Solar energy is one of the most important technologies of our time, and understanding how it works can be… enlightening! Julián Caro Linares, Senior Field Technology Evangelist at Qualcomm Europe, built a project that helps learn about this topic with a hands-on approach: a fully functional, fully open-source smart solar panel system that adjusts to optimize solar exposure in real time, built with UNO Q and a handful of Arduino^® Modulino nodes. The best part? He explains every step so you can 3D print, assemble, and program it yourself, too! 

A shining moment for UNO Q!

The system does a surprising amount for something you can build at home or in a classroom. Two servo motors move the solar panel continuously, orienting it toward the best position to capture energy from the sun based on a sky scan the board runs automatically. A live dashboard – served directly from the Linux side of UNO Q to any browser on your local network – shows panel orientation, a solar heatmap, real-time energy readings, infrared light levels, and local weather data. Just watching it update is pretty cool!

And the system doesn’t just chase the sun, it also protects itself. If temperatures climb too high, the panel adjusts to avoid damage. If a snowstorm or sandstorm is forecast, it moves to a protective vertical position. If it gets too dark to generate meaningful energy, it returns to a resting position and waits. And when dust or debris accumulates, it vibrates – just like NASA’s Mars rovers do – to shake itself clean.

All of this runs on UNO Q thanks to its dual-brain architecture: the Linux side handles the dashboard, weather data, and higher-level logic, while the real-time microcontroller reads sensors and drives the motors. The Modulino nodes do the sensing: Modulino Light reads both visible and infrared light, Modulino Thermo monitors temperature and humidity, Modulino Movement detects anomalous vibrations, and Modulino Vibro handles the self-cleaning function. 

Everything was easily orchestrated through Arduino® App Lab, using three ready-to-use Bricks: 

• weather_forecast: for an online API that always has the latest weather information;
• web_ui: to build and host the webpage;
• dbstorage_tsstore: to store and get data using a time series database.

Try it as a bright classroom project

Julián designed this explicitly as an educational tool, and it shows. The project is fully open-source – 3D files, code, and a complete step-by-step tutorial are all available on Arduino^® Project Hub. On the hardware side, a small solar panel and two servo motors are all you need to add. For the software, Arduino App Lab handles the whole setup.

What makes it particularly compelling as a teaching tool is how many real concepts it touches at once. Students can explore renewable energy, sensor fusion, weather data integration, edge AI decision-making, real-time data visualization, and basic robotics – all within a single, coherent project that actually does something. Just observe when the panel tilts toward the sun, or switches to emergency mode before a storm: the logic behind is visible and understandable. 

“It’s a very interesting project to do at home and to apply in the classroom,” he says, “to show how we can use UNO Q to solve real-life problems in actual solar panel installations.”

Check out the full tutorial on Arduino Project Hub.

In addition to the Arduino Store, UNO Q is available to order from DigiKey, Farnell, Mouser, Newark, RS Components, and Robu.in, along with our other authorized distributors and resellers.

Arduino, UNO, Modulino and the Arduino logo are trademarks or registered trademarks of Arduino S.r.l.

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Just a few weeks after launching the UNO Q DIY synth bundle, we’re adding a new classic project you can pick up in one click, and get done in a weekend. 

Meet the UNO Q Arcade bundle: a rewarding new way to start building with UNO Q, following a complete step-by-step tutorial towards a clear objective – and gaining hands-on tech experience as you go!

Not only is the bundle designed to make your experience frictionless and rewarding, but it comes out during a special sale with a 20% discount built right in to sweeten the deal. Don’t sleep on it! If the DIY synth bundle taught us anything, it’s these sell out fast.

The UNO Q Arcade bundle: one board, infinite games

Now anyone can build their own arcade cabinet, in just a few hours of fun. Follow the instructions available on Arduino^® Project Hub, and you’ll quickly have UNO Q run RetroArch – the well-established open-source multi-system emulator – directly on its Debian Linux side, outputting via HDMI and handling audio over USB. No separate single-board computer, no extra compute module, no compromises: the Qualcomm Dragonwing QRB2210 processor manages multiple emulation cores, controller input, video, and audio simultaneously, while the onboard eMMC provides plenty of room for ROMs and configuration files.

But one board does not a bundle make! We’ve included three easy-to-use Arduino^® Modulino nodes – Buttons, Joystick, and Movement – to make the controller side of UNO 4GB really shine. Modulino eliminate complex wiring: just daisy-chain them together via Qwiic and you’re ready to configure. Use Joystick to simulate D-Pad or mouse movement, Buttons for single or multi-key shortcuts, and Movement for motion-based mouse control. Every input is customizable to your playstyle. 

