There are plenty of problems that are easy for humans to solve, but are almost impossibly difficult for computers. Even though it seems that with modern computing power being what it is we should be able to solve a lot of these problems, things like identifying objects in images remains fairly difficult. Similarly, identifying specific sounds within audio samples remains problematic, and as [Eivind] found, is holding up a lot of medical research to boot. To solve one specific problem he created a system for counting coughs of medical patients.
This was built with the idea of helping people with chronic obstructive pulmonary disease (COPD). Most of the existing methods for studying the disease and treating patients with it involves manually counting the number of coughs on an audio recording. While there are some software solutions to this problem to save some time, this device seeks to identify coughs in real time as they happen. It does this by training a model using tinyML to identify coughs and reject cough-like sounds. Everything runs on an Arduino Nano with BLE for communication.
While the only data the model has been trained on are sounds from [Eivind], the existing prototypes do seem to show promise. With more sound data this could be a powerful tool for patients with this disease. And, even though this uses machine learning on a small platform, we have seen before that Arudinos are plenty capable of being effective machine learning solutions with the right tools on board.
Measuring air quality at any particular location isn’t too complicated. Just a sensor or two and a small microcontroller is generally all that’s needed. Predicting the upcoming air quality is a little more complicated, though, since so many factors determine how safe it will be to breathe the air outside. Luckily, though, we don’t need to know all of these factors and their complex interactions in order to predict air quality. We can train a computer to do that for us as [kutluhan_aktar] demonstrates with a machine learning-capable air quality meter.
The build is based around an Arduino Nano 33 BLE which is connected to a small weather station outside. It specifically monitors ozone concentration as a benchmark for overall air quality but also uses an anemometer and a BMP180 precision pressure and temperature sensor to assist in training the algorithm. The weather data is sent over Bluetooth to a Raspberry Pi which is running TensorFlow. Once the neural network was trained, the model was sent back to the Arduino which is now capable of using it to make much more accurate predictions of future air quality.
The build goes into quite a bit of detail on setting up the models, training them, and then using them on the Arduino. It’s an impressive build capped off with a fun 3D-printed case that resembles an old windmill. Using machine learning to help predict the weather is starting to become more commonplace as well, as we have seen before with this weather station that can predict rainfall intensity.
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.
Step sequencers are fantastic instruments, but they can be a little, well, repetitive. At it’s core, the step sequencer is a pretty simple device: it loops through a series of notes or phrases that are, well, sequentially ordered into steps. The operator can change the steps while the sequencer is looping, but it generally has a repetitive feel, as the musician isn’t likely to erase all of the steps and enter in an entirely new set between phrases.
Enter our old friend machine learning. If we introduce a certain variability on each step of the loop, the instrument can help the musician out a bit here, making the final product a bit more interesting. Such an instrument is exactly what [Charis Cat] set out to make when she created the After Eight Step Sequencer.
The After Eight is an eight-step sequencer that allows the artist to set each note with a series of potentiometers (which are, of course, housed in an After Eight mint tin). The potentiometers are read by an Arduino, which passes MIDI information to a computer running the popular music-oriented visual programming language Max MSP. The software uses a series of Markov Chains to augment the musician’s inputted series of notes, effectively working with the artist to create music. The result is a fantastic piece of music that’s different every time it’s performed. Make sure to check out the video at the end for a fantastic overview of the project (and to hear the After Eight in action, of course)!
[Charis Cat]’s wonderful creation reminds us of some the work [Sara Adkins] has done, blending human performance with complex algorithms. It’s exactly the kind of thing we love to see at Hackaday- the fusion of a musician’s artistic intent with the stochastic unpredictability of a machine learning system to produce something unique.
Mind control might seem like something out of a sci-fi show, but like the tablet computer, universal translator, or virtual reality device, is actually a technology that has made it into the real world. While these devices often requires on advanced and expensive equipment to interpret brain waves properly, with the right machine learning system it’s possible to do things like this mind-controlled flame thrower on a much smaller budget. (Video, embedded below.)
[Nathaniel F] was already experimenting with using brain-computer interfaces and machine learning, and wanted to see if he could build something practical combining these two technologies. Instead of turning to an EEG machine to read brain patterns, he picked up a much less expensive Mindflex and paired it with a machine learning system running TensorFlow to make up for some of its shortcomings. The processing is done by a Raspberry Pi 4, which sends commands to an Arduino to fire the flamethrower when it detects the proper thought patterns. Don’t forget the flamethrower part of this build either: it was designed and built entirely by [Nathanial F] as well using gas and an arc lighter.
While the build took many hours of training to gather the proper amount of data to build the neural network and works as the proof of concept he was hoping for, [Nathaniel F] notes that it could be improved by replacing the outdated Mindflex with a better EEG. For now though, we appreciate seeing sci-fi in the real world in projects like this, or in other mind-controlled projects like this one which converts a prosthetic arm into a mind-controlled music synthesizer.
