In a perfect futuristic world you have pre-emptive 3D scans of your specific anatomy. They’d be useful to compare changes in your body over time, and to have a pristine blueprint to aid in the event of a catastrophe. As with all futuristic worlds there are some problems with actually getting there. The risks may outweigh the rewards, and cost is an issue, but having 3D imaging of a sick body’s anatomy does have some real benefits. Take a journey with me down the rabbit hole of 3D technology and Gray’s Anatomy.
In March of 2014, I knew my eight year old daughter was sick. Once borderline overweight, she was now skeletally thin and fading away from us. A pre-dawn ambulance ride to the hospital gave us the devastating news – our daughter had Type 1 diabetes, and would be dependent on insulin injections for the rest of her life.
This news hit me particularly hard. I’ve always been a preparedness-minded kind of guy, and I’ve worked to free myself and my family from as many of the systems of support as possible. As I sat in the dark of the Pediatric ICU watching my daughter slowly come back to us, I contemplated how tied to the medical system I had just become. She was going to need a constant supply of expensive insulin, doled out by a medical insurance system that doesn’t understand that a 90-day supply of life-saving medicine is a joke to a guy who stocks a year supply of toilet paper. Plus I had recently read an apocalyptic novel where a father watches his 12-year old diabetic daughter slip into a coma as the last of her now-unobtainable insulin went bad in an off-grid world. I swore to myself that I’d never let this happen, and set about trying to find ways to make my own insulin, just in case.
A team at [Vanderbilt University] have been hacking together their own peristaltic pumps. Peristaltic pumps are used to deliver precise volumes of fluid for research, medical and industrial applications. They’re even occasionally used to dose fish tanks.
They work by squeezing the fluid in a flexible tube with a series of rollers (check out the awesome gif from Wikipedia to the right). We’ve seen 3D printed peristaltic pumps before, and cheap pumps have been appearing on eBay. But this build is designed to be lab grade, and while the cheap eBay devices can deliver ~20ml/min this one can deliver flow rates in the microliter/min range. It also has a significant cost advantage over commercial research grade pumps which typically cost thousands of dollars, each of these pumps costs only fifty bucks.
The pump has a clear hacker heritage, using an Arduino Uno, Adafruit Motor shield, and 3D printed mechanical parts. So it’s particularly awesome that they’ve also made their design files and Arduino code freely available!
Simple blood tests can lead a doctor toward a diagnosis of blood cancers, like leukemia, lymphoma and myeloma, but to really see what’s going on, he or she needs to go to the source of the problem: the bone marrow. Examining maturing blood cells from the marrow with a microscope is an important step in staging the disease and developing a plan for treatment, but it’s a tedious and error-prone process that requires a doctor to classify and tally a dozen or so different cells based on their size, shape and features. Automated systems like flow cytometry and image analysis software can help, but in an austere environment, a doctor might not have access to these. Luckily, there’s now an on-line app to assist with bone marrow cytometry.
Thanks to [Eduardo Zola], a doctor can concentrate on classifying cells without looking up from the microscope, and without dictating to an assistant. Keys are assigned to the different cell morphologies, and a running total of each cell type is kept. With practice, the doctor should be able to master the keying for the various cells; we suspect the generation of physicians that grew up with the WASD keying common in PC-based gaming might have a significant advantage over the older docs when it comes to learning such an app.
[Eduardo]’s app seems like a simple way to improve on an important medical procedure, and an enabling technology where access to modern instrumentation is limited. To that end, one area for improvement might be a standalone app that can run on a laptop without internet access, or perhaps even a version that runs on a smart phone. But even as it is, it’s a great entry for the 2015 Hackaday Prize.
The 2015 Hackaday Prize is sponsored by:
Syringe pumps are valuable tools when specific amounts of fluid must be dispensed at certain rates and volumes. They are used in many ways, for administering IV medications to liquid chromatography (LC/HPLC). Unfortunately, a commercial pump can cost a pretty penny. Not particularly thrilled with the hefty price tag, [Aldric Negrier] rolled up his sleeves and made a 3D-printed version for 300 USD.
