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PranayKo

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PranayKo
·10 か月前·議論
1. the train test in here was leaky, but in our current iteration of the work we do 10 fold train / test without leakage 2. There was no different in performance at higher HR values, the rpi data contained people running in place and our performance on that was as good as laying down 3. a simple presence detection model would solve that and also the algorithm already covers this
PranayKo
·10 か月前·議論
1. in this iteration we used a 64% training 16% validation 20% testing split. In our current work we are testing with 10 fold / leave a subject out to get better analysis. 2. the esp dataset had heart rate up to 130 which is relatively high, and the raspberry pi data had people running in place etc... where the heart rate is higher
PranayKo
·10 か月前·議論
1. We are currently working on multi person, this iteration doesn't support it. 2. The range was just what we had in this dataset, if we exposed the algorithm to a broader range, it would work for that too.
PranayKo
·10 か月前·議論
Oh great- I forgot we had the open pdf posted somewhere
PranayKo
·10 か月前·議論
Thanks!

https://ieeexplore.ieee.org/abstract/document/11096342

That’s the official paper link. Sorry it’s not open access.
PranayKo
·10 か月前·議論
Hey guys I am the high schooler who developed this let me know if you got any questions I'd be happy to answer them