Create your own games with open-source Godot

So you can build the cool hardware, and leverage ready-to-use software to play your favorite retro-style games. But how about creating your own?

Thanks to its Debian Linux environment, UNO Q can run projects built with the popular open-source game engine Godot. This makes the UNO Q the heart of a compact game-development platform and, ultimately, a super-portable DIY video game console.

Develop your game with Godot, connect UNO Q to a display via HDMI, add a USB audio device, and build your own controller using Modulino nodes or other Arduino-compatible hardware. Buttons, joysticks, motion sensors, knobs, LEDs, and even environmental sensors can become part of the gameplay – not just accessories connected to it.

Put everything inside a 3D-printed or custom enclosure, add a small HDMI display and a portable power source, and your project can move from desktop prototype to a self-contained gaming device you can carry, demonstrate, and play almost anywhere.

Check out these inspiring GitHub repos to get a better idea of how you can stretch the UNO Q Arcade bundle: 

• Hackathon Project Spear
• Arduino Summer Game
• SummerEngine Hackathon
• Penguin Modulino Explorer

Ready? Set? Play!

While the community has been building these kinds of playful and retro projects for years, UNO Q makes them more capable than ever. With the bundle, we want to make it easier to get started, and more affordable to dive in. Bring your creativity! We’ve taken care of the rest.

Get the UNO Q Arcade bundle (with 20% discount until September 10th!).

Check out the full tutorial on Arduino Project Hub.

Qualcomm branded products are products of Qualcomm Technologies, Inc. and/or its subsidiaries. Arduino, Modulino, UNO, and the Arduino logo are trademarks or registered trademarks of Arduino S.r.l.

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If you want to build RC airplanes, you’ll need a way to make lightweight wings. 3D printing can work with foaming filament, but most 3D printers have limited build volume. A better method is to use a hot wire cutter to shape foam. Michael Rechtin’s CNC hot wire cutter automates that process across four axes, which is perfect for making large, high-quality airplane wings.

Hot wire cutters are very simple and you can get basic handheld models for about $15. They work by passing current through a nichrome (nickel-chromium) wire, so resistive heating makes the wire hot. When the temperature is right, that wire will melt material (like Styrofoam) on contact, resulting is a straight cut with almost no force.

Rechtin’s machine pulls the hot nichrome wire between two actuated assemblies, each with two axes. They move the ends of the nichrome wire independently, both horizontally and vertically. When the two sides move the same way, you get cuts straight across — a consistent cross section. If they move differently, the wire can stretch across at an angle, so more complex shapes are possible.

Almost all of the parts of the machine are identical to what you’d find on a 3D printer: aluminum extrusion, stepper motors, a RAMPS 1.4 controller on an Arduino Mega 2560 board, and TMC2209 stepper drivers. That receives G-code created with an online tool called RC Airplane Wing Designer, which was built specifically for this kind of work.

The only unique hardware is that used for the nichrome wire. A novel pulley mechanism keeps the wire under constant tension, regardless of machine position. Power comes from a variable DC power supply, so Rechtin can tune the voltage to achieve the right current and resistance to get the best wire temperature for performance. Then the wire physically extends between MIG welding tips, which are designed for feeding wire and passing current.

The machine works great, giving Rechtin the ability to fabricate the perfect airfoils for his RC airplanes.

The post This CNC hot wire cutter was created specifically for making airplane wings appeared first on Arduino Blog.

Michel Willems runs a photography and print shop in Ontario, Canada. Like all business owners, Willems is always keen on improving efficiency because even small changes can have a big effect over time. He has found it useful to have easy access to important information, so he decided to leverage an Arduino UNO Q to build a simple intranet web server that helps manage operations — tracking everything from current sales and passport office wait times to indoor and outdoor environmental conditions.

Intranets are local and usually small networks, typically used by businesses and organizations to provide access to resources like databases and shared storage. They’re like the internet on a miniature scale. And like the internet, they rely on web servers to host content that users can access from their computers or smartphones. For this project, Willems used an UNO Q to create his own affordable and lightweight web server.

Because the UNO Q runs Linux, there are many compatible web server software packages available. But in this case, Willems didn’t need to the heft of a fully featured package. Instead, he programmed his own server in Python that grabs the relevant data and makes that available on the network. Some of that data comes from local sources (including sensors built into the device), while some of it, like the regional weather forecast, comes from online APIs.

