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Wearable system detects postpartum depression via baby/mother interaction

Texas professor Kaya de Barbaro is creating a mother-child wearable system to detect and attempt to prevent postpartum depression. Mother stress levels are measured via heart rythm, and encouraging messages are sent.  Mom wears the sensor on her wrist, and baby wears it on her/his ankle. The child’s sensor collects heart rate and movement data, which is correlated with the mother’s reaction.  Audio is recorded to track crying. Mothers receive messages, including “great job” and “take a breather” when stress is sensed via a faster heart beat, in an attempt to limit feelings of isolation.

Join ApplySci at the 10th Wearable Tech + Digital Health + Neurotech Silicon Valley conference on February 21-22 at Stanford University — Featuring:  Zhenan Bao – Christof Koch – Vinod Khosla – Nathan Intrator – John Mattison – David Eagleman – Unity Stoakes – Shahin Farshchi

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Blood glucose-powered sensor for long term monitoring

Subhanshu Gupta and Washington State colleagues have developed an implantable sensor, powered by  harvested blood glucose, for long term monitoring.

The electronics consume only a few microwatts of power, while being highly sensitive. Combined with the biofuel cells,  the sensor is more efficient than (and non-toxic as compared to) traditional battery-powered devices.  Fueled by body glucose, the electronics can be powered indefinitely.

According to Gupta; “The human body carries a lot of fuel in its bodily fluids through blood glucose or lactate around the skin and mouth. Using a biofuel cell opens the door to using the body as potential fuel.”

Join ApplySci at the 10th Wearable Tech + Digital Health + Neurotech Silicon Valley conference on February 21-22 at Stanford University — Featuring:  Zhenan Bao – Christof Koch – Nathan Intrator – John Mattison – David Eagleman – Unity Stoakes – Shahin Farshchi

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Adhesive emergency response sensors

VitalTag by Pacific Northwest National Laboratory is a chest-worn sticker that detects, monitors and transmits blood pressure, heart rate, respiration rate and other vital signs, eliminating the need for multiple medical devices.

It is meant for emergency responders to quickly assess a person’s state.

Additional sensors are worn on the finger, and in the ear.

Data is displayed in an app, allowing responders to see patients’ location and receive alerts when status changes or they are moved.  Multiple patients can be monitored simultaneously.

Join ApplySci at the 9th Wearable Tech + Digital Health + Neurotech Boston conference on September 24, 2018 at the MIT Media Lab.  Speakers include:  Rudy Tanzi – Mary Lou Jepsen – George Church – Roz Picard – Nathan Intrator – Keith Johnson – John Mattison – Roozbeh Ghaffari – Poppy Crum – Phillip Alvelda –Marom Bikson – Ed Simcox – Sean Lane

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Small ultrasound patch detects heart disease early

Sheng Xu, Brady Huang, and UCSD colleagues have developed a small, wearable ultrasound patch that  monitors blood pressure in arteries up to 4 centimeters under the skin.  It is meant to detect cardiovascular problems earlier, with greater accuracy

Applications include continuous blood pressure monitoring in heart and lung disease, the critically ill, and those undergoing surgery.  It could be used to measure other vital signs, but this was not studied.

The wearable measures central blood pressure, considered more accurate and better at predicting disease than peripheral blood pressure. Central blood pressure is not routinely measured, and involves a catheter inserted into a blood vessel in the arm, groin or neck, and guiding to the heart. A non-invasive method exists, but it does not produce consistently accurate readings.

Join ApplySci at the 9th Wearable Tech + Digital Health + Neurotech Boston conference on September 24, 2018 at the MIT Media Lab.  Speakers include:  Rudy Tanzi – Mary Lou Jepsen – George Church – Roz Picard – Nathan Intrator – Keith Johnson – John Mattison – Roozbeh Ghaffari – Poppy Crum – Phillip Alvelda –Marom Bikson – Ed Simcox – Sean Lane

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Apple watch detects falls, diagnoses heart rhythm, bp irregularities

The Apple Watch has become a serious medical monitor.  It will now be able to detect falls, contact emergency responders, and diagnose  irregularities in heart rhythm and blood pressure.  Its ECG app has been granted a De Novo classification by the FDA.

