Sensor tracks cerebral aneurysm hemodynamics

Georgia Tech’s Woon-Hong Yeo has developed a 3D-printed, stretchable, battery-free, wireless sensor, implanted in brain blood vessels to measure incoming blood flow, to evaluate aneurysm healing.  The tiny device wraps around stents or diverters implanted to control blood flow in affected vessels. It is believed to be the first demonstration of aerosol jet 3D printing to produce an implantable, stretchable sensing system for wireless monitoring.

Inserted using a catheter, the sensor uses inductive coupling of signals to allow wireless detection of biomimetic cerebral aneurysm hemodynamics.

Current cerebral aneurysms monitoring requires repeated angiogram imaging with potentially harmful contrast materials. Cost and potential negative effects limit the use of these techniques.  A sensor placed in a blood vessel could allow more frequent evaluations without the use of imaging dyes.


REGISTRATION RATES INCREASE SEPTEMBER 20 | Join ApplySci at the 12th Wearable Tech + Digital Health + Neurotech Boston conference on November 14, 2019 at Harvard Medical School featuring talks by Brad Ringeisen, DARPA – Joe Wang, UCSD – Carlos Pena, FDA  – George Church, Harvard – Diane Chan, MIT – Giovanni Traverso, Harvard | Brigham & Womens – Anupam Goel, UnitedHealthcare  – Nathan Intrator, Tel Aviv University | Neurosteer – Arto Nurmikko, Brown – Constance Lehman, Harvard | MGH – Mikael Eliasson, Roche

Sensors + algorithm quantify, analyze body movement to optimize exercise/treatment

FIGUR8 has commercialized MGH/MIT Media Lab research to create a sensor and algorithm to measure and track body movement as a biomarker. The wearable creates a movement baseline, diagnoses dysfunction, identifies injury risk, optimizes exercise routines and tracks progress.

During movement, the sensor quantifies muscle activity and joint mobility, and the algorithm analyzes more than a million data points to determine biomechanical health.  This is cheaper and potentially more effective than current MRI and x ray screening for soft tissue issues.

Athletes and physical therapists are using the system, which the company hopes will also be used in clinical Parkinson’s studies, and stroke diagnostics and recovery.


REGISTRATION RATES INCREASE AUGUST 30 | Join ApplySci at the 12th Wearable Tech + Digital Health + Neurotech Boston conference on November 14, 2019 at Harvard Medical School featuring talks by Brad Ringeisen, DARPA – Joe Wang, UCSD – Carlos Pena, FDA  – George Church, Harvard – Diane Chan, MIT – Giovanni Traverso, Harvard | Brigham & Womens – Anupam Goel, UnitedHealthcare  – Nathan Intrator, Tel Aviv University | Neurosteer – Arto Nurmikko, Brown – Constance Lehman, Harvard | MGH – Mikael Eliasson, Roche – David Rhew, Microsoft

Join ApplySci at the 13th Wearable Tech + Neurotech + Digital Health Silicon Valley conference on February 11-12, 2020 at Stanford University featuring talks by Zhenan Bao, Stanford – Rudy Tanzi, Harvard – David Rhew, Microsoft – Carla Pugh, Stanford – Nathan Intrator, Tel Aviv University | Neurosteer

Printed stickers, stretchable antennas, fluctuation-resistant RFID for continuous whole-body monitoring

Zhenan Bao‘s adhesive, unobtrusive wearables continue to change the way health is monitored.  Her new BodyNet system tracks pulse, respiration, and other physiological signs using small, screen printed stickers around the body, and a wireless receiver clipped to clothing. The research was published in Nature Electronics last week.

Her  goal is to “create an array of wireless sensors that stick to the skin and work in conjunction with smart clothing to more accurately track a wider variety of health indicators than the smart phones or watches consumers use today.”

The technology is almost un-noticeable to the wearer, as it does not include batteries or rigid circuits. To achieve this, the Bao Lab created a new antenna that could stretch and bend like skin, and an RFID system capable of sending strong and accurate signals to the receiver, despite constant fluctuations.

The initial version of the stickers relied on tiny motion sensors. The team will next integrate sweat, temperature and other sensors.

Bao believes that “one day it will be possible to create a full-body skin-sensor array to collect physiological data without interfering with a person’s normal behavior.”


AI detects brain aneurysms, predicts rupture risk in surgery

Fujitsu, GE Healthcare, Macquarie University and Macquarie Medical Imaging are using AI to detect and monitor brain aneurysms on scans faster and more efficiently. Fujitsu will use AI to analyze brain images generated by GE’s Revolution C scanner and an algorithm that detect abnormalities and aneurysms. The algorithm will be capable of highlighting an arterial ring at the base of the brain that can have one or more aneurysms, and the tech will track aneurysms over time.The next phase will include a planning tool for surgical stent intervention. Fluid dynamic modeling will be used to predict the risk of aneurysm rupture.

