Categories
Sensors

Wang’s fingertip sweat sensor detects glucose levels

UCSD’s Joe Wang has developed a totally noninvasive sensor and algorithm to detect glucose levels from sweat on the fingertip. The painless, rapid, and accurate system could revolutionize diabetes management. The systemcombines a simple touch-based fingertip sweat electrochemical sensor with a new algorithm that addresses for personal variations toward the accurate estimate of blood glucose concentrations. It leverages the fast sweat rate on the fingertip for rapid assays of natural perspiration, without any sweat stimulation, along with the personalized sweat-response-to-blood concentration translation. A reliable estimate of the blood glucose sensing concentrations can thus be realized through a simple one-time personal precalibration. Such system training leads to a substantially improved accuracy with a Pearson correlation coefficient higher than 0.95, along with an overall mean absolute relative difference of 7.79%, with 100% paired points residing in the A + B region of the Clarke error grid. The speed and simplicity of the touch-based blood-free fingertip sweat assay, and the elimination of periodic blood calibrations, should lead to frequent self-testing of glucose and enhanced patient compliance toward the improved management of diabetes.

Click to view Joe Wang discussing wearable sensors at they 2019 ApplySci conference at Harvard Medical School.

Join ApplySci at the 14th Wearable Tech + Digital Health + Neurotech conference at MIT on April 8, 2022

Categories
BCI Brain

Nurmikko’s Neurograins can enable unprecedented brain signal recording detail, new therapies


Arto Nurmikko and Brown colleagues have developed BCI system which employs a coordinated network of independent, wireless microscale neural sensors, to record and stimulate brain activity. “Neurograins” independently record electrical pulses made by firing neurons and send the signals wirelessly to a central hub, which coordinates and processes the signals.

In a recent Nature paper, the team demonstrated the use of 50 autonomous neurograins to record neural activity in a rodent. They believe that this can enable a future system of unprecedented brain signal recording detail, and new therapies for brain diseases and spine injuries.

According to Nurmikko, “one of the big challenges in the field of brain-computer interfaces is engineering ways of probing as many points in the brain as possible. Up to now, most BCIs have been monolithic devices — a bit like little beds of needles. Our team’s idea was to break up that monolith into tiny sensors that could be distributed across the cerebral cortex. That’s what we’ve been able to demonstrate here.”

48 neurograins were placed on a rodent’s cerebral cortex, and successfully recorded characteristic neural signals associated with spontaneous brain activity. The team also tested the devices’ ability to stimulate the brain with electrical pulses.

The size of the animal’s brain limited the team to 48 neurograins for this study, but the data suggest that the current configuration of the system could support up to 770. Ultimately, the team envisions scaling up to many thousands of neurograins, which would provide a currently unattainable picture of brain activity.

Click to view Professor Nurmikko discussing Brain Computer Interfaces at the 2019 ApplySci conference at Harvard Medical School.

June us at the 14th Wearable Tech + Digital Health + Neurotech Boston conference, on April 8, 2022 at MIT.

Categories
Sensors

Patch simultaneously monitors blood pressure, biochemical levels

UCSD’s Joe Wang continues to define the future of vital sign monitoring with a combination of advanced chemistry and unobtrusive wearables. Together with Professor Sheng Xu, he has developed a skin patch that continuously tracks blood pressure and heart rate while measuring glucose levels, lactate, and alcohol or caffeine. It is the first wearable device that monitors cardiovascular signals and multiple biochemical levels simultaneously.

Remote monitoring is obviously increasingly important in the age of COVID. A device that can show the earliest signs of distress, including the onset of sepsis, in those at risk of becoming seriously ill during the pandemic, is significant.

The patch could also be used in hospital ICUs for patients of all ages.

According to Wang, “the novelty here is that we take completely different sensors and merge them together on a single small platform as small as a stamp. We can collect so much information with this one wearable and do so in a non-invasive way, without causing discomfort or interruptions to daily activity.”

Professo Xu described the new patch, saying “Each sensor provides a separate picture of a physical or chemical change. Integrating them all in one wearable patch allows us to stitch those different pictures together to get a more comprehensive overview of what’s going on in our bodies.”.

The patch is capable of measuring three parameters at once, one from each sensor: blood pressure, glucose, and either lactate, alcohol or caffeine. The blood pressure sensor sits near the center of the patch. It consists of a set of small ultrasound transducers that are welded to the patch by a conductive ink. A voltage applied to the transducers causes them to send ultrasound waves into the body. When the ultrasound waves bounce off an artery, the sensor detects the echoes and translates the signals into a blood pressure reading.

The chemical sensors are two electrodes that are screen printed on the patch from conductive ink. The electrode that senses lactate, caffeine and alcohol is printed on the right side of the patch; it works by releasing a drug called pilocarpine into the skin to induce sweat and detecting the chemical substances in the sweat. The other electrode, which senses glucose, is printed on the left side; it works by passing a mild electrical current through the skin to release interstitial fluid and measuring the glucose in that fluid. The researchers were interested in measuring these particular biomarkers because they impact blood pressure.

