Tag Archives: Featured

Noninvasive sensor tattoo detects glucose levels


UC San Diego professor Joseph Wang has developed an ultra-thin, flexible device that sticks to skin like a tattoo and can detect glucose levels.  The sensor  has the potential to eliminate finger-pricking for diabetes.

The wearable, non-irritating sensor tattoo can detect glucose in the fluid just under the skin.  It is based on integrating glucose extraction and electrochemical biosensing.  Testing on seven volunteers showed  that it was able to accurately determine glucose levels. The sensor response correlated with that of a commercial glucose monitor.

Noninvasive monitoring will be one of the disruptive innovations discussed at  Wearable Tech + Digital Health NYC 2015:  The health sensor revoltion on June 30, 2015 at the New York Academy of Sciences.

Physiological and mathematical models simulate body systems


Another CES standout was LifeQ, a company that combines physiological and bio-mathematical modeling to provide health decision data.

LifeQ Lens is a multi-wavelength optical sensor that can be integrated into wearable devices. It monitors key metrics, with what the company claims to be laboratory level accuracy, using a proprietary algorithm. Raw data is translated through bio-mathematical models, called LifeQ Core. The models are turned into digital, virtual simulations of body systems. LifeQ Link is an open access platform through which partners can use the technology.

The system can be used by athletes, individuals monitoring nutrition, stress and sleep, doctors seeking data to help inform diagnoses  and manage chronic conditions.

The company foresees their data providing population level  health analysis for research purposes. They hope to be able to monitor clinical trials to help create safer medicines and more effective treatments.

Wearables, sensables, and opportunities at CES


It was the year of Digital Health and Wearable Tech at CES.  Endless watches tracked vital signs (and many athletes exercised tirelessly to prove the point).   New were several ear based fitness monitors (Brag), and some interesting TENS pain relief wearables (Quell).  Many companies provided  monitoring for senior citizens, and the most interesting only notified caregivers when there was a change in learned behavior (GreenPeak).  Senior companion robots were missing, although robots capable of household tasks were present (Oshbot).  3D printing was big (printed Pizza)–but where were 3d printed bones and organs?  Augmented reality was popular (APX, Augmenta)–but mostly for gaming or industrial use.  AR for health is next.

Two companies continue to stand out in Digital Health.  Samsung’s Simband  is best positioned to take wearables into  medical monitoring, with its multitude of sensors, open platform, and truly advanced health technologies.  And  MC10‘s electronics that bend, stretch, and flex will disrupt home diagnosis, remote monitoring, and smart medical devices.

We see two immediate opportunities.  The brain, and the pulse.

1.  A few companies at CES claimed to monitor brain activity, and one savvy brand (Muse) provided earphones with soothing sounds while a headband monitored attention.  While these gadgets were fun to try, noone at CES presented extensive brain state interpretation to address cognitive and emotional issues.

2.  Every athlete at CES used a traditional finger based pulse sensor.  A slick wearable that can forgo the finger piece will make pulse oximetry during sports fun, instead of awkward.  As with every gadget, ensuring accuracy is key, as blind faith in wearables can be dangerous.

ApplySci looks forward to CES 2016, and the many breakthroughs to be discovered along the way, many of which will be featured at Wearable Tech + Digital Health NYC 2015.

Implant stimulates vagus nerve, relieves arthritis pain


Academic Medical Center scientists have implanted tiny pacemaker-like devices in the necks of 20 patients with severe rheumatoid arthritis,  reducing joint pain with out drugs.  The trial was led by Professor Paul-Peter Tak.

The implant stimulates the vagus nerve, which connects the brain to major organs, and is responsible for automatic body functions. Spleen activity was reduced after impulses were fired for three minutes a day.  In less than a week, participant’s spleens produced fewer chemicals and other immune cells that cause abnormal joint inflammation in rheumatoid arthritis.

GlaxoSmithKline, a partner in the study, hopes that the same technique could, in the future, reverse other chronic conditions, including asthma, obesity and diabetes.

Samsung moves from fitness tracking to health monitoring


Samsung’s Simband represents the company’s shift from fitness tracking to health monitoring, allowing medical startups and researchers to develop sensor applications.

Simband is equipped with six sensors: electrocardiogram, photoplethysmogram, galvanic skin response, accelerometer, and thermometer.  Developers can also add proprietary sensors.

