I'm working in a team that is collecting data by bicycle : We have biometric t-shirts that measure our ventilation rate. The problem is that during the last data collection, participants used masks to protect themself against air-pollution.This has significantly affected the measure. So, I have a variable Y (ventilation rate), collected for three participants with a time resolution of one minute in previous data collections. For the same participants, I have new data but "corrupted" by the effect of the mask.
Is there a way to clean the data "corrupted" by knowing previous data about participants ? I have some variables linked to the ventilation rate like the speed of the participant, the mean slope of the terrain during the minute measured, the mean acceleration etc.
I found the third part of this blog interesting : https://abidlabs.github.io/removing-noise-from-signal/ They talk about "contrastive Dataset" and "confounding signal"
If somone has an idea, you are welcome !
All the best