In tests, subjects wore the patch on the neck while performing various combinations of the following tasks: exercising on a stationary bicycle; eating a high-sugar meal; drinking an alcoholic beverage; and drinking a caffeinated beverage. Measurements from the patch closely matched those collected by commercial monitoring devices such as a blood pressure cuff, blood lactate meter, glucometer and breathalyzer. Measurements of the wearers’ caffeine levels were verified with measurements of sweat samples in the lab spiked with caffeine.

One of the biggest challenges in making the patch was eliminating interference between the sensors’ signals. To do this, the researchers had to figure out the optimal spacing between the blood pressure sensor and the chemical sensors. They found that one centimeter of spacing did the trick while keeping the device as small as possible.

The researchers also had to figure out how to physically shield the chemical sensors from the blood pressure sensor. The latter normally comes equipped with a liquid ultrasound gel in order to produce clear readings. But the chemical sensors are also equipped with their own hydrogels, and the problem is that if any liquid gel from the blood pressure sensor flows out and makes contact with the other gels, it will cause interference between the sensors. So instead, the researchers used a solid ultrasound gel, which they found works as well as the liquid version but without the leakage.

Click to view UCSD video

Categories
AI Covid-19

AI detects COVID in chest x rays

DeepCOVID-XR is a Northwestern University developed algorithm that automatically detects the signs of COVID-19 on a basic X-ray of the lungs. The system is able to detect COVID-19 in X-rays 10 times faster than thoracic radiologists and 1% to 6% more accurately.

The developers said the AI could be used to rapidly screen patients at hospital admission, and trigger protocols to help protect healthcare workers.

Accoring to Professor Aggelos Katsaggelos, the alorithm will not replace testing, but will enable the use of cheap, routine, safe x-rays to determine if a patient needs to be isolated.

In 17,000 X-ray images, the algorithm identified lungs which appeared patchy and hazy, as air sacs became inflamed and filled with fluid instead of air, which is common in COVID-19.

When put up against five experienced, fellowship-trained radiologists, DeepCOVID-XR was able to process a set of 300 test X-rays in about 18 minutes, compared to about two and a half to three and a half hours. The AI also delivered an accuracy rate of 82%, about on par with the group’s range of 76% to 81%.

“Radiologists are expensive and not always available,” Katsaggelos said. “X-rays are inexpensive and already a common element of routine care. This could potentially save money and time—especially because timing is so critical when working with COVID-19.”

Categories
BCI

Algorithm isolates specific brain signals, provides feedback

The US Army and USC Prof Maryam Shanechi have developed an algorithm that can determine which specific behaviors—like walking and breathing—belong to specific brain signals.

Segmenting brain signals has been notoriously difficult, as all signals associated with tasks mix together. Shanechi and her team used the algorithm to separate behaviorally relevant brain signals from behaviorally irrelevant brain signals.

Army research office project manager Hamid Krim said that if the algorithm detects behavior indicating a soldier is stressed or overloaded, then a machine could alert that soldier before they recognize their own fatigue. This may enable the development of technology that can interpret signals from the brain and then send signals back, to automatically correct behavior, or to enable soldiers to communicate with out speaking.

In the civilian world, this could allow locked-in patients to communicate, and those with various disabilities and brain diseases to improve function.

Categories
Sensors

“Ambient intelligence” monitoring to prevent medical errors, send alerts

Stanford’s Fei Fei Li, Arnold Milstein and albert Haque have developed AI and sensor based “ambient intelligence” protocols to prevent medical errors and improve outcomes.

Applications include alerting clinicians and visitors when they fail to sanitize their hands before entering a hospital room; monitoring the elderly for behavioral clues of impending health crises; prompting caregivers, remotely clinicians and patients to make life-saving interventions.

Milstein believes that “we are in a foot race with the complexity of bedside care.” He noted that clinicians in a hospital’s neonatal intensive care unit took 600 bedside actions, per patient, per day and hat ambient intelligence is necessary as “perfect execution of this volume of complex actions is well beyond what is reasonable to expect of even the most conscientious clinical teams.”

The alert systems are being tested to see if they can reduce the number of ICU patients who get nosocomial infections.

In one experiment, a tablet near the door shows a solid green screen that transitions to red when a hygiene failure occurs.

A thermal sensor above an ICU bed would enable the detection of twitching or writhing beneath the sheets, and alert clinical team members.

Constant monitoring by ambient intelligence systems at home could detect clues of serious illness or potential accidents, and alert caregivers to make timely interventions, such as when frail seniors start moving more slowly or stop eating regularly.

Categories
Covid-19 Sensors

Electrostatic and electrochemical sensors rapidly detect airborne viruses

Jang Jae-sung and Ulsan colleagues have developed a method to quickly and accurately detect airborne viruses, to inform public health and quarantine efforts.