Join ApplySci at the 12th Wearable Tech + Digital Health + Neurotech Boston conference on November 14, 2019 at Harvard Medical School featuring talks by Brad Ringeisen, DARPA – Joe Wang, UCSD – Carlos Pena, FDA  – George Church, Harvard – Diane Chan, MIT – Giovanni Traverso, Harvard | Brigham & Womens – Anupam Goel, UnitedHealthcare  – Nathan Intrator, Tel Aviv University | Neurosteer – Arto Nurmikko, Brown – Constance Lehman, Harvard | MGH – Mikael Eliasson, Roche – David Rhew, Microsoft

Join ApplySci at the 13th Wearable Tech + Neurotech + Digital Health Silicon Valley conference on February 11-12, 2020 at Stanford University featuring talks by Zhenan Bao, Stanford – Rudy Tanzi, Harvard – David Rhew, Microsoft – Carla Pugh, Stanford – Nathan Intrator, Tel Aviv University | Neurosteer

Cuffless blood pressure measurement via selfie video

Kang Lee, Zong Ping Feng, Paul Zheng, and University of Toronto colleagues are measuring blood pressure using a selfie video and transdermal optical imaging technology.

Because of its translucency, phone optical sensors can capture red light reflected from hemoglobin under skin, allowing TOI to visualize and measure blood flow changes.

In a study, two-minute iPhone selfie videos of 1,328 adults were analyzed. Three types of blood pressure  were measured with 95 percent accuracy. Pre-recorded videos can also be used.

The technology is being commercialized through Nuralogix, a company founded by Lee and Zheng, which is already measuring resting heart rate using videos.

Click to view University of Toronto video.


Join ApplySci at the 12th Wearable Tech + Digital Health + Neurotech Boston conference on November 14, 2019 at Harvard Medical School featuring talks by Brad Ringeisen, DARPA – Joe Wang, UCSD – Carlos Pena, FDA  – George Church, Harvard – Diane Chan, MIT – Giovanni Traverso, Harvard | Brigham & Womens – Anupam Goel, UnitedHealthcare  – Nathan Intrator, Tel Aviv University | Neurosteer – Arto Nurmikko, Brown – Constance Lehman, Harvard | MGH – Mikael Eliasson, Roche – David Rhew, Samsung

Join ApplySci at the 13th Wearable Tech + Neurotech + Digital Health Silicon Valley conference on February 11-12, 2020 at Stanford University featuring talks by Zhenan Bao, Stanford – Rudy Tanzi, Harvard – David Rhew, Samsung – Carla Pugh, Stanford – Nathan Intrator, Tel Aviv University | Neurosteer

BCI reads whole words from thoughts; no virtual keyboard necessary

Edward Chang at UCSF, Mark Chevillet at Facebook, and colleagues, have published a study where implanted electrodes were used to “read” whole words from thoughts.  Previous technology required users to spell words with a virtual keyboard.

Subjects listened to multiple-choice questions and spoke answers aloud.  An electrode array recorded activity in parts of the brain associated with understanding and producing speech, and sought patterns that matched with words and phrases in real-time.

Participants responded to questions with one of several options while their brain activity was recorded. The system guessed when they were asking a question and when they were answering it, and then the content of both speech events. The predictions were shaped by prior contex. Results were 61 to 76 percent accurate, compared with 7 to 20 percent accuracy expected by chance.

This builds on Facebook technology described by Mark Chevillet at the ApplySci conference at the MIT Media Lab in September, 2017, and could result in the ability for the speech-impaired to freely communicate.


Join ApplySci at the 12th Wearable Tech + Digital Health + Neurotech Boston conference on November 14, 2019 at Harvard Medical School featuring talks by Brad Ringeisen, DARPA – Joe Wang, UCSD – Carlos Pena, FDA  – George Church, Harvard – Diane Chan, MIT – Giovanni Traverso, Harvard | Brigham & Womens – Anupam Goel, UnitedHealthcare  – Nathan Intrator, Tel Aviv University | Neurosteer – Arto Nurmikko, Brown – Constance Lehman, Harvard | MGH – Mikael Eliasson, Roche – David Rhew, Samsung

Join ApplySci at the 13th Wearable Tech + Neurotech + Digital Health Silicon Valley conference on February 11-12, 2020 at Stanford University featuring talks by Zhenan Bao, Stanford – Rudy Tanzi, Harvard – David Rhew, Samsung – Carla Pugh, Stanford – Nathan Intrator, Tel Aviv University | Neurosteer

AIgorithm detects cancer potential of pancreatic cysts

CompCyst is a proof-of-concept study, led by Anne Marie Lennon at Johns Hopkins, which uses AI to more accurately determine which pancreatic cysts will become cancerous. The test evaluates molecular and clinical markers in cyst fluids, and could significantly improve detection rates vs. current clinical and imaging tests.

In the study, the researchers evaluated molecular profiles, including DNA mutations and chromosome changes, of 862 pancreatic cysts. An algorithm developed by David Masica classified patients into the three groups: those with no potential to turn cancerous, for which patients would not require periodic monitoring; mucin-producing cysts that have a small risk of progressing to cancer, for which patients can receive periodic monitoring for progression to possible cancer; and cysts for which surgery is recommended because there is a high likelihood of progression to cancer.

