Sensor assesses blood clotting in 30 minutes

ClotChip assesses  blood clotting 95 times faster than current methods, with a single single drop of blood, using miniaturized dielectric spectroscopy.

A finger-prick sample is taken from heart arrhythmia, pulmonary embolism, post surgery, or  hemophilia patients, to analyze clotting abilities in the ER or at home.  Results are received in 30 minutes.

Caregivers currently cannot quickly assess if a patient with coagulation issues is at risk for spontaneous bleeding, or if the drugs they are taking are effective.

The company is completing clinical trials for use in hemophilia and anticoagulation therapy.

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 ChurchRoz PicardNathan IntratorKeith JohnsonJuan EnriquezJohn MattisonRoozbeh GhaffariPoppy Crum – Phillip Alvelda Marom Bikson


Combined BCI + FES system could improve stroke recovery

Jose Millan and EPFL colleagues have combined a brain computer interface with functional electrical stimulation in a system that, in a study, showed the ability to enhance the restoration of limb use after a stroke.

According to Millan: “The key is to stimulate the nerves of the paralyzed arm precisely when the stroke-affected part of the brain activates to move the limb, even if the patient can’t actually carry out the movement. That helps re-establish the link between the two nerve pathways where the signal comes in and goes out.”

27 patients with a similar lesion that resulted in moderate to severe arm paralysis following a stroke participated in the trial. Half were treated with the dual-therapy approach, and reported clinically significant improvements.  A BCI system  enabled the researchers to pinpoint where the electrical activity occurred in the brain when they tried to extend their hands. Each time the electrical activity was identified, the system stimulated the muscle controlling the corresponding wrist and finger movements.

The control group received FES only, and had their arm muscles stimulated randomly. This allowed the scientists to understand how much additional motor function improvement could be attributed to the BCI system.

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 ChurchRoz PicardNathan IntratorKeith JohnsonJuan EnriquezJohn MattisonRoozbeh GhaffariPoppy Crum – Phillip Alvelda Marom Bikson


AI CT analysis speeds stroke identification, treatment‘s algorithms analyze brain scans and immediately transfer data to ensure rapid stroke treatment. The system connects to a hospital CT and sends alerts when a suspected LVO stroke has been identified.  Radiological images are sent to a doctor’s phone.  The company claims that the median time from picture to notification is less than 6 minutes, which can be life-saving, as they also claim that standard stroke workflow is now 66 minutes. Patient transfer to  interventional centers is initiated through messaging and call capabilities connected with emergency and transportation services.

Join ApplySci at the 9th Wearable Tech + Digital Health + Neurotech Boston conference on September 24, 2018 at the MIT Media Lab

VR + neurofeedback for movement training after stroke

Join ApplySci at Wearable Tech + Digital Health + Neurotech Silicon Valley on February 26-27, 2018 at Stanford University, featuring:  Vinod KhoslaJustin SanchezBrian OtisBryan JohnsonZhenan BaoNathan IntratorCarla PughJamshid Ghajar – Mark Kendall – Robert Greenberg

BCI-controlled exoskeleton helps motor recovery in stroke

Ipsihand, developed by Eric Leuthardt and Washington University colleagues, is a brain controlled glove that helps reroute hand control to an undamaged part of the brain.  The system uses a glove or brace on the hand, an EEG cap, and an amplifier.

One’s hands are controlled by the opposite side of the brain. If one hemisphere is damaged, it is difficult to control the other hand.

According to Leuthard, the idea of Ipsihand is that if one can “couple those motor signals that are associated with moving the same-sided limb with the actual movements of the hand, new connections will be made in your brain that allow the uninjured areas of your brain to take over control of the paralyzed hand.”

Ipsihand’s cap detects intention signals to open or close the hand, then the computer amplifies them. The brace then opens or closes in a pincer-like grip with the hand inside, bending the fingers and thumb to meet.

Join ApplySci at Wearable Tech + Digital Health + NeuroTech Boston on September 19, 2017 at the MIT Media Lab – featuring  Joi Ito – Ed Boyden – Roz Picard – George Church – Nathan Intrator –  Tom Insel – John Rogers – Jamshid Ghajar – Phillip Alvelda – Michael Weintraub – Nancy Brown – Steve Kraus – Bill Geary

Robots support neural and physical rehab in stroke, cerebral palsy

Georgia Tech’s  Ayanna Howard has developed Darwin, a socially interactive robot that encourages children to play an active role in physical therapy.

Specific targeting children with cerebral palsy (who are involved in current studies),  autism, or tbi, the robot is designed to function in the home, to supplement services provided by  clinicians.  It engages users as their human therapist would — monitoring performance, and providing motivation and feedback.In a recent experiment, motion trackers monitored user movements as Darwin offered encouragement, and demonstrated movements when they were not performed correctly.  Researchers claimed that wth the exception of one case, the robot’s impact was “significantly positive.

Darwin is still evolving (pun intended) and has not yet been commercialized.

