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The original was posted on /r/machinelearning by /u/GoochCommander on 2023-09-25 13:16:10.
I made a FOSS baby sleep tracking system. The system tracks wake/sleep status, and informs the user when their baby is likely to need a nap next. But it stopped working as soon as my baby started sleeping on his stomach, and started using blankets. The original version relied on anatomical features being visible.
This version delivers the ability to train a blank slate SVM binary classifier on pictures of a user's baby, making it extremely biased (and resilient) to the custom behaviors the user's baby exhibits (blanket covering baby, teddy bear/other objects in crib, etc.). All generated data stays on your machine, nothing leaves the LAN.
Video:
For obvious reasons I didn't have a dataset of babies sleeping.. so I wrapped a python/flask service with a React app which lets a user press a button to train the model w/ a new image from the camera's live stream. Then this model is invoked over time (+ other heuristics) to determine whether your baby is present and sleeping.
I believe it works better than $300+ systems sold on the market, open sourced it:
I'm thinking a fun next step for this project could be to apply privateGPT on the feature-engineered sleep records for a true "Sleep Coach"
That's pretty cool