Years ago I used to write regular “Five Short Links” posts but I gave up as my Twitter account became a better place to share updates, notes, and things I found interesting from around the internet. Now that Twitter is Nazi-positive I’m giving up on it as a platform, so I’m going to try going back to occasional summary posts here instead.
Person Sensor back in stock on SparkFun. Sorry for all the delays in getting our new sensors to everyone who wanted them, but we now have a new batch available at SparkFun, and we hope to stay ahead of demand in the future. I’ve also been expanding the Hackster project guides with new examples like face-following robot cars and auto-pausing TV remote controls.
Blecon. It can be a little hard to explain what Blecon does, but my best attempt is that it allows BLE sensors to connect to the cloud using peoples’ phones as relays, instead of requiring a fixed gateway to be installed. The idea is that in places like buildings where staff will be walking past rooms with sensors installed, special apps on their phones can automatically pick up and transmit recorded data. This becomes especially interesting in places like hotels, where management could be alerted to plumbing problems early, without having to invest in extra infrastructure. I like this because it gets us closer to the idea of “peel and stick” sensors, which I think will be crucial to widespread deployment.
Peekaboo. I’ve long been a fan of CMU’s work on IoT security and privacy labels, so it was great to see this exploration of a system that gives users more control over their own data.
32-bit RISC-V MCU for $0.10. It’s not as cheap as the Paduak three-cent MCU, but the fact that it’s 32-bit, with respectable amounts of flash, SRAM, and I/O makes it a very interesting part. I bet it would be capable of running many of the Hackster projects for example, and since it supports I2C it should be able to talk to a Person Sensor. With processors this low cost, we’ll see a lot more hardware being replaced with software.
Hand Pose using TensorFlow JS. I love this online demo from MediaPipe, showing how well it’s now possible to track hands with deep learning approaches. Give the page permission to access your camera and then hold your hands up, you should see rather accurate and detailed hand tracking!