ECG readings are taken from the wrist, using electrodes built into the Digital Crown and an electrical heart rate sensor in the back crystal. Users touch the Digital Crown and receive a heart rhythm classification in 30 seconds. It can classify if the heart is beating in a normal pattern or whether there are signs of Atrial Fibrillation . All recordings, their associated classifications and any noted symptoms are stored and can be shared with physicians.

The watch intermittently analyzes heart rhythms in the background and sends a notification if an irregular heart rhythm such as AFib is detected.  It can also alert the user if the heart rate exceeds or falls below a specified threshold.

Fall detection is via a built in accelerometer and gyroscope, which measures forces, and an algorithm to identify hard falls. Wrist trajectory and impact acceleration are analyzed to detect falls.  Users are then sent an alert, which can be dismissed or used to call emergency services.  If  immobility  is sensed for 60 seconds,  emergency services will automatically be called, and emergency contacts will be notified.

Join ApplySci at the 9th Wearable Tech + Digital Health + Neurotech Boston conference on September 24, 2018 at the MIT Media Lab.  Speakers include:  Rudy Tanzi – Mary Lou Jepsen – George Church – Roz Picard – Nathan Intrator – Keith Johnson – John Mattison – Roozbeh Ghaffari – Poppy Crum – Phillip Alvelda –Marom Bikson – Ed Simcox – Sean Lane
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Implanted sensors track dopamine for a year

Helen Schwerdt, Ann Graybiel, Michael Cima, Bob Langer, and MIT colleagues have developed and implantable sensor that can measure dopamine in the brain of rodents for more than one year.  They believe that this can inform the treatment and understanding of Parkinson’s and other brain diseases.

According to Graybiel, “Despite all that is known about dopamine as a crucial signaling molecule in the brain, implicated in neurologic and neuropsychiatric conditions as well as our abilty to learn, it has been impossible to monitor changes in the online release of dopamine over time periods long enough to relate these to clinical conditions.”

The sensors arenearly invisible to the immune system, avoiding scar tissue that would impede accuracy. After  implantation, populations of microglia  and astrocytes were the same as those in brain tissue that did not have the probes.

In a recent animal  study, three to five sensors per were implanted 5 millimeters deep in the striatum. Readings were taken every few weeks, after dopamine release was stimulated in the brainstem, traveling to the striatum. Measurements remained consistent for up to 393 days.

If developed for use in humans, these sensors could be useful for monitoring Parkinson’s patients who receive deep brain stimulation.

Join ApplySci at the 9th Wearable Tech + Digital Health + Neurotech Boston conference on September 24, 2018 at the MIT Media Lab.  Speakers include:  Rudy Tanzi – Mary Lou Jepsen – George Church – Roz Picard – Nathan Intrator – Keith Johnson – John Mattison – Roozbeh Ghaffari – Poppy Crum – Phillip Alvelda –Marom Bikson – Ed Simcox – Sean Lane

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DARPA: Three aircraft virtually controlled with brain chip

Building on 2015 research that enabled a paralyzed person to virtually control an F-35 jet, DARPA’s Justin Sanchez has announced that the brain can be used to command and control three types of aircraft simultaneously.

Click to view Justin Sanchez’s talk at ApplySci’s 2018 conference at Stanford University

Join ApplySci at the 9th Wearable Tech + Digital Health + Neurotech Boston conference on September 24, 2018 at the MIT Media Lab.  Speakers include:  Rudy Tanzi – Mary Lou Jepsen – George Church – Roz Picard – Nathan Intrator – Keith Johnson – John Mattison – Roozbeh Ghaffari – Poppy Crum – Phillip Alvelda –Marom Bikson – Ed Simcox – Sean Lane

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VR + motion capture to study movement, sensory processing, in autism, AD, TBI

MoBi, developed by John Foxe at the University of Rochester, combines VR, EEG, and motion capture sensors to study movement difficulties associated with neurological disorders.

According to Foxe, “The MoBI system allows us to get people walking, using their senses, and solving the types of tasks you face every day, all the while measuring brain activity and tracking how the processes associated with cognition and movement interact.”