At MIT,  Newman Lab researcher Hermano Igo Krebs has been using robots for gait and balance neurorehabilitation in stroke and cerebral palsy patients since 1989.  Krebs’s technology continues to be incorporated into  Burke Rehabilitation hospital treatment plans.

Join ApplySci at Wearable Tech + Digital Health + NeuroTech Boston – Featuring Roz Picard, Tom Insel, John Rogers and Nathan Intrator – September 19, 2017 at the MIT Media Lab

Self regulating patch optimizes blood thinner delivery

UNC and  NC State researchers have developed a promising, self-regulating, Heparin releasing patch, meant to optimize levels of the blood thinner in one’s body.  It has only been tested on animals, but was found to  be more effective at preventing thrombosis than traditional drug delivery methods.

Current protocol requires regular blood testing, to prevent hemorrhaging from a too-high dose, or, of course, thrombosis from an inadequate one.

The patch uses microneedles made of a polymer that consists of hyaluronic acid and  Heparin. It responds to thrombin, an enzyme that initiates blood clotting. When elevated thrombin levels come into contact with a microneedle, the enzymes break the amino acid chains that bind the Heparin to the HA, releasing the Heparin into the blood stream.

ApplySci’s 6th   Wearable Tech + Digital Health + NeuroTech Silicon Valley  –  February 7-8 2017 @ Stanford   |   Featuring:   Vinod Khosla – Tom Insel – Zhenan Bao – Phillip Alvelda – Nathan Intrator – John Rogers – Roozbeh Ghaffari –Tarun Wadhwa – Eythor Bender – Unity Stoakes – Mounir Zok – Krishna Shenoy – Karl Deisseroth – Shahin Farshchi – Casper de Clercq – Mary Lou Jepsen – Vivek Wadhwa – Dirk Schapeler – Miguel Nicolelis


Robotic hand exoskeleton for stroke patients

ETH professor Roger Gassert has developed a robotic exoskeleton that allows stroke patients  to perform daily activities by supporting motor and somatosensory functions.

His vision is that “instead of performing exercises in an abstract situation at the clinic, patients will be able to integrate them into their daily life at home, supported by a robot.” He observes that existing exoskeletons are heavy, rendering patients unable to lift their hands. They also have difficulty feeling objects and exerting the right amount of force. To address this, the palm of the hand is left free in the new device.

Gassert’s Kyushu University colleague Jumpei Arata developed a mechanism for the finger featuring three overlapping leaf springs. A motor moves the middle spring, which transmits the force to the different segments of the finger through the other two springs. The fingers thus automatically adapt to the shape of the object the patient wants to grasp.

To reduce the weight of the exoskeleton, motors are placed on the patient’s back and  force is transmitted using a bicycle brake cable. ApplySci hopes that the size and weight of the motor can be reduced, allowing it to be integrated into the exoskeleton in its next phase.

Gassert wants to make the exoskeleton thought controlled, and is using MRI and EEG to detect, in the brain,  a patient’s intention to move his or her hand, and communicating this to the device.

ApplySci’s 6th   Wearable Tech + Digital Health + NeuroTech Silicon Valley  –  February 7-8 2017 @ Stanford   |   Featuring:   Vinod Khosla – Tom Insel – Zhenan Bao – Phillip Alvelda – Nathan Intrator – John Rogers – Mary Lou Jepsen – Vivek Wadhwa – Miguel Nicolelis – Roozbeh Ghaffari – Unity Stoakes – Mounir Zok – Krishna Shenoy

Machine learning for faster stroke diagnosis

MedyMatch uses big data and artificial intelligence to improve stroke diagnosis, with the goal of faster treatment.

Patient CT photos are scanned  and immediately compared with hundreds of thousands of other patient results.  Almost any deviation from a normal CT is quickly detected.

With current methods, medical imaging errors can occur when emergency room radiologists miss subtle aspects of brain scans, leading to delayed treatment. Fast detection of stroke can prevent paralysis and death.

The company claims that it can detect irregularities more accurately than a human can. Findings are presented as 3D brain images, enabling a doctor to make better informed decisions. The cloud-based system allows scans to be uploaded from any location.

Wearable Tech + Digital Health San Francisco – April 5, 2016 @ the Mission Bay Conference Center

NeuroTech San Francisco – April 6, 2016 @ the Mission Bay Conference Center

Wearable Tech + Digital Health NYC – June 7, 2016 @ the New York Academy of Sciences

NeuroTech NYC – June 8, 2016 @ the New York Academy of Sciences


Stroke detecting headset prototype

Samsung’s Early Detection Sensor & Algorithm Package (EDSAP), developed by  Se-hoon Lim, is meant to detect early signs of stroke.

A multiple sensor headset records electrical impulses in the brain, algorithms determine the likelihood of a stroke in one minute, and results are presented in a mobile app.  EDSAP can also analyze stress and sleep patterns, and potentially be used to monitor heart activity.  The company believes that the system can one day be built into one’s own glasses.

Wearable Tech + Digital Health NYC 2015 – June 30 @ New York Academy of Sciences