Gearhead & Scanella
Gearhead Gearhead
Scanella Scanella
Scanella Scanella
Hey Gearhead, I’ve been sketching out a modular smart‑home hub that can learn my routines and suggest tweaks—any ideas on keeping it both user‑friendly and super power‑efficient?
Gearhead Gearhead
Sounds like a cool project! Start with a low‑power microcontroller that can drop idle into sleep when nothing’s happening. Keep the AI on‑device so you don’t need a constant internet feed—just run small models that learn routines locally. Use a touch‑screen or voice command with a simple menu; hide the fancy options behind a settings menu so the main screen stays uncluttered. Add a battery backup that kicks in when mains are down, and design the system in modules so you can swap out a power‑hungry feature for a lighter one if you need to. And remember, a little LED indicator that’s off when the hub’s sleeping will give you instant visual feedback without draining power. Good luck, and keep tweaking until it feels just right!
Scanella Scanella
Thanks for the solid outline—sleep mode and local AI are definitely on my list. I’ll start with a Raspberry Pi Zero W because it’s low‑power and has Wi‑Fi for the occasional sync. Maybe I’ll throw in a small neural net on a Coral Edge TPU to keep the learning snappy. I’ll sketch a touch‑screen interface with a “Quick” view for the day’s routine and a hidden “Advanced” panel for tweaking. Battery backup could be a 5V Li‑Po with a boost converter, so the hub can keep running when the outlet goes dead. I’ll add a tiny status LED that blinks when awake, stays dark in sleep, and maybe flash red if there’s an error. Got any tips on balancing the model size so it fits in memory but still learns well?
Gearhead Gearhead
Great pick the Pi Zero and the Edge TPU—together they’re a solid low‑power combo. For the model size, start by pruning the network: keep only the layers that really affect the output. Use TensorFlow Lite’s quantization to 8‑bit integers; that cuts size by about 4x and speeds up inference on the TPU. Drop any dropout layers for the final model; they’re useful during training but not needed for inference. If you still need more space, try a depthwise separable convolution or replace a dense layer with a depthwise‑separable one. Keep the input resolution low—maybe 64×64 grayscale if you’re doing basic activity recognition—so the feature maps stay small. Finally, run a quick test on the Pi to make sure the memory usage stays below 512 MB; that should leave room for the OS and your code. Good luck—debugging is half the fun!
Scanella Scanella
That makes sense—quantizing to 8‑bit and pruning the layers should keep the footprint small. I’ll try a 64×64 grayscale input for the activity recognizer and drop the dropout before export. Maybe I’ll also test a depthwise‑separable layer in place of the dense one to see if the accuracy stays up. I’ll run a quick inference test on the Pi Zero to check the memory usage. Any idea which dataset I should use to train the model before converting it to TFLite?
Gearhead Gearhead
A few solid choices: the UCI Human Activity Recognition dataset is great if you’re just looking for basic movements, and it’s already pre‑processed for 64×64 grayscale if you resize the sensor plots. If you want something a bit more home‑oriented, check out the WISDM Mobile Phone Sensors dataset—lots of daily‑life activities that map nicely to smart‑home routines. For a more custom feel, you could record your own data with the Pi’s camera or motion sensors and label a few dozen hours of activity; that keeps the model tuned to your exact patterns. Whichever you pick, keep the classes simple at first and then expand once the baseline accuracy is solid. Good luck!