[Aldric] has been featured on Hackaday before, so we knew his latest project would not disappoint. His 3D Printed Syringe Pump Rack contains five individual pumps that can operate independently of each other. Five pieces are 3D-printed to form the housing for each pump. In addition, each pump is composed of a Teflon-coated lead screw, an Arduino Nano V3, a Pololu Micro stepper motor driver, and a NEMA-17 stepper motor. The 3D Printed Syringe Pump Rack runs on a 12V power supply using a maximum of 2 amps per motor.
While the standard Arduino IDE contains the Stepper library, [Aldric] wanted a library that allowed for more precise control and went with the Accelstepper library. The 3D Printed Syringe Pump Rack has a measured accuracy of 0.5µl in a 10ml syringe, which is nothing to laugh at.
Syringe pump racks like [Aldric’s] are another great example of using open source resources and the spirit of DIY to make typically expensive technologies more affordable to the smaller lab bench. If you are interested in other open source syringe pump designs, you can check out this entry for the 2014 Hackaday Prize.
[Myrijam Stoetzer] and her friend [Paul Foltin], 14 and 15 years old kids from Duisburg, Germany are working on a eye movement controller wheel chair. They were inspired by the Eyewriter Project which we’ve been following for a long time. Eyewriter was built for Tony Quan a.k.a Tempt1 by his friends. In 2003, Tempt1 was diagnosed with the degenerative nerve disorder ALS and is now fully paralyzed except for his eyes, but has been able to use the EyeWriter to continue his art.
This is their first big leap moving up from Lego Mindstorms. The eye tracker part consists of a safety glass frame, a regular webcam, and IR SMD LEDs. They removed the IR blocking filter from the webcam to make it work in all lighting conditions. The image processing is handled by an Odroid U3 – a compact, low cost ARM Quad Core SBC capable of running Ubuntu, Android, and other Linux OS systems. They initially tried the Raspberry Pi which managed to do just about 3fps, compared to 13~15fps from the Odroid. The code is written in Python and uses OpenCV libraries. They are learning Python on the go. An Arduino is used to control the motor via an H-bridge controller, and also to calibrate the eye tracker. Potentiometers connected to the Arduino’s analog ports allow adjusting the tracker to individual requirements.
The web cam video stream is filtered to obtain the pupil position, and this is compared to four presets for forward, reverse, left and right. The presets can be adjusted using the potentiometers. An enable switch, manually activated at present is used to ensure the wheel chair moves only when commanded. Their plan is to later replace this switch with tongue activation or maybe cheek muscle twitch detection.
First tests were on a small mockup robotic platform. After winning a local competition, they bought a second-hand wheel chair and started all over again. This time, they tried the Raspberry Pi 2 model B, and it was able to work at about 8~9fps. Not as well as the Odroid, but at half the cost, it seemed like a workable solution since their aim is to make it as cheap as possible. They would appreciate receiving any help to improve the performance – maybe improving their code or utilising all the four cores more efficiently. For the bigger wheelchair, they used recycled car windshield wiper motors and some relays to switch them. They also used a 3D printer to print an enclosure for the camera and wheels to help turn the wheelchair. Further details are also available on [Myrijam]’s blog. They documented their build (German, pdf) and have their sights set on the German National Science Fair. The team is working on English translation of the documentation and will release all design files and source code under a CC by NC license soon.
With prosthetics, EEG, and all the other builds focused on the body and medicine for this year’s Hackaday Prize, it might be a good idea to take a look at what it takes to measure the tiny electrical signals that come from the human body. Measuring brain waves or heartbeats indoors is hard; AC power frequencies easily couple to the high impedance inputs for these measurements, and the signals themselves are very, very weak. For his entry to The Hackaday Prize, [Paul Stoffregen] is building the tools to make EEG, ECG, and EMG measurements easy with cheap tools.
If the name [Stoffregen] sounds familiar, it’s because he’s the guy behind the Teensy family of microcontroller boards and several dozen extremely popular libraries for everything from displays to real time clocks. The biopotential signal library continues in [Paul]’s tradition of building very cool stuff with just code.
The hardware used in this project is TI’s ADS1294, a 24-bit ADC with either 4 or 8 channels. This chip is marketed as a medical analog front end with a little bit of ECG thrown in for good measure. [Paul] is only using the ADS1294 initially; more analog chips can be added later. It’s a great project in its own right, and when you include the potential applications of this library – everything from prosthetics to body sensors – it makes for an awesome Hackaday Prize entry.