| I also think this is a better solution compared to the other suggesions. ML for audio and computer vision are both very interesting but absolutely not easy / short projects, from my limited experience it was never as straightforward as you'd expect. Reflections, inconsistent lighting conditions, echo, multipath propagation are all very unpredicatble factors that require a ton of math and signal (pre)processing to solve. Compared to this, I think one can boge together an ugly prototype to send data to a server over wifi / BLE in a few days even with close to zero electronics experience. > though it will be a step learning curve if one has never done any embedded For someone who has not previously worked with hardware I'd suggest to buy first a cheap Arduino-compatible board like this one [1] with BLE and WiFi instead of professional hardware. Although not as efficient, calls to hardware sensors are much simpler and well documented for example here [2, 3]. For the filtering part jononor is talking about I'd suggest this chapter 10 of this book [4] complemeted with more accessible resources like this video [5] (the book is to dig deeper into the maths, but not strictly necessary). Then once you have this you can be as fancy as you want on the server side with ML models etc, but I suspect that since the seizures are rather long (minutes) just looking at the mean frequency about 15 ~ 30 seconds could already be a pretty good indicator. [1]: https://www.lilygo.cc/products/t-energy-s3 (This is the first board I found, but I'm sure are also other smaller boards with ESP32 or ESP8266 using a CR2032 battery like https://hackaday.com/2019/02/22/a-coin-cell-powers-this-tiny... on AliExpress / BangGood / etc.) [2]: https://www.arduino.cc/reference/en/libraries/arduinoble/ [3]: https://docs.arduino.cc/built-in-examples/sensors/ADXL3xx/ [4]: http://eceweb1.rutgers.edu/~orfanidi/intro2sp/orfanidis-i2sp... http://eceweb1.rutgers.edu/~orfanidi/intro2sp/ [5]: https://www.youtube.com/watch?v=uNNNj9AZisM |