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).
Arduino is on a mission to make machine learning simple enough for anyone to use. We’ve been working with the TensorFlow Lite team over the past few months and are excited to show you what we’ve been up to together: bringing TensorFlow Lite Micro to the Arduino Nano 33 BLE Sense. In this article, we’ll show you how to install and run several new TensorFlow Lite Micro examples that are now available in the Arduino Library Manager.
The first tutorial below shows you how to install a neural network on your Arduino board to recognize simple voice commands.
Next, we’ll introduce a more in-depth tutorial you can use to train your own custom gesture recognition model for Arduino using TensorFlow in Colab. This material is based on a practical workshop held by Sandeep Mistry and Dan Coleman, an updated version of which is now online.
If you have previous experience with Arduino, you may be able to get these tutorials working within a couple of hours. If you’re entirely new to microcontrollers, it may take a bit longer.
We’re excited to share some of the first examples and tutorials, and to see what you will build from here. Let’s get started!
Note: The following projects are based on TensorFlow Lite for Microcontrollers which is currently experimental within the TensorFlow repo. This is still a new and emerging field!
Microcontrollers and TinyML
Microcontrollers, such as those used on Arduino boards, are low-cost, single chip, self-contained computer systems. They’re the invisible computers embedded inside billions of everyday gadgets like wearables, drones, 3D printers, toys, rice cookers, smart plugs, e-scooters, washing machines. The trend to connect these devices is part of what is referred to as the Internet of Things.
Arduino is an open-source platform and community focused on making microcontroller application development accessible to everyone. The board we’re using here has an Arm Cortex-M4 microcontroller running at 64 MHz with 1MB Flash memory and 256 KB of RAM. This is tiny in comparison to Cloud, PC, or mobile but reasonable by microcontroller standards.
There are practical reasons you might want to squeeze ML on microcontrollers, including:
Function – wanting a smart device to act quickly and locally (independent of the Internet).
Cost – accomplishing this with simple, lower cost hardware.
Privacy – not wanting to share all sensor data externally.
Efficiency – smaller device form-factor, energy-harvesting or longer battery life.
There’s a final goal which we’re building towards that is very important:
Machine learning can make microcontrollers accessible to developers who don’t have a background in embedded development
On the machine learning side, there are techniques you can use to fit neural network models into memory constrained devices like microcontrollers. One of the key steps is the quantization of the weights from floating point to 8-bit integers. This also has the effect of making inference quicker to calculate and more applicable to lower clock-rate devices.
TinyML is an emerging field and there is still work to do – but what’s exciting is there’s a vast unexplored application space out there. Billions of microcontrollers combined with all sorts of sensors in all sorts of places which can lead to some seriously creative and valuable TinyML applications in the future.
Environmental – temperature, humidity and pressure
Light – brightness, color and object proximity
Unlike classic Arduino Uno, the board combines a microcontroller with onboard sensors which means you can address many use cases without additional hardware or wiring. The board is also small enough to be used in end applications like wearables. As the name suggests it has Bluetooth LE connectivity so you can send data (or inference results) to a laptop, mobile app or other BLE boards and peripherals.
Tip: Sensors on a USB stick – Connecting the BLE Sense board over USB is an easy way to capture data and add multiple sensors to single board computers without the need for additional wiring or hardware – a nice addition to a Raspberry Pi, for example.
TensorFlow Lite for Microcontrollers examples
The inference examples for TensorFlow Lite for Microcontrollers are now packaged and available through the Arduino Library manager making it possible to include and run them on Arduino in a few clicks. In this section we’ll show you how to run them. The examples are:
micro_speech – speech recognition using the onboard microphone
magic_wand – gesture recognition using the onboard IMU
person_detection – person detection using an external ArduCam camera
For more background on the examples you can take a look at the source in the TensorFlow repository. The models in these examples were previously trained. The tutorials below show you how to deploy and run them on an Arduino. In the next section, we’ll discuss training.
How to run the examples using Arduino Create web editor
Once you connect your Arduino Nano 33 BLE Sense to your desktop machine with a USB cable you will be able to compile and run the following TensorFlow examples on the board by using the Arduino Create web editor:
Focus on the speech recognition example: micro_speech
One of the first steps with an Arduino board is getting the LED to flash. Here, we’ll do it with a twist by using TensorFlow Lite Micro to recognise voice keywords. It has a simple vocabulary of “yes” and “no”. Remember this model is running locally on a microcontroller with only 256KB of RAM, so don’t expect commercial ‘voice assistant’ level accuracy – it has no Internet connection and on the order of 2000x less local RAM available.
Note the board can be battery powered as well. As the Arduino can be connected to motors, actuators and more this offers the potential for voice-controlled projects.
How to run the examples using the Arduino IDE
Alternatively you can use try the same inference examples using Arduino IDE application.
First, follow the instructions in the next section Setting up the Arduino IDE.
In the Arduino IDE, you will see the examples available via the File > Examples > Arduino_TensorFlowLite menu in the ArduinoIDE.
Select an example and the sketch will open. To compile, upload and run the examples on the board, and click the arrow icon:
For advanced users who prefer a command line, there is also the arduino-cli.
Training a TensorFlow Lite Micro model for Arduino
Next we will use ML to enable the Arduino board to recognise gestures. We’ll capture motion data from the Arduino Nano 33 BLE Sense board, import it into TensorFlow to train a model, and deploy the resulting classifier onto the board.
The idea for this tutorial was based on Charlie Gerard’s awesome Play Street Fighter with body movements using Arduino and Tensorflow.js. In Charlie’s example, the board is streaming all sensor data from the Arduino to another machine which performs the gesture classification in Tensorflow.js. We take this further and “TinyML-ifiy” it by performing gesture classification on the Arduino board itself. This is made easier in our case as the Arduino Nano 33 BLE Sense board we’re using has a more powerful Arm Cortex-M4 processor, and an on-board IMU.
