Using A Thermal Camera To Spot A Broken Wrist

Chemist and Biochemist [Thunderf00t] has shown us a really interesting video in which you can spot the wrist he broke 10 years ago using a thermal camera.

He was on an exercise bike while filming himself on a high-resolution thermal camera, As his body started to heat up he noticed that one hand was not dumping as much heat as the other. In fact one was dumping very little heat. Being a man of science he knew there must be some explanation for this. He eventually came to the conclusion that during a nasty wrist breaking incident about 10 years ago it must have affected the blood-flow to that hand, Which would go on to produce these type of results on a thermal camera while exercising.

Using thermal camera’s to spot fractures in the extremities is nothing new as it has the benefit of eliminating radiation exposure for patients, But it’s not as detailed as an X-ray or as cool as fluoroscopy and is only useful for bones near the surface of the skin.  It’s still great that you can visualize this for yourself and even after 10 years still notice a significant difference.

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Using Machine Learning To Cut Down Surgical Videos

Recording video of medical surgeries is a great way to both educate doctors in training and identify process improvements. However, surgeries can be very time consuming, and it can be a gargantuan task to sort through endless hours of video searching for relevant points where the action happens. To tackle this issue, researchers at MIT have used machine learning techniques to analyse videos of surgical procedures.

There’s some fairly serious mathematics behind this sort of videographic analysis.

The machine learning algorithm needed to be trained to identify the relevant parts of surgical videos. To do this, the laparoscopic surgeries being investigated were split up into distinct stages, each relating to a different part of the surgical process. Researchers would then watch recordings of prior surgeries and mark the start of each stage. This data was used to train the model which was then used to sift through other recordings to capture the key moments of each surgery.

The time-saving advantages of such technology could be applied to a great many fields – such an algorithm could be put to great use to sort through hours of uneventful security footage looking for anomalies, or rapidly cut together holiday footage so you only have to see the good parts. We’d love to see the researchers release footage showing the algorithm’s work – thus far, all we have to go off is the project paper.

If you’re thirsty for more machine learning knowledge, read up on the state of working with neural networks in 2017.

Hackaday Prize Entry: Heart Failure Detection Device

Early and low-cost detection of a Heart Failure is the proposal of [Jean Pierre Le Rouzic] for his entry for the 2017 Hackaday Prize. His device is based on a low-cost Doppler device, like those fetal Doppler devices used to listen an unborn baby heart, feeding a machine learning algorithm that could differentiate between a healthy and an unhealthy heart.

The theory behind it is that a regular, healthy heart tissue has a different acoustic impedance than degenerated tissue. Based on the acoustic impedance, the device would classify the tissue as: normal, degenerated, granulated or fibrous. Each category indicates specific problems mostly in connective tissues.

There are several advantages to have a working device like the one [Rouzic] is working on. To start, it would be possible to use it at home, without the intervention of a doctor or medical staff. It seems to us that would be as easy as using a blood pressure device or a fetal Doppler. It’s also relatively cheap (estimated under 150$) and it needs no gel to work. We covered similar projects that measure different heart signals, like Open Source electrocardiography, but ECG has the downfall that it requires attaching electrodes to the body.

One interesting proposed feature is that what is learn from a single case, is sent to every devices at their next update, so the devices get ‘smarter’ as they are used. Of course, there are a lot of ways for this to go wrong, but it’s a good idea to begin with.

Measuring Walking Speed Wirelessly

There are a lot of ways to try to mathematically quantify how healthy a person is. Things like resting pulse rate, blood pressure, and blood oxygenation are all quite simple to measure and can be used to predict various clinical outcomes. However, one you may not have considered is gait velocity, or the speed at which a person walks. It turns out gait velocity is a viable way to predict the onset of a wide variety of conditions, such as congestive heart failure or chronic obtrusive pulmonary disease. It turns out, as people become sick, elderly or infirm, they tend to walk slower – just like the little riflemen in your favourite RTS when their healthbar’s way in the red. But how does one measure this? MIT’s CSAIL has stepped up, with a way to measure walking speed completely wirelessly.

You can read the paper here (PDF). The WiGate device sends out a low-power radio signal, and then measures the reflections to determine a person’s location over time. Alone, however, this is not enough – it’s important to measure the walking speed specifically, to avoid false positives being triggered by a person simply not moving while watching television, for example. Algorithms are used to separate walking activity from the data set, allowing the device to sit in the background, recording walking speed data with no user interaction required whatsoever.

This form of passive monitoring could have great applications in nursing homes, where staff often have a huge number of patients to monitor. It would allow the collection of clinically relevant data without the need for any human intervention; the device could simply alert staff when a patient’s walking pattern is indicative of a bigger problem.

We see some great health research here at Hackaday – like this open source ECG. Video after the break.