And the UNO Q offers a big bonus perk in the form of its onboard microcontroller. Through that, it can monitor buttons, switches, sensors, and more. It can, for example, react to a button press by showing corresponding data—the same data published by the server—on seven-segment displays. It can also update the information the server provides to users on the network, such as the local temperature and humidity.

While Willem tailored this particular web server to his own needs, it demonstrates that you can use an UNO Q to build your own version that works for your own requirements.

The post Arduino® UNO™ Q intranet web server helps this business run appeared first on Arduino Blog.

Most machines warn you before they fail. A motor begins to vibrate differently. A pump slowly drifts out of balance. A bearing develops a new mechanical signature. A cooling fan starts producing frequencies that were not present during normal operation.

These changes can appear well before a complete breakdown. The challenge is detecting them early, reliably, and at a cost that makes monitoring practical across more than just the factory’s most expensive machines.

UNO Q provides a flexible platform for building a compact predictive maintenance node that collects vibration data, runs a machine learning model locally, and turns unusual machine behavior into actionable alerts. The result is a practical way to begin monitoring motors, pumps, fans, bearings, compressors, and other rotating equipment without immediately deploying a complex cloud infrastructure.

Pickin’ up good (and bad!) vibrations

Vibration is one of the most useful signals for understanding the condition of rotating machinery. When a machine is operating normally, its motor, bearings, shafts, and mechanical components produce a relatively consistent vibration pattern. Changes in alignment, balance, friction, mounting, or component wear can alter that pattern.

A traditional monitoring system might trigger an alert whenever vibration exceeds a predefined value. That approach can be useful, but machines rarely operate under perfectly fixed conditions. Speed, load, product type, temperature, mounting position, and operating mode can all influence the vibration signal. This is where anomaly detection becomes especially valuable.

A vibration sensor does not automatically identify every mechanical fault. It provides the raw signal from which meaningful patterns can be extracted. UNO Q – perhaps starting with an Arduino^® Modulino Movement sensor and later adding a more precise vibration sensor – can capture acceleration data across three axes. Mounted securely on a motor or pump housing, it can record how the machine behaves during normal operation and how that behavior changes when a fault begins to develop.

The sensor can capture more than a single vibration value. It can record a time series that describes the direction, amplitude, and frequency of the movement. This gives a machine learning model more information than a simple threshold alarm.

A team can collect representative data from a healthy machine, train an anomaly detection model, and deploy it to the edge. When the model sees a vibration pattern that is sufficiently different from what it learned, it produces a higher anomaly score.

The system does not need to know the name of every possible fault before deployment. It can begin by answering a more practical question: “Is this machine still behaving as expected?”

See this pattern in action: This sense-infer-respond loop is exactly what’s demonstrated in the AI Guard Demo with Arduino UNO Q, Modulino sensors, and local NPU face recognition, published on Arduino Project Hub by user shivaylamba. The project is built around face recognition, but the underlying architecture – a Modulino sensor triggering local inference, which then drives a physical response – is the same blueprint a vibration-based anomaly detector would follow, just with different sensors and a different model.

Or check out how AudioLog uses Arduino UNO Q Edge AI to “listen” to machines, detecting early signs of failure to prevent costly industrial downtime.

From raw data to business intelligence

A first predictive maintenance project can begin with one machine and a limited number of operating conditions.

The out-of-the-box example shipped with Arduino^® App Lab is a great starting point.

The Modulino Movement sensor is mounted firmly on the equipment. UNO Q records acceleration data while the machine is idle, starting, running normally, operating under different loads, and shutting down.

The microcontroller side manages sensor acquisition, sending the data to the Linux side that manages data logging, model execution, dashboards.

Collecting this range of normal behavior is important. A model trained on only one operating condition may incorrectly classify a legitimate speed or load change as a fault.

Depending on the application, the project can use classification or anomaly detection.

Classification is useful when the team already has examples of known conditions, such as normal operation, imbalance, misalignment, or a loose mounting.

Anomaly detection is useful when fault data is limited or when intentionally damaging equipment to create training examples would be unsafe or impractical. In this case, the model learns normal behavior and highlights signals that do not fit that baseline.

Worth a look: For a hands-on look at what running ML locally on UNO Q actually feels like in practice, check out Running ML/AI on Arduino UNO Q on Hackster. It’s a capability demo rather than a predictive maintenance project specifically, but it walks through the Arduino App Lab sample apps and on-device inference experience that the classification and anomaly detection workflows above are built on top of.

