Machining A Golf Ball To Make A Lovely Tactile Volume Knob

Golf balls are wonderfully tactile things. They have a semi-grippy covering, and they’re a beautiful size and weight that sits nicely in the hand. Sadly, most of them just get smacked away with big metal clubs. [Jeremy Cook] recognized their value as a human interface device, though, and set about turning one into a useful volume knob.

The trick here is in the machining. [Jeremy] used a 3D printed jig to hold a golf ball tightly in place so that it could be machined using a milling machine. With the bottom taken off and a carefully-designed 3D printed insert in the bottom, the golf ball is ready to be used as a knob for a volume control. As for the hardware side of things, [Jeremy] used an existing USB keypad, fitting the golf ball onto the encoder for volume and seek control in various programs.

The results sadly weren’t ideal. While the golf ball sits nicely upon the encoder, [Jeremy] found the device uncomfortable to use. Size may be an issue, but we also suspect the crowding of the surrounding buttons has a role to play. It forces the wrist into an uncomfortable curve to access the ball without hitting the surrounding controls. Without that, it may be greatly improved.

Files are available for those wishing to make their own. We don’t get a lot of golf ball builds here on Hackaday, but we’d love to see more. Hit up the tipsline if you’ve got ’em. Video after the break.

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Cheaper Sodastream With A Big CO2 Tank Is A Semi-Dangerous Way To Save

Sodastream machines are a fun way to turn tap water into carbonated water. However, the canisters are expensive and generally require a trip to the store to get a replacement. Lifehacker has a workaround that may make life easier for the bubble-addicted set.

The trick is simple: simply buy a larger bottle of CO2, and hook it up to the Sodastream in place of the regular cartridge. CO2 can be bought in large cylinders at a far cheaper rate than Sodastream will charge you for their proprietary canisters. All you need is a local supplier of food-grade CO2 in cylinders, and you can visit them when you need a refill or swap.

There are several caveats, though, which the comment section dicussed when we featured a similar hack before. Getting an extra-large CO2 canister can pose a risk to life if there’s a leak. Alarms may not save you as the heavy gas has a tendency to lurk low to the ground. You should also consider using a regulator to lower the pressure from your large canister to something closer to the levels the Sodastream machine is built to withstand. Beyond that, you want to ensure you’re using food-grade CO2. Don’t go bubbling cheap welding gas through your water if you want to live a long and healthy life.

It’s a neat hack, it’s just one that requires you to practice proper gas safety at all times. Reports are that a cylinder costing less than $200 can last you for several years though, with ultra-cheap refills, so it may indeed be worth the hassle! Go forth and bubble, friends.

Microbubbles And Ultrasound: Getting Drugs Through The Blood-Brain Barrier

The brain is a rather important organ, and as such, nature has gone to great lengths to protect it. The skull provides physical protection against knocks and bumps, but there’s a lesser-known defense mechanism at work too: the blood-brain barrier. It’s responsible for keeping all the nasty stuff – like bacteria, viruses, and weird chemicals – from messing up your head.

The blood-brain barrier effectively acts as a filter between the body’s circulatory system and the brain. However, it also frustrates efforts to deliver drugs directly to the brain for treating conditions like brain tumors. Now, scientists have developed a new technique that may allow critical life saving drugs to get through the barrier with the help of ultrasound technology. 

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Hackaday Prize 2023: A DIY Voice-Control Module

If science fiction taught us anything, it’s that voice control was going to be the human-machine interface of the future. [Dennis] has now whipped up a tutorial that lets you add a voice control module to any of your own projects.

The voice control module uses a Raspberry Pi 4 as the brains of the operation, paired with a Seeed Studio ReSpeaker 4-microphone array. The Pi provides a good amount of processing power to crunch through the audio, while the mic array captures high-quality audio from any direction, which is key to reliable performance. Rhasspy is used as the software element, which is responsible for processing audio in a variety of languages to determine what the user is asking for. Based on the voice commands received, Rhasspy can then run just about anything you could possibly require, from sending MQTT smart home commands to running external programs.

If you’ve always dreamed of whipping up your own version of Jarvis from Iron Man, or you just want a non-cloud solution to turn your lights on and off, [Dennis’s] tutorial is a great place to start. Video after the break.

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Hackaday Prize 2023: Eye-Tracking Wheelchair Interface Is A Big Help

For those with quadriplegia, electric wheelchairs with joystick controls aren’t much help. Typically, sip/puff controllers or eye-tracking solutions are used, but commercial versions can be expensive. [Dhruv Batra] has been experimenting with a DIY eye-tracking solution that can be readily integrated with conventional electric wheelchairs.

The system uses a regular webcam aimed at the user’s face. A Python script uses OpenCV and a homebrewed image segmentation algorithm to analyze the user’s eye position. The system is configured to stop the wheelchair when the user looks forward or up. Looking down commands the chair forward. Glancing left and right steers the chair in the given direction.

The Python script then sends the requisite commands via a TCP connection to an ESP32, which controls a bunch of servos to move the wheelchair’s joystick in the desired manner. This allows retrofitting the device on a wheelchair without having to modify it in an invasive manner.

It’s a neat idea, though it could likely benefit from some further development. A reverse feature would be particularly important, after all. However, it’s a great project that has likely taught [Dhruv] many important lessons about human-machine interfaces, particularly those beyond the ones we use every day. 

This project has a good lineage as well — a similar project, EyeDriveOMatic won the Hackaday prize back in 2015.

Hackaday Prize 2023: The NEOKlacker Pocket Computer

Science fiction always promised us pocket computers. These days, we’re spoiled for choice. [Spider Jerusalem] eschewed a simple smartphone or tablet, though, instead building a custom pocket computer of their own design. 

Like so many other DIY cyberdecks and handheld computers, this one relies on a Raspberry Pi. In this case, it’s built using a Pi 4 with 8GB of RAM, which offers a snappy experience that wasn’t available on the earliest boards. [Spider] paired it with a nifty 720×720 LCD screen and a full QWERTY button pad, wrapped up in a tidy 3D-printed case. Like any good pocket computer, it’s well-connected, thanks to a 4G LTE cellular data connection.

It might seem to be a build without a purpose in this era, but that’s not necessarily the case. When it comes to running barebones Linux utilities at a real command line, a Raspberry Pi offers some utility that the average smartphone doesn’t have out of the box. It’s a useful tool if you need to interface with a server on the go or do some low-level network diagnostics without carrying a whole laptop around. Video after the break.

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Hackaday Prize 2023: Finger Tracking Via Muscle Sensors

Whether you want to build a computer interface device, or control a prosthetic hand, having some idea of a user’s finger movements can be useful. The OpenMuscle finger tracking sensor can offer the data you need, and it’s a device you can readily build in your own workshop.

The device consists of a wrist cuff that mounts twelve pressure sensors, arranged radially about the forearm. The pressure sensors are a custom design, using magnets, hall effect senors, and springs to detect the motion of the muscles in the vicinity of the wrist.

We first looked at this project last year, and since then, it’s advanced in leaps and bounds. The basic data from the pressure sensors now feeds into a trained machine learning model, which then predicts the user’s actual finger movements. The long-term goal is to create a device that can control prosthetic hands based on muscle contractions in the forearm. Ideally, this would be super-intuitive to use, requiring a minimum of practice and training for the end user.

It’s great to see machine learning combined with innovative mechanical design to serve a real need. We can’t wait to see where the OpenMuscle project goes next.

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