external sensor via BLE
  
• Try differnt algorithms starting with basic ones as eg.
  
  • just ignore all PPL readings above a certain threshold of movement measured by Accelerometer
    
  • Improve peak finding (as with the eye one sees very clear peaks that are not found)
    
    

Result of attempts so far and analysis:

• http://forum.espruino.com/conversations/373033/?offset=25#16653576
  
  

How can I get reliable heart rate right now?
(until me/you/somebody finds the time to improve the algorithm)

• Use a BT chest belt. Bangle can recieve, show, store heartrate via bluetooth.