Looking for even more inspiration? Check out this predictive maintenance project that reads automotive CAN bus raw data to determine systematic drifts early, and alert you when the line is not in sync with specs.

A large model is not necessarily a better model

The model for a vibration monitoring application has a narrow job. It does not need to understand images, natural language, or hundreds of unrelated machine types. It only needs to distinguish the relevant operating patterns of the equipment being monitored. This focus on smaller, task-specific models helps make always-on monitoring practical. 

Continuous vibration monitoring can generate a large amount of data, but sending every raw sample to the cloud is not always necessary – especially considering it can increase bandwidth consumption, introduce recurring infrastructure costs, and make the monitoring system dependent on network availability.

UNO Q processes vibration windows locally and stores or transmit only useful information, such as the current health state, anomaly score, operating mode, timestamp, or alert event. A local dashboard can show recent machine behavior and event history. When an anomaly exceeds a validated threshold for a defined period, the system can activate a warning light, sound a buzzer, log the event, or send a message to a maintenance service.

Cloud connectivity can still be added when it provides value. The difference is that the core detection process does not need to stop when the internet connection is unavailable.

Going deeper: Edge Impulse has specific predictive maintenance guidance around monitoring equipment while it runs, so service teams can act before failure occurs. Its optimization tools are designed precisely for constrained edge deployments like this one: quantized int8 models and RAM-optimized compilation matter here because always-on monitoring needs lower compute, lower memory use, and better power behavior over the long run.

The real value of predictive maintenance

Predictive maintenance works when people are given enough warning to inspect a machine before an unexpected stoppage: there still has to be time to act. The great news is UNO Q now brings the sensing, local intelligence, Linux applications, connectivity, and machine-facing control needed to build that workflow on a single platform. It allows teams to start with a simple question – “Is this machine still behaving normally?” – and to develop the answer into a scalable maintenance system.

Ready to never be caught off guard by a faulty machine or worn-down part again? Build a custom predictive maintenance system that you can easily train on your specific data, with UNO Q and Arduino App Lab.

UNO Q is available to order from the Arduino Store as well as DigiKey, Farnell, Mouser, Newark, RS Components, and Robu.in; along with our other authorized distributors and resellers.

Arduino, UNO, Modulino and the Arduino logo are trademarks or registered trademarks of Arduino S.r.l.

The post How to achieve cost-effective predictive maintenance with the Arduino® UNO™ Q board appeared first on Arduino Blog.

Version 0.56.0 of the Arduino^® Core on Zephyr is live – and it’s a sizable update to the earlier release. Think optimized performance, expanded hardware capabilities. We’re still smoothing some edges towards the official Stable release, but if you’ve been testing the beta, prepare for a meaningful upgrade.

Multimedia expansion: Arduino^® GIGA Display Shield & Arduino^® Portenta H7 board video support

Multimedia capabilities take a major step forward in this release, establishing native support for advanced visual outputs.

We have introduced official video support for Portenta H7 alongside full compatibility for GIGA Display Shield when paired with the Arduino^® GIGA R1 WiFi. Whether you are building industrial interfaces, interactive kiosks, or custom dashboards, these display features are now fully integrated into the core and ready for your application code.

Core optimizations and network bug fixes

For our supported board lineup, this release delivers several updates to performance, pin management, and connectivity:

• RAM usage optimization: We have optimized internal memory management across the core. This effectively lowers the core’s background footprint, freeing up more RAM for your sketches, complex variables, and larger application buffers.
• Dynamic pin-muxing improvements: Runtime pin multiplex configurations have been refined. This improvement allows for more flexible and reliable dynamic pin reassignment, ensuring better stability when managing hardware peripherals programmatically.
• Network fixes and improvements: Rather than adding new features, this time around we focused on essential bug fixing within the network stack. These improvements resolve ongoing connection issues and optimize socket management to make your connected prototypes more dependable.

How to get started

To update, open the Arduino^® IDE, search for “zephyr” in the Board Manager, and install the 0.56.0 release. For a granular breakdown of specific code commits and fixes, you can view the full release notes on GitHub.

First use: flashing the Zephyr loader

To prepare a supported board for running Zephyr-based sketches for the first time, you must install the Zephyr loader firmware onto your hardware. Follow these steps within the Arduino IDE 2:

1. Enter bootloader mode: Double-click the physical RESET button on your board.
2. Select a programmer: Go to the Tools -> Programmer menu and select any available programmer.
3. Burn the loader: Navigate to Tools and click Burn Bootloader to write the Zephyr loader to the board.
4. Upload your first sketch: Once the loader is successfully installed, put the board into bootloader mode by double-clicking the RESET button one more time, and upload your sketch. After this initial setup, subsequent uploads will happen automatically without manual resets.

