Exoskeletons are demonstrably awesome, allowing humans to accomplish feats of strength beyond their normal capacity. The future is bright for the technology — not just for industrial and military applications, but especially in therapy and rehabilitation. Normally, one thinks of adults who have lost function in their limbs, but in the case of this exoskeleton, developed by The Spanish National Research Council (CSIC), children with spinal muscular atrophy are given a chance to lead an active life.
Designing prosthetics for children can be difficult since they are constantly growing, and CSIC’s is designed to be telescopic to accommodate patients between the ages 3-14. Five motors in each leg adapt to the individual symptoms of the patient through sensors which detect the child’s intent to move and simulates what would be their natural walking gait.
DIY medical science is fun stuff. One can ferret out many of the electrical signals that make the body run with surprisingly accessible components and simple builds. While the medical community predictably dwells on the healthcare uses of such information, the hacker is free to do whatever he or she wants.
You don’t have to know a word Swedish to understand that 86-year old [Lasse Thörn] is the coolaste modernaste pensionären in Gränna. All you have to do is see him rolling on his walker-assisted hoverboard and you’ve got the whole story.
Still, not knowing any Swedish and the spotty nature of Google translations makes it hard to discern the details of this build. Did [Lasse] build the folding aluminum bracket that connects the battery-powered hoverboard to his walker himself? We guess that he did, since another story says that he built a pedal boat back in the 1950s because he thought it sounded cool. He also says that he gets a lot of attention when he’s out on his contraption, and that other seniors have asked him to build one. [Lasse] says he’s too old to start a business; we don’t think he’s giving himself enough credit, but if he’s willing to leave the field of affordable personal mobility open to the rest of us, we say go for it.
We’ve seen lots of hoverboard builds lately, and lots of hate in the comments about the use of that term. Seems like the false advertising vibe grates on folks, but face it: “rolling wheelie board” is kind of awkward, and until technology catches up with the laws of physics, it’s the best we’re going to do.
Slit lamps are prohibitively expensive in the third world areas of India where they are most needed. An invention that’s been around for over a hundred years, the slit lamp is a simple-in-concept way to see and diagnose a large array of ocular issues.
Since they are relatively old by technological standards, the principles behind them have become more and more understood as time has gone on. While a nice lab version with a corneal microscope is certainly better, innovations in manufacturing have brought the theoretical minimum cost of the device way down, or at least that’s what [Kewal Chand Swami] hopes.
His design aims for portability and cost reduction. It must be able to travel to remote locations and it must be significantly cheaper than the lab versions. It uses off-the-shelf lenses in a 3D printed housing with a simple LED torch, the kind you can buy for a dollar at the check-out stand.
The assembly slides onto the user’s head and is held there with straps. The doctor can adjust where the slit the lamp shines and also look through a microscope to diagnose the issue. Hopefully devices like this will see similar community support to the prosthetic projects we’ve covered.
Sometimes the journey is as interesting as the destination, and that’s certainly the case with [Marc]’s pursuit of measuring his sleep apnea (PDF, talk slides. Video embedded below.). Sleep apnea involves periods of time when you don’t breathe or breathe shallowly for as long as a few minutes and affects 5-10% of middle-aged men (half that for women.) [Marc]’s efforts are still a work-in-progress but along the way he’s tried a multitude of things, all involving different technology and bugs to work out. It’s surprising how many ways there are to monitor breathing.
His attempts started out using a MobSenDat Kit, which includes an Arduino compatible board, and an accelerometer to see just what his sleeping positions were. That was followed by measuring blood O2 saturation using a cheap SPO2 sensor that didn’t work out, and one with Bluetooth that did work but gave results as a graph and not raw data.
Next came measuring breathing by detecting airflow from his nose using a Wind Sensor, but the tubes for getting the “wind” from his nose to the sensor were problematic, though the approach was workable. In parallel with the Wind Sensor he also tried the Zeo bedside sleep manager which involves wearing a headband that uses electrical signals from your brain to tell you what sleep state you’re in. He particularly liked this one as it gave access to the data and even offered some code.
And his last approach we know of was to monitor breathing by putting some form of band around his chest/belly to measure expansion and contraction. He tried a few bands and an Eeonyx conductive textile/yarn turned out to be the best. He did run into noise issues with the Xbee, as well as voltage regulator problems, and a diode that had to be bypassed.
The lucky and resourceful hacker in this case is one [Julien Schuermans], who managed to take home pieces of a multi-million dollar da Vinci Si surgical robot. Before anyone cries “larcency”, [Julien] appears to have come by the hardware legitimately – the wrist units of these robots are consumable parts costing about $2500 each, and are disposed of after 10 procedures. The video below makes it clear how they interface with the robot arm, and how [Julien] brought them to life in his shop. A quartet of Arduino-controlled servos engages drive pins on the wrist and rotates pulleys that move the cables that drive the instruments. A neat trick by itself, but when coupled with the Leap Motion controller, the instruments become gesture controlled. We’re very sure we’d prefer the surgeon’s hands on a physical controller, but the virtual control is surprisingly responsive and looks like a lot of fun.
Ziehl-Neelsen Sputum Smear Microscopy (ZN) is one of most common methods for diagnosing Tuberculosis. On the equipment side, it requires not much more than an optical microscope, although it still needs a trained professional to look through the glass, identify and count the number of bacteria in a sample. To provide reliable and effective Tuberculosis diagnostic to regions, where both equipment and trained personnel is in short supply, [Rodrigo Loza] and [khalilnallar] are developing an automated digital microscope based on computer vision and machine learning, their entry for the Hackaday Prize.
They started out gathering images of Tuberculosis bacteria from the internet and experimented with color threshold algorithms to detect dyed bacteria, as well as algorithms for counting individual and clusters of bacteria. This process alone can, according to the team, take a trained professional 30 minutes or more. A graphical interface highlights identified bacteria and reads the bacteria count.
[Rodrigo Loza] and [khalilnallar] are testing their device at the Dr. Roberto Galindo Teran hospital in Cobija, Bolivia. However, getting access to a lab environment is one thing, and being given access to a steady supply of fresh M. Tuberculosis samples is another. Unable to obtain samples, which they need to test their algorithms on live subjects, they turned to another front of their project: The hardware. In several iterations, they developed a low-cost, 3D-printable kit, which transforms a laboratory-grade optical microscope into an embedded CNC-controlled microscopy platform. Their kit comprises three stepper-motor-based axis for the X, Y and Z direction, as well as a webcam mount. An Intel Edison and a custom, Arduino compatible shield control the system to achieve features such as homing procedures, autofocus and bacteria detection.
The team is currently in the process of refining their bacteria detection pipeline, exploring the feasibility of semi-automated detection methods, machine learning and neural networks for classification of bacteria within the hardware constraints. The video below shows their latest update on the Z-axis of their microscope.