We’ve adapted the tutorial below, so no additional hardware is needed – the sampling starts on detecting movement of the board. The original version of the tutorial adds a breadboard and a hardware button to press to trigger sampling. If you want to get into a little hardware, you can follow that version instead.
Setting up the Arduino IDE
Following the steps below sets up the Arduino IDE application used to both upload inference models to your board and download training data from it in the next section. There are a few more steps involved than using Arduino Create web editor because we will need to download and install the specific board and libraries in the Arduino IDE.
First, we need to capture some training data. You can capture sensor data logs from the Arduino board over the same USB cable you use to program the board with your laptop or PC.
Arduino boards run small applications (also called sketches) which are compiled from .ino format Arduino source code, and programmed onto the board using the Arduino IDE or Arduino Create.
We’ll be using a pre-made sketch IMU_Capture.ino which does the following:
Monitor the board’s accelerometer and gyroscope
Trigger a sample window on detecting significant linear acceleration of the board
Sample for one second at 119Hz, outputting CSV format data over USB
Loop back and monitor for the next gesture
The sensors we choose to read from the board, the sample rate, the trigger threshold, and whether we stream data output as CSV, JSON, binary or some other format are all customizable in the sketch running on the Arduino. There is also scope to perform signal preprocessing and filtering on the device before the data is output to the log – this we can cover in another blog. For now, you can just upload the sketch and get sampling.
To program the board with this sketch in the Arduino IDE:
Compile and upload it to the board with Sketch > Upload
Visualizing live sensor data log from the Arduino board
With that done we can now visualize the data coming off the board. We’re not capturing data yet this is just to give you a feel for how the sensor data capture is triggered and how long a sample window is. This will help when it comes to collecting training samples.
In the Arduino IDE, open the Serial Plotter Tools > Serial Plotter
If you get an error that the board is not available, reselect the port:
Tools > Port > portname (Arduino Nano 33 BLE)
Pick up the board and practice your punch and flex gestures
You’ll see it only sample for a one second window, then wait for the next gesture
You should see a live graph of the sensor data capture (see GIF below)
When you’re done be sure to close the Serial Plotter window – this is important as the next step won’t work otherwise.
Capturing gesture training data
To capture data as a CSV log to upload to TensorFlow, you can use Arduino IDE > Tools > Serial Monitor to view the data and export it to your desktop machine:
Reset the board by pressing the small white button on the top
Pick up the board in one hand (picking it up later will trigger sampling)
In the Arduino IDE, open the Serial Monitor Tools > Serial Monitor
If you get an error that the board is not available, reselect the port:
Tools > Port > portname (Arduino Nano 33 BLE)
Make a punch gesture with the board in your hand (Be careful whilst doing this!)
Make the outward punch quickly enough to trigger the capture
Return to a neutral position slowly so as not to trigger the capture again
Repeat the gesture capture step 10 or more times to gather more data
Copy and paste the data from the Serial Console to new text file called punch.csv
Clear the console window output and repeat all the steps above, this time with a flex gesture in a file called flex.csv
Make the inward flex fast enough to trigger capture returning slowly each time
Note the first line of your two csv files should contain the fields aX,aY,aZ,gX,gY,gZ.
Linux tip: If you prefer you can redirect the sensor log output from the Arduino straight to a .csv file on the command line. With the Serial Plotter / Serial Monitor windows closed use:
$ cat /dev/cu.usbmodem[nnnnn] > sensorlog.csv
Training in TensorFlow
We’re going to use Google Colab to train our machine learning model using the data we collected from the Arduino board in the previous section. Colab provides a Jupyter notebook that allows us to run our TensorFlow training in a web browser.
The colab will step you through the following:
Set up Python environment
Upload the punch.csv and flex.csv data
Parse and prepare the data
Build and train the model
Convert the trained model to TensorFlow Lite
Encode the model in an Arduino header file
The final step of the colab is generates the model.h file to download and include in our Arduino IDE gesture classifier project in the next section:
Create a new tab in the IDE. When asked name it model.h
Open the model.h tab and paste in the version you downloaded from Colab
Upload the sketch: Sketch > Upload
Open the Serial Monitor: Tools > Serial Monitor
Perform some gestures
The confidence of each gesture will be printed to the Serial Monitor (0 = low confidence, 1 = high confidence)
Congratulations you’ve just trained your first ML application for Arduino!
For added fun the Emoji_Button.ino example shows how to create a USB keyboard that prints an emoji character in Linux and macOS. Try combining the Emoji_Button.ino example with the IMU_Classifier.ino sketch to create a gesture controlled emoji keyboard ?.
It’s an exciting time with a lot to learn and explore in TinyML. We hope this blog has given you some idea of the potential and a starting point to start applying it in your own projects. Be sure to let us know what you build and share it with the Arduino community.
That’s a question [Charlie Gerard] is going to have to tackle should her AI gesture-recognition controller experiments take off. [Charlie] put together the game controller to learn more about the dark arts of machine learning in a fun and engaging way.
The controller consists of a battery-powered Arduino MKR1000 with WiFi and an MPU6050 accelerometer. Held in the hand, the controller streams accelerometer data to an external PC, capturing the characteristics of the motion. [Charlie] trained three different moves – a punch, an uppercut, and the dreaded Hadouken – and captured hundreds of examples of each. The raw data was massaged, converted to Tensors, and used to train a model for the three moves. Initial tests seem to work well. [Charlie] also made an online version that captures motion from your smartphone. The demo is explained in the video below; sadly, we couldn’t get more than three Hadoukens in before crashing it.