Important reminder: It is highly recommended to update the Zephyr loader with each new core release. Keeping the loader aligned with the current core version ensures your board remains fully functional, secure, and compatible with future framework modifications.

A streamlined workflow for the Arduino^® UNO Q board

If you are using UNO Q, you can completely skip the manual process above. Starting with version 0.56.0, the core automatically checks the loader version behind the scenes during every single sketch upload and handles any necessary updates natively. You can write your code, click upload, and let Arduino IDE and Arduino^® App Lab take care of the rest. 

We are actively working to bring this automated behavior to all other supported boards in future releases.

Help us shape the final release!

Your real-world testing continues to be invaluable as we head towards the Stable milestone. Please share your feedback, report bugs, or contribute on our GitHub Issues page. Thank you for being an active part of the Arduino community!

Arduino, GIGA, Portenta, GIGA R1, and UNO and the Arduino logo are trademarks or registered trademarks of Arduino S.r.l.

The post Arduino Core-zephyr 0.56.0: try it out now, and help us get closer to Stable appeared first on Arduino Blog.

When Manivannan moved to the UK from India, he found that the damp winters provide the perfect conditions for mold growth in the shower. The preventative solution is to run the ventilation fan when showering, but that is easy to forget. So, he built the AntiMould Shower Sentinel to ensure discipline.

This device’s purpose is to sound a warning alarm if the user runs the shower, but doesn’t turn on the ventilation fan that is necessary to prevent mold. When it determines that the user has failed to turn on the fan, it sends a notification to the user via Home Assistant. 

You might be wondering why the device wouldn’t simply turn on the fan itself, because that would solve the problem in a more convenient way. But that would also require modifying the fan’s mains power wiring, which isn’t an option for most renters. Manivannan’s solution doesn’t require anything but Wi-Fi, a USB-C power supply, and a microphone.

That’s because it works using an Arduino UNO Q, which runs a machine learning model deployed via Edge Impulse. That model leverages the microphone to listen to the ambient sound and recognizes the acoustic signature of a running shower. If it hears that, it then checks to see if the fan is running. If the fan is on and continues to stay on for at least 20 minutes, then all is well. If those conditions aren’t met, the user gets the notification through Home Assistant.

It is impressive that Manivannan found an affordable and unobtrusive way to detect the shower and fan states, all in a compact package that doesn’t require any installation steps that might upset a landlord.

The post Keep forgetting the fan and getting mold in the shower? This is the solution appeared first on Arduino Blog.

Dear Builders, Engineers, and Innovators,

Arduino is built on one belief: powerful technology should be accessible to everyone who wants to make, learn, or innovate with it. Staying true to that principle in every decision we make sometimes means making tough calls about what we charge for it. So let me get straight to the point and break the news: the time has come where at Arduino we finally have to increase the price for UNO Q. 

Effective July 6th, we are adjusting the price of UNO Q. UNO Q 2GB will increase from $44 to $59, while UNO Q 4GB from $59 to $79. 

If you follow the news across the tech sector you’ll surely know that memory prices have been on a rally, fueled by the enduring demand for AI applications. Over the last six months alone, our memory component costs have more than doubled. Arduino is not immune to these effects but, thanks to support from Qualcomm Technologies, Inc., we held off increasing the price for as long as possible, enabling the community to do what it does best: innovating and sharing projects. However, with no near-term signs of relief in memory supply or pricing, continuing to absorb these costs is no longer possible.

Since its launch in October 2025, I’ve been amazed at the scale of uptake for UNO Q and by the amazing breadth of projects developed using the board. You’ve exceeded every expectation! Personally, I have loved the ingenious innovation from the community – my personal favorites being the Invisible Mess Glasses based on UNO Q 2GB and the AI Plant Guardian based on UNO Q 4GB. 

The price increase takes effect on July 6th, so you still have the opportunity to take advantage of the current pricing for a few more days, and for a limited time you can get a hold of a 4GB board for what will be the 2GB board’s new price!

Thank you for the trust you place in Arduino, and for the energy you bring to every build. What you create with these boards is why we do this work. I look forward to seeing what you all will build, channeling your creativity into your next amazing project on UNO Q.

