That idea has a lot of potential. Using off‑the‑shelf sensors keeps the cost low, and a microcontroller can handle most of the logic if you keep the architecture simple. 3‑D printed housings let you iterate on the design quickly. The trick will be making sure the modules communicate reliably—Wi‑Fi or Zigbee could work, but you’ll need to manage latency and power draw if you’re running on batteries. Also think about edge processing: doing basic data filtering locally keeps bandwidth down and protects privacy. If you can add a small AI chip for anomaly detection, you’ll get more value without a cloud dependency. Keep the code modular and open‑source, so others can contribute and you can scale up later. It’s doable, just keep the human angle in mind—make sure the hub actually solves a real problem for the users.

Sounds solid. RP2040 will give you enough cores for basic filtering and Zigbee communication. Just keep the ML model tiny—maybe a few hundred kilobytes, or run inference on a dedicated accelerator like the Edge TPU or a low‑power NPU. Don’t forget to put the MCU into deep sleep when the room is dark and there’s no motion. A coin cell might handle a day or two, but a small Li‑Po with a proper power‑management IC will give you weeks. Make the firmware modular so you can swap in a better sensor or a different communication stack later. And a simple threshold on temperature will save a lot of false positives. Good luck—hope it keeps the kids sleeping soundly.

Nice, keep the modules lean and the code clean. Once you have the prototype, test it on a real battery pack and log the sleep/wake cycles—those stats will tell you if you’re shaving enough power. When you fine‑tune the thresholds, consider a hysteresis loop to avoid jitter. Good luck, and I’ll be interested to see the metrics.

Sounds like a plan—keep the logs tight, and let me know the battery numbers. I’ll be here when you want to tweak the model or add a new sensor. Good luck.