Gesture recognition and machine learning are getting a lot of air time these days, as people understand them more and begin to develop methods to implement them on many different platforms. Of course this allows easier access to people who can make use of the new tools beyond strictly academic or business environments. For example, rollerblading down the streets of Atlanta with a gesture-recognizing, streaming TV that [nate.damen] wears over his head.
He’s known as [atltvhead] and the TV he wears has a functional LED screen on the front. The whole setup reminds us a little of Deep Thought. The screen can display various animations which are controlled through Twitch chat as he streams his journeys around town. He wanted to add a little more interaction to the animations though and simplify his user interface, so he set up a gesture-sensing sleeve which can augment the animations based on how he’s moving his arm. He uses an Arduino in the arm sensor as well as a Raspberry Pi in the backpack to tie it all together, and he goes deep in the weeds explaining how to use Tensorflow to recognize the gestures. The video linked below shows a lot of his training runs for the machine learning system he used as well.
We tend to think that the lowest point of entry for machine learning (ML) is on a Raspberry Pi, which it definitely is not. [EloquentArduino] has been pushing the limits to the low end of the scale, and managed to get a basic classification model running on the ATtiny85.
Using his experience of running ML models on an old Arduino Nano, he had created a generator that can export C code from a scikit-learn. He tried using this generator to compile a support-vector colour classifier for the ATtiny85, but ran into a problem with the Arduino ATtiny85 compiler not supporting a variadic function used by the generator. Fortunately he had already experimented with an alternative approach that uses a non-variadic function, so he was able to dust that off and get it working. The classifier accepts inputs from an RGB sensor to identify a set of objects by colour. The model ended up easily fitting into the capabilities of the diminutive ATtiny85, using only 41% of the available flash and 4% of the available ram.
It’s important to note what [EloquentArduino] isn’t doing here: running an artificial neural network. They’re just too inefficient in terms of memory and computation time to fit on an ATtiny. But neural nets aren’t the only game in town, and if your task is classifying something based on a few inputs, like reading a gesture from accelerometer data, or naming a color from a color sensor, the approach here will serve you well. We wonder if this wouldn’t be a good solution to the pesky problem of identifying bats by their calls.
We really like how approachable machine learning has become and if you’re keen to give ML a go, have a look at the rest of the EloquentArduino blog, it’s a small goldmine.
Arduino is on a mission to make machine learning easy enough for anyone to use. The other week we announced the availability of TensorFlow Lite Micro in the Arduino Library Manager. With this, some cool ready-made ML examples such as speech recognition, simple machine vision and even an end-to-end gesture recognition training tutorial. For a comprehensive background we recommend you take a look at that article.
In this article we are going to walk through an even simpler end-to-end tutorial using the TensorFlow Lite Micro library and the Arduino Nano 33 BLE Sense’s colorimeter and proximity sensor to classify objects. To do this, we will be running a small neural network on the board itself.
The philosophy of TinyML is doing more on the device with less resources – in smaller form-factors, less energy and lower cost silicon. Running inferencing on the same board as the sensors has benefits in terms of privacy and battery life and means its can be done independent of a network connection.
The fact that we have the proximity sensor on the board means we get an instant depth reading of an object in front of the board – instead of using a camera and having to determine if an object is of interest through machine vision.
In this tutorial when the object is close enough we sample the color – the onboard RGB sensor can be viewed as a 1 pixel color camera. While this method has limitations it provides us a quick way of classifying objects only using a small amount of resources. Note that you could indeed run a complete CNN-based vision model on-device. As this particular Arduino board includes an onboard colorimeter, we thought it’d be fun and instructive to demonstrate in this way to start with.
We’ll show a simple but complete end-to-end TinyML application can be achieved quickly and without a deep background in ML or embedded. What we cover here is data capture, training, and classifier deployment. This is intended to be a demo, but there is scope to improve and build on this should you decide to connect an external camera down the road. We want you to get an idea of what is possible and a starting point with tools available.
The Arduino Nano 33 BLE Sense board we’re using here has an Arm Cortex-M4 microcontroller running mbedOS and a ton of onboard sensors – digital microphone, accelerometer, gyroscope, temperature, humidity, pressure, light, color and proximity.
While tiny by cloud or mobile standards the microcontroller is powerful enough to run TensorFlow Lite Micro models and classify sensor data from the onboard sensors.
Setting up the Arduino Create Web Editor
In this tutorial we’ll be using the Arduino Create Web Editor – a cloud-based tool for programming Arduino boards. To use it you have to sign up for a free account, and install a plugin to allow the browser to communicate with your Arduino board over USB cable.
You can get set up quickly by following the getting started instructions which will guide you through the following:
Download and install the plugin
Sign in or sign up for a free account
(NOTE: If you prefer, you can also use the Arduino IDE desktop application. The setup for which is described in the previous tutorial.)
Capturing training data
We now we will capture data to use to train our model in TensorFlow. First, choose a few different colored objects. We’ll use fruit, but you can use whatever you prefer.