Motion sensor and EEG data, collected while a subject is walking in a virtual environment, are synchronized, allowing researchers to track which areas of the brain are being activated when walking or performing task. Brain response while moving, performing tasks, or doing both at the same time, is analyzed.

This technique could potentially guide treatment in Autism, dementia, and TBI, characterized by difficulty in processing sensory information from multiple sources and an abnormal gait.

Click to view University of Rochester video

Join ApplySci at the 9th Wearable Tech + Digital Health + Neurotech Boston conference on September 24, 2018 at the MIT Media Lab.  Speakers include:  Rudy Tanzi – Mary Lou Jepsen – George Church – Roz Picard – Nathan Intrator – Keith Johnson – John Mattison – Roozbeh Ghaffari – Poppy Crum – Phillip Alvelda –Marom Bikson – Ed Simcox – Sean Lane

PREFERRED REGISTRATION AVAILABLE THROUGH TODAY, SEPTEMBER 7TH

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Brain imaging to detect suicidal thoughts

Last year, Carnegie Mellon professor Marcel Just and Pitt professor David Brent used brain imagining to identify suicidal thoughts.

Supported by the NIMH, they are now working to establish reliable neurocognitive markers of suicidal ideation and attempt. They will examine the differences in brain activation patterns between suicidal and non-suicidal young adults as they think about words related to suicide — such as positive and negative concepts — and use machine learning to identify neural signatures of suicidal ideation and behavior.

According to Just,  “We were previously able to obtain consistent neural signatures to determine whether someone was thinking about objects like a banana or a hammer by examining their fMRI brain activation patterns. But now we are able to tell whether someone is thinking about ‘trouble’ or ‘death’ in an unusual way. The alterations in the signatures of these concepts are the ‘neurocognitive thought markers’ that our machine learning program looks for.”

Join ApplySci at the 9th Wearable Tech + Digital Health + Neurotech Boston conference on September 24, 2018 at the MIT Media Lab.  Speakers include:  Rudy Tanzi – Mary Lou Jepsen – George Church – Roz Picard – Nathan Intrator – Keith Johnson – John Mattison – Roozbeh Ghaffari – Poppy Crum – Phillip Alvelda –Marom Bikson – Ed Simcox – Sean Lane

PREFERRED REGISTRATION AVAILABLE THROUGH FRIDAY, SEPTEMBER 7TH

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AI predicts response to antipsychotic drugs, could distinguish between disorders

Lawson Health Research Institute, Mind Research Network and Brainnetome Center researchers have developed an algorithm that analyzes brain scans to classify illness in patients with complex mood disorders and help predict their response to medication.

A recent study analyzed and compared fMRI scans of those with MDD, bipolar I,  and no history of mental illness, and found that each group’s brain networks differed, including regions in the default mode network and thalamus.

When tested against participants with a known MDD or Bipolar I diagnosis, the algorithm correctly classified illness with 92.4 per cent accuracy.

The team also imaged the brains of 12 complex mood disorder patients with out a clear diagnosis, to predict diagnosis and examine medication response.

The researchers hypothesized that participants classified by the algorithm as having MDD would respond to antidepressants while those classified as having bipolar I would respond to mood stabilizers. When tested with the complex patients, 11 out of 12 responded to the medication predicted by the algorithm.

According to lead researcher Elizabeth Osuch:: “This study takes a major step towards finding a biomarker of medication response in emerging adults with complex mood disorders. It also suggests that we may one day have an objective measure of psychiatric illness through brain imaging that would make diagnosis faster, more effective and more consistent across health care providers.”

Join ApplySci at the 9th Wearable Tech + Digital Health + Neurotech Boston conference on September 24, 2018 at the MIT Media Lab.  Speakers include:  Rudy Tanzi – Mary Lou Jepsen – George Church – Roz Picard – Nathan Intrator – Keith Johnson – Juan Enriquez – John Mattison – Roozbeh Ghaffari – Poppy Crum – Phillip Alvelda –Marom Bikson – Ed Simcox – Sean Lane