Electrostatic force captures and condenses viruses in the air, and a paper electrochemical sensor checks samples for antigens and virus antibodies. Liquid particles as small as 1 micrometer have been successfully collected.

Current methods of airborne sample collection typically use vacuums that can damage the samples, and cannot collect very small particles.

A recent study showed that tests on the type-A H1N1 flu virus showed good results. Jang believes that since the coronavirus is similar in structure and size, the technology should be applicable to COVID-19, which he is researching.

Categories
BCI Brain Sensors

Polymer improves medical implants, could enable brain-computer interface

David Martin and University of Delaware colleagues have developed a bio-synthetic coating for electronic components that could avoid the scarring (and signal disruption) caused by traditional microelectric materials. The PEDOT polymer improved the performance of medical implants by reducing their opposition to an electric current.

Pedot film was used with an antibody to stimulate blood vessel growth after injury, and could be used to detect early stages of tumor growth. The polymers could also help sense or treat brain or nervous system disorders, while versions could theoretically attach peptides, antibodies and DNA.

The team believes that materials, when inserted, could connect brains to a computer.

Categories
Covid-19 Sensors Wearables

Presymptomatic COVID detection with wearables

Stanford’s Michael Snyder and colleagues have used smartwatch data to detect early, presymptomatic COVID-19 in 31 individuals out of a cohort of 5,000.

They demonstrated that COVID-19 infections are associated with alterations in heart rate, steps and sleep in 80% of cases. Physiological alterations were detected prior to, or at, symptom onset in 85% of the positive cases, in some cases nine or more days before symptoms.

A method to detect onset of COVID-19 infection in real-time was developed, which detected 67% of infection cases at or before symptom onset.

The study intends to provide a roadmap to a rapid and universal diagnostic method for the large-scale detection of respiratory viral infections in advance of symptoms.

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Click to view Professor Snyder’s talk at the 2019 ApplySci conference at Stanford.

Categories
Pharmaceuticals Sensors

Sweat sensor monitors drug levels, informs dosage

Sam Emaminejad, Shuyu Lin, Carlos Milla, Ronald Davis and UCLA and Stanford colleagues have developed a watch which monitors drug levels inside the body by analyzing a wearer’s sweat. The goal is individually tailored drug dosages.

Dosages are currently prescribed based on statistical effectiveness averages driven by weight and age. However, constantly changing body chemistry and an ones genetic makeup affect how fast drugs are absorbed, take effect and are eliminated from the body.

Current efforts to personalize the drug dosage rely heavily on repeated blood draws at the hospital. The samples are then sent out to be analyzed in central labs. These solutions are inconvenient, time-consuming, invasive and expensive. That is why they are only performed on a small subset of patients and on rare occasions.

Emaminjegad wanted to “create a wearable technology that can track the profile of medication inside the body continuously and non-invasively” and he seems to have succeeded, using tiny droplets of sweat.

In a recent study, he tracked the effect of acetaminophen, over a period of hours, by stimulating sweat glands with an electric current, and accurately detecting the drug’s unique electrochemical signal, against the backdrop of signals from many other molecules that may be circulating in the body and in higher concentrations than the drug.

The technology can personalize pharmacotherapy approaches, and, according to Emaminejad also be used to monitor medication adherence and drug abuse.

Categories
Brain Sensors

Sensor platform detects dopamine in sweat; could be used for future treatment

Penn State’s Aida Ebrahimi and Maurico Terrones, RPI’s Humberto Terrones, and colleagues, have developed a highly sensitive, non-invasive wearable Dopamine sensor platform. The goal is the use of the technology to develop wearable sensors able to track and eventually treat conditions caused by too much (ie schizophrenia) or too little (ie Parkinson’s, depression) dopamine.

The low cost, flexible detector was achieved by doping a Molybdenum disulfide with Manganes, embedded in two-dimensional transition metal dichalcogenide.

Current dopamine monitoring methods are invasive and require specialized lab equipment. The researchers described the new method as “very simple and scalable.”

Categories
Covid-19 Sensors

First nutrition monitoring wearable tracks vitamin C; could be useful in COVID treatment

UCSD’s Joe Wang has again disrupted chemical sensing, by creating a wearable vitamin C sensor, which is a departure from now common vital sign and activity sensing wearables. This is the first time a wearable has been used to track nutritional intake, a key component of general health and disease prevention.

Vitamin C cannot be synthesized by the human body and must be obtained through food or supplements. It supports immune health, collagen production, wound healing and may be useful in treating cancer, heart disease, and COVID-19. High doses have been linked to reduced mortality in COVID patients with Sepsis and/or ARDS in studies.

The adhesive patch, applied to skin, stimulates sweating, and quickly detects vitamin c levels using flexible electrodes containing the enzyme ascorbate oxidase. When vitamin C is present, the enzyme converts it to dehydroascrobic acid. The resulting consumption of oxygen generates a current that is measured by the device. 

Click to view Prof Wang’s (brilliant) talk at the recent ApplySci conference at Harvard Medical School.