If there’s one demographic that has benefited from people being stuck at home during Covid lockdowns, it would be dogs. Having their humans around 24/7 meant more belly rubs, more table scraps, and more attention. Of course, for many dogs, especially those who found their homes during quarantine, this has led to attachment issues as their human counterparts have begin to return to work and school.
[Clairette] has had a particularly difficult time adapting to her friends leaving every day, but thankfully her human [Nathaniel Felleke] was able to come up with a clever solution. He trained a TinyML neural net to detect when she barked and used and Arduino to play a sound byte to sooth her. The sound bytes in question are recordings of [Nathaniel]’s mom either praising or scolding [Clairette], and as you can see from the video below, they seem to work quite well. To train the network, [Nathaniel] worked with several datasets to avoid overfitting, including one he created himself using actual recordings of barks and ambient sounds within his own house. He used Eon Tuner, a tool by Edge Impulse, to help find the best model to use and perform the training. He uploaded the trained network to an Arduino Nano 33 BLE Sense running Mbed OS, and a second Arduino handled playing sound bytes via an Adafruit Music Maker Featherwing.
While machine learning may sound like a bit of an extreme solution to curb your dog’s barking, it’s certainly innovative, and even appears to have been successful. Paired with this web-connected treat dispenser, you could keep a dog entertained for hours.
Continue reading “Machine Learning Shushes Stressed Dogs”
Machine learning (ML) typically conjures up ideas of fancy code requiring oodles of storage and tons of processing power. However, there are some ML models that, once trained, can readily be run on much more spartan hardware – even a microcontroller! The RP2040, star of the Raspberry Pi Pico, is one such chip up to the task, and [Arducam] have announced a board aiming to employ it to those ends – the Pico4ML.
The board goes heavy on the hardware, equipping the RP2040 with plenty of tools useful for machine learning tasks. There’s a QVGA camera on board, as well as a tiny 0.96″ TFT display. The camera feed can even be streamed live to the screen if so desired. There’s also a microphone to capture audio and an IMU, already baked into the board. This puts object, speech, and gesture recognition well within the purview of the Pico4ML.
Running ML models on a board like the Pico4ML isn’t about robust high performance situations. Instead, it’s intended for applications where low power and portability are key. If you’ve got some ideas on what the Pico4ML could do and do well, sound off in the comments. We’d probably hook it up to a network so we could have it automatically place an order when we yell out for pizza. We’ve covered machine learning on microcontrollers before, too – with a great Remoticon talk on how to get started!
Going from a microcontroller blinking an LED, to one that blinks the LED using voice commands based on a data set that you trained on a neural net work is a “now draw the rest of the owl” problem. Lucky for us, Shawn Hymel walks us through the entire process during his Tiny ML workshop from the 2020 Hackaday Remoticon. The video has just now been published and can be viewed below.
This is truly an end-to-end Hello World for getting machine learning up and running on a microcontroller. Shawn covers the process of collecting and preparing the audio samples, training the data set, and getting it all onto the microcontroller. At the end of two hours, he’s able to show the STM32 recognizing and responding to two different spoken words. Along the way he pauses to discuss the context of what’s happening in every step, which will help you go back and expand in those areas later to suit your own project needs.
Continue reading “Remoticon Video: How To Use Machine Learning With Microcontrollers”
Baby monitors are cool, but [Ish Ot Jr.] wanted his to only transmit sounds that required immediate attention and filter any non-emergency background noise. Posed with this problem, he made a baby monitor that would only send alerts when his baby was crying.
For his project, [Ish] used an Arduino Nano 33 BLE Sense due to its built-in microphone, sizeable RAM for storing large chunks of data, and it’s BLE capabilities for later connecting with an app. He began his project by collecting background noise using Edge Impulse Studio’s data acquisition functionality. [Ish] really emphasized that Edge Impulse was really doing all the work for him. He really just needed to collect some test data and that was mostly it on his part. The work needed to run and test the Neural Network was taken care of by Edge Impulse. Sounds handy, if you don’t mind offloading your data to the cloud.
[Ish] ended up with an 86.3% accurate classifier which he thought was good enough for a first pass at things. To make his prototype a bit more “finished”, he added some status LEDs, providing some immediate visual feedback of his classifier and to notify the caregiver. Eventually, he wants to add some BLE support and push notifications, alerting him whenever his baby needs attention.
We’ve seen a couple of baby monitor projects on Hackaday over the years. [Ish’s] project will most certainly be a nice addition to the list.