There may be no place on Earth less visited by humans than the South Pole. Despite a permanent research base with buildings clustered about the pole and active scientific programs, comparatively few people have made the arduous journey there. From October to February, up to 200 people may be stationed at the Amundsen-Scott South Pole Station for the Antarctic summer, and tourists checking an item off their bucket lists come and go. But by March, when the sun dips below the horizon for the next six months, almost everyone has cleared out, except for a couple of dozen “winter-overs” who settle in to maintain the station, carry on research, and survive the worst weather Mother Nature brews up anywhere on the planet.
To be a winter-over means accepting the fact that whatever happens, once that last plane leaves, you’re on your own for eight months. Such isolation and self-reliance require special people, and Dr. Jerri Nielsen was one who took the challenge. But as she and the other winter-overs watched the last plane leave the Pole in 1998 and prepared for the ritual first-night screening of John Carpenter’s The Thing, she had no way of knowing what she would have to do to survive the cancer that was even then growing inside her.
Few days are worse than a day when you hear the words, “I’m sorry, you have cancer.” Fear of the unknown, fear of pain, and fear of death all attend the moment when you learn the news, and nothing can prepare you for the shock of learning that your body has betrayed you. It can be difficult to know there’s something growing inside you that shouldn’t be there, and the urge to get it out can be overwhelming.
Sometimes there are surgical options, other times not. But eradicating the tumor is not always the job of a surgeon. Up to 60% of cancer patients will be candidates for some sort of radiation therapy, often in concert with surgery and chemotherapy. Radiation therapy can be confusing to some people — after all, doesn’t radiation cause cancer? But modern radiation therapy is a remarkably precise process that can selectively kill tumor cells while leaving normal tissue unharmed, and the machines we’ve built to accomplish the job are fascinating tools that combine biology and engineering to help people deal with a dreaded diagnosis.
Picture this: you’re at home and you hear a rapping on your door. At last!– your parcel has arrived. You open the door, snatch a drone out of the air, fold it up, remove your package, unfold it and set it down only for it to take off on its merry way. Hand-delivery courier drones might be just over the horizon.
Designed in the [Laboratory of Intelligent Systems] at Switzerland’s École Polytechnique Fédérale de Lausanne and funded by [NCCR Robotics], this delivery drone comes equipped with its own collapsible carbon fibre shield — it fold up small enough to fit in a backpack — and is able to carry packages such as letters, small parcels, and first aid supplies up to 500 g and to 2 km away!
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.
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.
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.
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.