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Transcranial Electrical Stimulation With Arduino, Hot Glue

The advance of electronic technology has been closely followed by the medical community over the past 200 years. Cutting edge electronics are used in medical imaging solutions to provide ever greater bandwidth and resolution in applications such as MRI machines, and research to interface with the human nervous system continues at a breakneck pace. The cost of this technology – particuarly in research and development – is incredibly high. Combine this with the high price of the regulatory approvals necessary for devices which deal in terms of life and death, and you’ll find that even basic medical technology is prohibitively expensive. Just ask any diabetic. On the face of things, there’s a moral dilemma. Humanity has developed technologies that can improve quality of life. Yet, due to our own rules and regulations, we cannot afford to readily distribute them.

One example of this is that despite the positive results from many transcranial electrical stimulation (TCS) studies, the devices used are prohibitively expensive, as are treatment regimens for patients. Realising this, [quicksilv3rflash] decided to develop a homebrew, open source transcranial electrical stimualtion device, and published it on Instructables. Yes, that’s the world we’re now living in.

It’s important to publish a warning here: Experimenting with this sort of equipment can easily kill you, fry your brain, or have any number of other awful results. If you don’t have a rock solid understanding of the principles behind seperate grounds, or your soldering is just a little sloppy, you don’t want to go anywhere near this. In particular, this device cannot be powered safely by a wall-wart.

To be honest, we find it difficult to trust any medical device manufactured out of modules sourced from eBay. But as a learning excercise, there is serious value here. Such a project requires mastery of analog design to avoid dangerous currents being passed to the body. The instructions also highlight the importance of rigorously testing the device before ever connecting it to a human body.

The equipment is based around an Arduino Nano receiving commands from a computer over serial, fed by an application written in Python & PyGame. To think, this writer thought he was being bold when he used it to control a remote control car! The Arduino Nano interprets this data and outputs it over SPI to a DAC which outputs a signal which is then amplified and fed to the human brain courtesy of op-amps, boost converters and sponge electrodes. The output of the device is limited to +/-2.1mA by design, in accordance with suggested limits for TCS use.

It should be noted, [quicksilv3rflash] has been experimenting with homebuilt TCS devices for several years now, and has lived to tell the tale. It’s impressive to see a full suite of homebrew, opensource tools being developed in this field. [quicksilv3rflash] reports to have not suffered injuries from the device, and several devices have been shipped to redditors. We’ve only found minimal reports on people receiving these, but nothing on anyone actually using the hardware as intended. If you’ve used one, get in touch in the comments.

It goes without saying – this sort of experimentation is dangerous and the stakes for getting it wrong are ludicrously high. We’ve seen before what happens when medical devices malfunction – things get real ugly, real fast. But hackers will be hackers and if you were wondering if it was possible to build a TCS device for under $100 in parts from eBay, well, yes. Yes it is.

Pulse Oximeter is a Lot of Work

These days we are a little spoiled. There are many sensors you can grab, hook up to your favorite microcontroller, load up some simple library code, and you are in business. When [Raivis] got a MAX30100 pulse oximeter breakout board, he thought it would go like that. It didn’t. He found it takes a lot of processing to get useful results out of the device. Lucky for us he wrote it all down with Arduino code to match.

A pulse oximeter measures both your pulse and the oxygen saturation in your blood. You’ve probably had one of these on your finger or earlobe at the doctor’s office or a hospital. Traditionally, they consist of a red LED and an IR LED. A detector measures how much of each light makes it through and the ratio of those two quantities relates to the amount of oxygen in your blood. We can’t imagine how [Karl Matthes] came up with using red and green light back in 1935, and how [Takuo Aoyagi] (who, along with [Michio Kishi]) figured out the IR and red light part.

The MAX30100 manages to alternate the two LEDs, regulate their brightness, filter line noise out of the readings, and some other tasks. It stores the data in a buffer. The trick is: how do you interpret that buffer? Continue reading “Pulse Oximeter is a Lot of Work”

Recording Functioning Muscles to Rehab Spinal Cord Injury Patients

[Diego Marino] and his colleagues at the Politecnico di Torino (Polytechnic University of Turin, Italy) designed a prototype that allows for patients with motor deficits, such as spinal cord injury (SCI), to do rehabilitation via Functional Electrical Stimulation. They devised a system that records and interprets muscle signals from the physiotherapist and then stimulates specific muscles, in the patient, via an electro-stimulator.

The acquisition system is based on a BITalino board that records the Surface Electromyography (sEMG) signal from the muscles of the physiotherapist, while they perform a specific exercise designed for the patient’s rehabilitation plan. The BITalino uses Bluetooth to send the data to a PC where the data is properly crunched in Matlab in order to recognize and to isolate the muscular activity patterns.

After that, the signals are ‘replayed’ on the patient using a relay-board connected to a Globus Genesy 600 electro-stimulator. This relay board hack is mostly because the Globus Genesy is not programmable so this was a fast way for them to implement the stimulator. In their video we can see the muscle activation being replayed immediately after the ‘physiotherapist’ performs the movement. It’s clearly a prototype but it does show promising results.

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