Atrial fibrillation-detecting ring

Eue-Keun Choi and Seoul National University colleages have developed an atrial fibrillation detecting ring, with similar functionality to AliveCor and other watch-based monitors. The researchers claim that the performance is comparable to medical grade pulse oximeters.

In a study, Soonil Kwon and colleagues analyzed data from 119 patients with AF who underwent simultaneous ECG and photoplethysmography before and after direct-current cardioversion. 27,569 photoplethysmography samples were analyzed by an algorithm developed with a convolutional neural network. Rhythms were then interpreted with the wearable ring.

The accuracy of the convolutional neural network was 99.3% to diagnose AF and 95.9% to diagnose sinus rhythm.  The accuracy of the wearable device was 98.3% for sinus rhythm and 100% for AF after filtering low-quality samples.

Choi believes that: “Deep learning or [artificial intelligence] can overcome formerly important problems of [photoplethysmography]-based arrhythmia diagnosis. It not only improves diagnostic accuracy in great degrees, but also suggests a metric how this diagnosis will be likely true without ECG validation. Combined with wearable technology, this will considerably boost the effectiveness of AF detection.”.


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

Fingertip wearable measures disease-associated grip strength

IBM researchers are studying grip strength, which is associated with the effectiveness of Parkinson’s drugs, cognitive function in schizophrenics, cardiovascular health, and elderly mortality.

To better understand these markers, Steve Heisig, Gaddi Blumrosen and colleagues have developed a prototype wearable that continuously measures how a fingernail bends and moves.

The project began as an attempt to capture the medication state Parkinson’s patients, but was soon expanded to measure the tactile sensing of pressure, temperature, surface textures and other indicators of various diseases. Nail bending was measured throughout the day, and AI was used to analyze the data for disease association.

The system consists of strain gauges attached to the fingernail and a small computer that samples strain values, collects accelerometer data and communicates with a smart watch. The watch runs machine learning models to rate bradykinesia, tremor, and dyskinesia.

The work is also being used in the development of a fingertip-structure modeled device that could  help quadriplegics communicate.

Click to view IBM video


Join ApplySci at the 10th Wearable Tech + Digital Health + Neurotech Silicon Valley conference on February 21-22 at Stanford University — Featuring:  Zhenan BaoChristof KochVinod KhoslaWalter Greenleaf – Nathan IntratorJohn MattisonDavid EaglemanUnity Stoakes Shahin Farshchi Emmanuel Mignot Michael Snyder Joe Wang – Josh Duyan – Aviad Hai Anne Andrews Tan Le – Anima Anandkumar – Hugo Mercier – Shea Balish – Kareem Ayyad – Mehran Talebinejad – Liam Kaufman – Scott Barclay

Continuous blood pressure monitoring glasses

Microsoft’s Glabella glasses, developed by Christian Holz and Edward Wang, will have integrated optical sensors that take pulse wave readings from three areas around the face, according to their recently granted patent.

Blood pressure is calculated by measuring the time between when blood is ejected from the heart and reaches the face. The researchers believe that the device can unobtrusively and continuously measure blood pressure.


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 – Ed Simcox – Sean Lane