PsyLink An Open Source Neural Interface For Non-Invasive EMG

We don’t see many EMG (electromyography) projects, despite how cool the applications can be. This may be because of technical difficulties with seeing the tiny muscular electrical signals amongst the noise, it could be the difficulty of interpreting any signal you do find. Regardless, [hut] has been striving forwards with a stream of prototypes, culminating in the aptly named ‘Prototype 8’

The current prototype uses a main power board hosting an Arduino Nano 33 BLE Sense, as well as a boost converter to pump up the AAA battery to provide 5 volts for the Arduino and a selection of connected EMG amplifier units. The EMG sensor is based around the INA128 instrumentation amplifier, in a pretty straightforward configuration. The EMG samples along with data from the IMU on the Nano 33 BLE Sense, are passed along to a connected PC via Bluetooth, running the PsyLink software stack. This is based on Python, using the BLE-GATT library for BT comms, PynPut handing the PC input devices (to emit keyboard and mouse events) and tensorflow for the machine learning side of things. The idea is to use machine learning from the EMG data to associate with a specific user interface event (such as a keypress) and with a little training, be able to play games on the PC with just hand/arm gestures. IMU data are used to augment this, but in this demo, that’s not totally clear.

An earlier prototype of the PsyLink.

All hardware and software can be found on the project codeberg page, which did make us double-take as to why GnuRadio was being used, but thinking about it, it’s really good for signal processing and visualization. What a good idea!

Obviously there are many other use cases for such a EMG controlled input device, but who doesn’t want to play Mario Kart, you know, for science?

Checkout the demo video (embedded below) and you can see for yourself, just be aware that this is streaming from peertube, so the video might be a little choppy depending on your local peers. Finally, if Mastodon is your cup of tea, here’s the link for that. Earlier projects have attempted to dip into EMG before, like this Bioamp board from Upside Down Labs. Also we dug out an earlier tutorial on the subject by our own [Bil Herd.]

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DIY Glasses Aim To Improve Color Vision

Typically, to improve one’s eyesight, we look to tools like corrective lenses or laser eye surgery to improve optical performance. However, [Casey Connor 2] came across another method, that uses light exposure to improve color vision, and set about trying to achieve the same results at home. 

A recent study published in Nature showed that a single exposure to 670 nm light for 3 minutes lead to an improvement in color perception lasting up to a week. The causative method is that cones in the eye get worse at producing ATP as we age, and with less of this crucial molecule supplying energy to cells in the eye, our colour perception declines. Exposure to 670 nm light seems to cause mitochondria in the eye to produce more ATP in a rather complicated physical interaction.

For [Casey’s] build, LEDs were used to produce the required 670 nm red light, installed into ping pong balls that were glued onto a pair of sunglasses. After calculating the right exposure level and blasting light into the eyes regularly each morning, [Casey] plans on running a chromaticity test in the evenings with a custom Python script to measure color perception.

[Casey] shows a proper understanding of the scientific process, and has accounted for the cheap monitor and equipment used in the testing. The expectation is that it should be possible to show a relative positive or negative drift, even if the results may not be directly comparable to industry-grade measures.

We’re eager to see the results of [Casey]’s testing, and might even be tempted to replicate the experiment if it proves successful. We’ve explored some ocular topics in the past too, like the technology that goes into eyeglasses. Video after the break.

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flow chart for Assessment of the Feasibility of Using Noninvasive Wearable Biometric Monitoring Sensors to Detect Influenza and the Common Cold Before Symptom Onset paper

Wearables Can Detect The Flu? Well…Maybe…

Surprisingly there are no pre-symptomatic screening methods for the common cold or the flu, allowing these viruses to spread unbeknownst to the infected. However, if we could detect when infected people will get sick even before they were showing symptoms, we could do a lot more to contain the flu or common cold and possibly save lives. Well, that’s what this group of researchers in this highly collaborative study set out to accomplish using data from wearable devices.

Participants of the study were given an E4 wristband, a research-grade wearable that measures heart rate, skin temperature, electrodermal activity, and movement. They then wore the E4 before and after inoculation of either influenza or rhinovirus. The researchers used 25 binary, random forest classification models to predict whether or not participants were infected based on the physiological data reported by the E4 sensor. Their results are pretty lengthy, so I’ll only highlight a few major discussion points. In one particular analysis, they found that at 36 hours after inoculation their model had an accuracy of 89% with a 100% sensitivity and a 67% specificity. Those aren’t exactly world-shaking numbers, but something the researchers thought was pretty promising nonetheless.

One major consideration for the accuracy of their model is the quality of the data reported by the wearable. Namely, if the data reported by the wearable isn’t reliable itself, no model derived from such data can be trustworthy either. We’ve discussed those points here at Hackaday before. Another major consideration is the lack of a control group. You definitely need to know if the model is simply tagging everyone as “infected” (which specificity does give us an idea of, to be fair) and a control group of participants who have not been inoculated with either virus would be one possible way to answer that question. Fortunately, the researchers admit this limitation of their work and we hope they will remedy this in future studies.

Studies like this are becoming increasingly common and the ongoing pandemic has motivated these physiological monitoring studies even further. It seems like wearables are here to stay as the academic research involving these devices seems to intensify each day. We’d love to see what kind of data could be obtained by a community-developed device, as we’ve seen some pretty impressive DIY biosensor projects over the years.

Comfortable, wearable packaging for biometric device for monitoring physiological data and pushing the data to the cloud

A DIY Biometric Device With Some Security Considerations

Biohacking projects are not new to Hackaday and it’s certainly a genre that really piques our interest. Our latest biohacking device comes courtesy of [Manivannan] who brings his flavor of a wearable biosensor with some security elements built-in through AWS.