– Marcello Majonchi, Arduino Maker and Arduino Chief Product Officer

UNO Q is available to order from the Arduino Store and DigiKey, Farnell, Mouser, Newark, RS Components, and Robu.in, as well as our other authorized distributors and resellers.

Arduino and UNO are trademarks or registered trademarks of Arduino S.r.l.

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The Modulino family keeps growing, to allow you to easily expand your projects with new tiny modules that bring additional functionalities – in a snap! 

With Modulino Hub, Modulino Extender, and Modulino Motors joining the range, you now have no less than 15 easy options to make building your next idea easier and more fun than ever. Think of them as ready-to-deploy functionalities you can add, swap around, and link together to learn, create, or prototype interactive and automated devices.

Let’s get to know the three new modules!

After releasing Modulino LED Matrix, we tackled three pain points the Arduino community has been voicing. 

• Not enough I²C channels? The I²C protocol supports up to 127 devices in theory, but in practice address conflicts between components can become a real bottleneck long before you get there – especially in complex builds where multiple sensors or actuators share the same default address. When your project hits that wall, you can now add a Modulino Hub. By daisy-chaining it to your existing setup, you gain 8 new independent I²C channels, each with its own isolated address space, so you can keep adding components without conflicts and without changing a line of code in your existing logic.
• Have a project that’s too large for I²C to handle? I²C is great for short-range communication, but if you have longer distances between devices or larger installations you have two choices: either change protocol and start over, or simply boost the signal. Of course, you had us at simply. Get a Modulino Extender, place it on the I²C bus, and it will amplify the signal to approximately 30 meters (?100 ft) when operating at 100 kHz. No additional libraries needed, because this is a 100% hardware module.
• Have a robot or automated mechanism to control? Try Modulino Motors to skip the spaghetti wiring between central controller and devices, and control two DC motors or a single stepper motor with precision. Now you can adjust speed, direction, and position right where the action happens.

What exactly is Modulino? 

Modulino is our range of plug-and-play smart components designed to take the friction out of building with electronics. Each node connects via Qwiic cable and supports daisy-chaining, so you can combine sensors, actuators, and controllers without redesigning your hardware every time you add something new. A dedicated library – supporting both Arduino language (based on C++) and MicroPython – means the learning curve stays gentle, no matter where you are on your path.

The design philosophy is consistency: every Modulino node shares the same (tiny!) form factor and connection standard, so swapping components between projects is straightforward, and breakout pins give advanced users room to push customization further. 

Modulino nodes connect directly to  Arduino UNO Q, Nesso N1, UNO R4 WiFi, and Nano boards, and integrate naturally with Arduino^® Cloud tools and the broader Arduino ecosystem – making them equally valuable in a classroom, a maker workshop, or a professional edge IoT prototyping lab. 

Get your new Modulino today

Modulino Hub, Modulino Extender, and Modulino Motors are available today from the Arduino Store today, as well as through trusted official distributors worldwide. 

Arduino, Modulino, UNO, Nesso, and Nano are trademarks or registered trademarks of Arduino S.r.l.

The post Three new Arduino® Modulino™ modules are here! Bigger ideas now come with zero added stress appeared first on Arduino Blog.

The human brain is remarkably efficient, running on the caloric equivalent of about 20 watts of power. But that’s still about 20% of the total required for your entire body, which means your head can get pretty hot. Mike Warren does big thinking and to keep his brain from overheating, he built this helmet to cool his dome automatically based on brain activity.

All of that “you only use 10% of your brain” stuff you’ve heard is complete and utter nonsense. In reality, you’re always using most of your brain. Actively thinking hard only increases energy requirements by a small amount — if at all. So, solving a puzzle shouldn’t make your head noticeably warmer. But this is a silly project for fun, so don’t take the premise too seriously.

Warren created the Brain Turbocharger around a Star Wars Force Trainer toy, which originally hit the market back in 2009. Ostensibly, that toy relies on a basic form of EEG (electroencephalography) to sense brain activity and turn on a fan that blows a ball. It is the same gimmick as many “mind control” toys.

Here, Warren repurposed that output. An Arduino UNO Rev3 board detects the fan signal from the toy’s EEG headset — the signal that corresponds to strong brain activity — and then activates a whole bunch of small cooling fans mounted to the helmet. Power comes from a USB battery pack and the Arduino directs that power to the fans through MOSFET modules. 

The result is significant airflow on and around the scalp, triggered by concentration.

The post Brain hot from serious thinking? This helmet automatically cools your head appeared first on Arduino Blog.