The wearable’s three main functions are called “trends,” “monitor,” and “spot check.” Trends displays one’s data over time, monitor is a real-time, all sensor tracking mode, and spot check quickly checks heart rate and blood pressure.


Reversing time improves cancer tissue imaging


Washington University professor Lihong Wang has developed a time-reversal technology that allows researchers to better focus light in tissue.  The photo acoustic imaging combines light with acoustic waves to form a sharper image, several centimeters into the skin.  Current high-resolution optical imaging technology allows researchers to see only 1 millimeter deep.

The time-reversed adapted-perturbation (TRAP) optical focusing  sends guiding light into tissue to seek movement. The light that has traversed stationary tissue appears differently than light that has moved through something moving, such as blood. By taking two successive images, they can subtract the light through stationary tissue, retaining only the scattered light due to motion. The light is then sent back to its original source via a time-reversal process, which improves its focus.

Contactless sleep and fatigue sensor


Entering the digital health market, Nintendo is developing a contactless device to track a user’s sleep and monitor fatigue.  It is based on a non-contact radio frequency sensor which measures breathing, heartbeat and body movement.

The company describes the system as five “Non” Sensing elements:

 1.  “Non-wearable.” Nothing is attached to the body.

2.  “Non-contact.” The product will not have any physical contact with the body.

 3.  “Non-operating.” Not requiring the user to operate the device.

4.  “Non-waiting.” Eliminating the wait for measurement results to be produced.

5.  “Non-installation efforts.” Not requiring users to install the product to start.

The nightstand based “QOL sensor” receives the data and  sends it to Nintendo’s cloud servers.  It is analyzed and sent to the user’s smartphone, tablet, Nintendo gaming system or other device. The software advises the user on ways to reduce fatigue.

Brain network map may improve non-invasive stimulation


Brain stimulation treatments can alter neural circuits electrically instead of chemically.  However, understanding what brain regions should be targeted, by condition, remains a challenge, particularly in non-invasive rTMS.  A Beth Israel Deaconess study suggests that brain networks – the interconnected pathways that link brain circuits to one another– can help guide site selection for brain stimulation therapies.

According to author Michael Fox, “Although different types of brain stimulation are currently applied in different locations, we found that the targets used to treat the same disease are nodes in the same connected brain network.”

Brain stimulation treatment data for 14 conditions, including addiction, Alzheimer’s, depression, dystonia, epilepsy, essential tremor, Huntington’s, and Parkinson’s were studied. The researchers listed the stimulation sites, deep in the brain and near the surface, thought to be effective for the treatment of each disease.

Through a data set of fMRI images of people’s brains at rest, the team  found correlated fluctuations in spontaneous brain activity, illustrating which sites were functionally connected.  A map of connections from deep brain stimulation sites to the surface of the brain was created. When the research team compared the map to sites on the brain surface that work for noninvasive brain stimulation, the two matched.

EEG could lead to earlier autism diagnosis


Albert Einstein College of Medicine professor Sophie Molholm has published a paper describing the way that autistic children process sensory information, as determined by EEG.  She believes that this could lead to earlier diagnosis (before symptoms of social and developmental delays emerge), hence earlier treatment, which might reduce the condition’s symptoms.

EEG readings were taken from 40 children, ages 6-17, who were diagnosed with autism,  and compared to those of unaffected children of similar age.  All were given either a flash cue, a beep cue or a combination, and asked to press a button when these stimuli occurred.  A 70 electrode cap measured brain responses every two milliseconds, including those that recorded how the brain first processed the information.

The children with autism showed a distinctly different brain wave signature from those without the condition.  There were differences in the speed in which the sights or sounds were processed, and in how the sensory neurons recruited neurons in other areas of the brain to register and understand the information. The more different this multi-processing was, the more severe the child’s autistic symptoms.

Professor Molholm acknowledges that the sample was too small to use the profile for diagnosing autism, but it could lead to such a test if the results are confirmed and repeated.

Google Fit platform aggregates health data


Using a single set of APIs, Google Fit collects and aggregates data from fitness apps and sensors to manage a user’s fitness stream.

The platform will work with wearables and other peripherals.  To protect privacy, permission is required and data can be deleted.  Initally, Adidas,  Nike , Intel, LG and Motorola will participate.  Nike will add its Fuel number to the Fit stream for other apps to utilize.

Apple announced a similar fitness data aggregation platform, Healthkit,  earlier this month. (See ApplySci, June 5 2014.)  Both platforms are expected to go live this fall.