The hardware is composed of some impressive components we have seen. He has an AD8232 electrocardiogram front end, the MAX30102 integrated pulse oximeter IC for determining blood oxygen and heart rate, and the ever-popular LM35 for measuring body temperature. Either of these chips would be perfect for your next DIY biosensor project though you might try the MAX30205 body temperature sensor given its 0.1-degree Celsius accuracy. However, what really piqued our interest was the use of Microchip’s AVR-IoT WA Development Board. Now we’ve talked about this board before and also mentioned you could probably do all the same things with an ESP-device, but perhaps now we get to see the board a bit more in action.

[Manivannan] walks the reader through the board’s setup and everything looks to be pretty straightforward. He ultimately rigged together a very primitive dashboard for viewing all his vitals in real-time, demonstrating how you could put together your own patient dashboard for remote monitoring of vitals or other sensor signals. He emphasizes that all this is powered through AWS, giving him some added security layers that are critical for protecting his data from unwanted viewers.

Though [Manivannan’s] security implementation doesn’t rise to the standard of medical devices, maybe it will serve as a case study in the growing open-source medical device movement.

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3D Printing Delivers “Glass” Eyes In Record Time

Obviously, losing an eye would be bad for your vision. But if you think about it, it is also a detriment to your appearance. You might not need a prosthetic eye, and you can certainly rock an eye patch, but a lot of people with this problem get an artificial or “glass” eye. These glass eyes are hand-painted disks that fit into the eye socket. However, a British man now has a new kind of eye prosthesis that is 3D printed, a technology that can potentially cut waiting time for patients in half.

The existing process is lengthy because it requires taking a mold of the eye socket and manually matching the remaining eye with the new artificial eye. With the 3D printed technology, scans of the eye socket and the other eye make this process much simpler.

Moorfields Eye Hospital, the source of the eye, says that a conventional eye takes about six weeks, but the new ones take no more than three weeks. The patient only needs to spend about a half-hour doing the scans before the wait starts. We presume it can be made for less cost, as well.

Medicine is embracing 3D printing and we’ve seen a 3D ear. We are waiting for our personal exoskeleton. Some of the medical 3D printing we’ve seen is for the birds.

E4 Empatica device for measuring location, temperature, skin conductance, sleep, etc. on arm

Wearable Sensor For Detecting Substance Use Disorder

Oftentimes, the feature set for our typical fitness-focused wearables feels a bit empty. Push notifications on your wrist? OK, fine. Counting your steps? Sure, why not. But how useful are those capabilities anyway? Well, what if wearables could be used for a more dignified purpose like helping people in recovery from substance use disorder (SUD)? That’s what the researchers at the University of Massachusetts Medical School aimed to find out.

In their paper, they used a wrist-worn wearable to measure locomotion, heart rate, skin temperature, and electrodermal activity of 38 SUD patients during their everyday lives. They wanted to detect periods of stress and craving, as these parameters are possible triggers of substance use. Furthermore, they had patients self-report times during the day when they felt stressed or had cravings, and used those reports to calibrate their model.

They tried a number of classification models such as decision trees, discriminant analysis, logistic regression, and others, but found the most success using support vector machines though they failed to discuss why they thought that was the case. In the end, they found that they could detect stress vs. non-stress with an accuracy of 81.3% and craving vs. no-craving with an accuracy of 82.1%. Not amazing accuracy, but given the dire need for medical advancements for SUD, it’s something to keep an eye on. Interestingly enough, they found that locomotion data alone had an accuracy of approximately 75% when it came to indicating stress and cravings.

Much ado has been made about the insufficient accuracy of wearable devices for medical diagnoses, particularly of those that measure activity and heart rate. Maybe their model would perform better, being trained on real-time measurements of cortisol, a more accurate physiological measure of stress.

Finally, what really stood out to us about this study was how willing patients were to use a wearable in their treatment strategy. It’s sad that society oftentimes has a very negative perception of SUD patients, leading to fewer treatment options for patients. But hopefully, with technological advancements such as this, we’re one step closer to a more equitable future of healthcare.

Mixing synthetic blood

The Challenges Of Finding A Substitute For Human Blood

Throughout history, the human body has been the subject of endless scrutiny and wonder. Many puzzled over the function of all these organs and fluids found inside. This included the purpose of blood, which saw itself alternately disregarded as being merely for ‘cooling the body’, to being responsible for regulating the body’s humors, leading to the practice of bloodletting and other questionable remedies. As medical science progressed, however, we came to quite a different perspective.

Simply put, our circulatory system and the blood inside it, is what allows us large, multi-celled organisms to exist. It carries oxygen and nutrients to cells, while enabling the removal of waste products as well as an easy path for the cells that make up our immune system. Our blood and the tissues involved with it are crucial to a healthy existence. This is something which becomes painfully clear when we talk about injuries and surgeries that involve severe blood loss.

While the practice of blood transfusions from donated blood has made a tremendous difference here, it’s not always easy to keep every single type of blood stocked, especially not in remote hospitals, in an ambulance, or in the midst of a war zone. Here the use of artificial blood — free from complicated storage requirements and the need to balance blood types — could be revolutionary and save countless lives, including those whose religion forbids the transfusion of human blood.

Although a lot of progress has been made in this field, with a limited number of practical products, it’s nevertheless proving to be a challenge to hit upon a replacement that ticks all of the boxes needed to make it generic and safe.

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