One of the challenges of diagnosing diseases is identifying them early. At this stage, signs may be vague or confusing, or difficult to identify. Early diagnosis is often tied to the best possible treatment outcomes, so there’s plenty of incentives to improve methods in this way.
A new voice-based method of diagnosing disease could prove fruitful in this regard. It relies on machine learning techniques to detect when patients may be suffering from certain conditions.
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.
Medical training simulators are expensive, but important, pieces of equipment. [Decent Simulators] is designing simulators that can easily be replicated using Fused Deposition Modeling (FDM) printers and silicone molds to bring the costs down.
Each iteration of the simulators is sent out for testing by paramedics and doctors around the world, and feedback is integrated into the next revision. Because the trainers are designed to be easily replicated, parts can easily be replaced or repaired which can be critical to keep personnel trained, especially in remote areas.
While not open source, some models are freely available on the [Decent Simulators] website like wound packing trainers or wound prostheses which could be great if you’re trying to get a head start on next year’s Halloween costumes. More complicated models will be on sale starting in January as either just the design files or a kit containing the files and the printed and/or silicone parts.
Cyberpunk is full of characters with cool body mods, and [bsmachinist] has made a prosthetic eye flashlight (TikTok) that is both useful and looks futuristic. [via Reddit]
[bsmachinist] has been machining titanium prosthetic eyes for over five years now, and this latest iteration, the Skull Lamp, has a high brightness LED that he says is great for reading books at night as well as any other task you might have for a headlamp. Battery life is reported as being 20 hours, and the device is switched by passing a magnet (Instagram) near the prosthetic.
Hearing loss is a common problem for many – especially those who may have attended too many loud concerts in their youth. [mircemk] had recently been for a hearing test, and noticed that the procedure was actually quite straightforward. Armed with this knowledge, he decided to build his own test system and document it for others to use.
Resultant audiogram from the device showing each ear in a different color
By using an Arduino to produce tones of various stepped frequencies, and gradually increasing the volume until the test subject can detect the tone, it is possible to plot an audiogram of hearing threshold sensitivity. Testing each ear individually allows a comparison between one side and the other.
[mircemk] has built a nice miniature cabinet that holds an 8×8 matrix of WS2812 addressable RGB LEDs. A 128×64 pixel OLED display provides user instructions, and a rotary encoder with push-button serves as the user input.
Of course, this is not a calibrated professional piece of test equipment, and a lot will depend on the quality of the earpiece used. However, as a way to check for gross hearing issues, and as an interesting experiment, it holds a lot of promise.
There is even an extension, including a Class D audio amplifier, that allows the use of bone-conduction earpieces to help narrow down the cause of hearing loss further.
[Gregor Herz] caught wind of a problem that neuropediatric clinics in Germany have been facing recently. Orfiril, a seizure-preventing medication used in those clinics for treating children, is normally prescribed to adults, and the usual dosages are too high for kids. Orfiril comes in regular pill-shaped capsules, each capsule containing a bunch of small medication-soaked pellets, and you only need a certain amount of these pellets if you want to achieve a lower dose.
It used to be that you could get a special spoon helping you to get a proper dosage — but sadly, the original supplier has quit making these. So, our hacker designed a 3D printable model instead.
[Gregor] tells us that a lot of clinics in Germany are facing this exact issue right now, so sharing this model may mean that more hospitals can work around the supply issue. Provided a friendly hobbyist has food-grade 3D printing conditions available, anyway. He tells about some suitable filaments models you can buy, as well as research on food-grade printing requirements — a topic we’ve talked about in detail, and just this month have seen someone revisit with reassuring results. Are you interested in printing some of these? If so, there might be a clinic nearby that’d be thankful.
Despite the proliferation of artificial lighting, humans are still highly dependent on sunlight for regulation of our circadian rhythms. Accordingly, [Sector 07] has built a futuristic headboard that can help with the waking up side of things whether you’re headed to space or just in the dead of winter.
The interior of the headboard includes custom 3D printed panels to mount the electronics and a light diffusion screen made of nylon fabric. The printed parts were all joined by “welding” the pieces with a soldering iron and extra filament. Besides the futuristic hexagon motif in the diffusion screen, the most eye-catching part of this build is the curved ends making it look like a set piece from Star Trek: TNG. [Sector 07] was able to get the unique shape by kerf bending the plywood ends before joining them to the flat sections with dowels and wood glue.
Since this build also includes an integrated coffee maker and voice assistant, there’s a bit more going on with the electronics than you might have in a normal circadian lamp. Powering the project are two Arduino Mega boards and a SpeakUp Click that handles the voice commands. Wake-up times are controlled via a keypad, and the voice assistant, Prisma, will ask if you are awake once the 30 minute sun simulation has completed before your alarm goes off. If you don’t confirm wakefulness, Prisma will escalate alarms until the system is sure you’re awake and then will ask if you want coffee. If you want a deep dive into the system’s functionality, be sure to checkout the video after the break.