Setting up the Arduino for data capture
Next we’ll use Arduino Create to program the Arduino board with an application object_color_capture.ino that samples color data from objects you place near it. The board sends the color data as a CSV log to your desktop machine over the USB cable.
To load the object_color_capture.ino application onto your Arduino board:
Connect your board to your laptop or PC with a USB cable
The Arduino board takes a male micro USB
Open object_color_capture.ino in Arduino Create by clicking this link
Your browser will open the Arduino Create web application (see GIF above).
Press OPEN IN WEB EDITOR
For existing users this button will be labeled ADD TO MY SKETCHBOOK
Press Upload & Save
This will take a minute
You will see the yellow light on the board flash as it is programmed
Open the serial Monitor
This opens the Monitor panel on the left-hand side of the web application
You will now see color data in CSV format here when objects are near the top of the board
Capturing data in CSV files for each object
For each object we want to classify we will capture some color data. By doing a quick capture with only one example per class we will not train a generalized model, but we can still get a quick proof of concept working with the objects you have to hand!
Say, for example, we are sampling an apple:
Reset the board using the small white button on top.
Keep your finger away from the sensor, unless you want to sample it!
The Monitor in Arduino Create will say ‘Serial Port Unavailable’ for a minute
You should then see Red,Green,Blue appear at the top of the serial monitor
Put the front of the board to the apple.
The board will only sample when it detects an object is close to the sensor and is sufficiently illuminated (turn the lights on or be near a window)
Move the board around the surface of the object to capture color variations
You will see the RGB color values appear in the serial monitor as comma separated data.
Capture at a few seconds of samples from the object
Copy and paste this log data from the Monitor to a text editor
Tip: untick AUTOSCROLL check box at the bottom to stop the text moving
Save your file as apple.csv
Reset the board using the small white button on top.
Do this a few more times, capturing other objects (e.g. banana.csv, orange.csv).
NOTE: The first line of each of the .csv files should read:
If you don’t see it at the top, you can just copy and paste in the line above.
Training the model
We will now use colab to train an ML model using the data you just captured in the previous section.
First open the FruitToEmoji Jupyter Notebook in colab
Follow the instructions in the colab
You will be uploading your *.csv files
Parsing and preparing the data
Training a model using Keras
Outputting TensorFlowLite Micro model
Downloading this to run the classifier on the Arduino
With that done you will have downloaded model.h to run on your Arduino board to classify objects!
Program TensorFlow Lite Micro model to the Arduino board
Finally, we will take the model we trained in the previous stage and compile and upload to our Arduino board using Arduino Create.
Your browser will open the Arduino Create web application:
Press the OPEN IN WEB EDITOR button
Import the model.h you downloaded from colab using Import File to Sketch:
Compile and upload the application to your Arduino board
This will take a minute
When it’s done you’ll see this message in the Monitor:
Put your Arduino’s RGB sensor near the objects you trained it with
You will see the classification output in the Monitor:
You can also edit the object_color_classifier.ino sketch to output emojis instead (we’ve left the unicode in the comments in code!), which you will be able to view in Mac OS X or Linux terminal by closing the web browser tab with Arduino Create in, resetting your board, and typing cat /cu/usb.modem[n].
The resources around TinyML are still emerging but there’s a great opportunity to get a head start and meet experts coming up 2-3 December 2019 in Mountain View, California at the Arm IoT Dev Summit. This includes workshops from Sandeep Mistry, Arduino technical lead for on-device ML and from Google’s Pete Warden and Daniel Situnayake who literally wrote the book on TinyML. You’ll be able to hang out with these experts and more at the TinyML community sessions there too. We hope to see you there!
We’ve seen a quick end-to-end demo of machine learning running on Arduino. The same framework can be used to sample different sensors and train more complex models. For our object by color classification we could do more, by sampling more examples in more conditions to help the model generalize. In future work, we may also explore how to run an on-device CNN. In the meantime, we hope this will be a fun and exciting project for you. Have fun!
Nothing spoils your mood quite like your windscreen wipers not feeling it when the beat drops. Every major car manufacturer is focused on trying to build the electric self driving vehicle for the masses, yet ignoring this very real problem. Well [Ian Charnas] is taking charge, and has successfully slaved his car’s wipers to beat of its stereo.
Starting with the basics, [Ian] first needed to control the speed of the wiper motor. This was done using a custom power supply adapted from another project. The brain of the system is a Raspberry Pi 3B+ which runs a phase locked loop algorithm to sync the music and the motor. Detecting the beat turned out to be the most difficult part of the project, and from the research [Ian] did, there is no standard solution. He ended up settling on “madmom“, a Python audio and music signal processing library, which runs a neural net to detect the beat in real time. The Raspi sends the required PWM and Enable signals to an Arduino over serial, which in turn controls the power supply. The entire system was neatly integrated in the car, with a switch in the dash that connects the motor to the new power supply on demand, to allow the wipers to still be used normally (and safely).