Based on histopathological analysis of surgically resected cysts, the researchers found that surgery was not needed in 45% of patients. This unnecessary surgery was performed because the clinicians could not determine the cysts were dangerous. If CompCyst had been used, the researchers estimated that 60% to 74% of the patients (depending on the cyst type) could have been spared unnecessary intervention.


Join ApplySci at the 12th Wearable Tech + Digital Health + Neurotech Boston conference on November 14, 2019 at Harvard Medical School featuring talks by Brad Ringeisen, DARPA – Joe Wang, UCSD – Carlos Pena, FDA  – George Church, Harvard – Diane Chan, MIT – Giovanni Traverso, Harvard | Brigham & Womens – Anupam Goel, UnitedHealthcare  – Nathan Intrator, Tel Aviv University | Neurosteer – Arto Nurmikko, Brown – Constance Lehman, Harvard | MGH – Mikael Eliasson, Roche – David Rhew, Samsung

Join ApplySci at the 13th Wearable Tech + Neurotech + Digital Health Silicon Valley conference on February 11-12, 2020 at Stanford University featuring talks by Zhenan Bao, Stanford – Rudy Tanzi, Harvard – David Rhew, Samsung – Carla Pugh, Stanford – Nathan Intrator, Tel Aviv University | Neurosteer

Robot helps autistic kids engage

Georgia Tech professor Ayanna Howard is using interactive robots to help autistic kids engage with others,  socially and emotionally. Her company, Zyrobotics, is commericalizing this technology.

In a study, 18 kids, between the ages of 4 and 12,  five of whom had autism, interacted with two robots which expressed 20 emotional states, including boredom, excitement, and nervousness. As children heard, saw, smelled, tasted, and touched in different scenarios, the robots showed them appropriate responses. The results were increased engagement when the robots engaged with the participants in sensory stations.


Join ApplySci at the 12th Wearable Tech + Digital Health + Neurotech Boston conference on November 14, 2019 at Harvard Medical School featuring talks by Brad Ringeisen, DARPA – Joe Wang, UCSD – Carlos Pena, FDA  – George Church, Harvard – Diane Chan, MIT – Giovanni Traverso, Harvard | Brigham & Womens – Anupam Goel, UnitedHealthcare  – Nathan Intrator, Tel Aviv University | Neurosteer – Arto Nurmikko, Brown – Constance Lehman, Harvard | MGH – Mikael Eliasson, Roche – David Rhew, Samsung

Join ApplySci at the 13th Wearable Tech + Neurotech + Digital Health Silicon Valley conference on February 11-12, 2020 at Stanford University featuring talks by Zhenan Bao, Stanford – Rudy Tanzi, Harvard – David Rhew, Samsung – Carla Pugh, Stanford – Nathan Intrator, Tel Aviv University | Neurosteer

Tiny fiber optic sensor monitors blood flow in real-time

John Arkwright and Flinders University colleagues have developed a tiny, low cost, fiber-optic sensor to monitor blood flow through the aorta in real-time.  The goal is continuous monitoring during prolonged intensive care and surgical procedures.  Current blood flow measurement, using ultrasound or thermo-dilution,  is intermittent, averaging every 30 minutes.

The device is inserted through a small  aperture in the skin, into the femoral artery, when heart function is compromised.  Its size allows it to be  used in the tiny blood vessels of infants. Very young babies  are particularly susceptible to sudden drops in blood pressure and oxygen delivery to vital organs.


Join ApplySci at the 12th Wearable Tech + Digital Health + Neurotech Boston conference on November 14, 2019 at Harvard Medical School and the 13th Wearable Tech + Neurotech + Digital Health Silicon Valley conference on February 11-12, 2020 at Stanford University

New electrodes, brain signal analysis, for smaller, lower power, wireless BCI

Building on his prior brain-controlled prosthetic work, Stanford’s Krishna Shenoy has developed a simpler way to study brain electrical activity, which he believes will lead to tiny, low-power, wireless brain sensors that would bring thought-controlled prosthetics into much wider use.

The method involved decoding neural activity in aggregate, instead of  “spike sorting.”  Spike sorting must be done for every neuron in every experiment, taking thousands of research hours.  Future brain sensors, with 1,000 or more electrodes — up from 100 today — would take a neuroscientist 100 hours or more to sort the spikes by hand for every experiment.

In the study, the researchers used a  statistics theory  to uncover patterns of brain activity when several neurons are recorded on a single electrode. An electrode designed to pick up brain signals in mice used the technology to record brain signals of rhesus monkeys. Hundreds of neurons were recorded at the same time, and accurately portrayed the monkey’s brain activity, without spike sorting.

The team believes that this work will ultimately lead to neural implants with simpler electronics, to track more neurons, more accurately than before.


Join ApplySci at the 12th Wearable Tech + Digital Health + Neurotech Boston conference on November 14, 2019 at Harvard Medical School and the 13th Wearable Tech + Neurotech + Digital Health Silicon Valley conference on February 11-12, 2020 at Stanford University