21 September 2019

Nanomembrane Wearable Brain Machine Interface

Combining new classes of nanomembrane electrodes with flexible electronics and a deep learning algorithm could help disabled people wirelessly control an electric wheelchair, interact with a computer or operate a small robotic vehicle without donning a bulky hair-electrode cap or contending with wires. By providing a fully portable, wireless brain-machine interface (BMI), the wearable system could offer an improvement over conventional electroencephalography (EEG) for measuring signals from visually evoked potentials in the human brain. The system's ability to measure EEG signals for BMI has been evaluated with six human subjects, but has not been studied with disabled individuals. The project was conducted by researchers from the Georgia Institute of Technology, University of Kent and Wichita State University. BMI is an essential part of rehabilitation technology that allows those with amyotrophic lateral sclerosis (ALS), chronic stroke or other severe motor disabilities to control prosthetic systems. Gathering brain signals known as steady-state virtually evoked potentials (SSVEP) now requires use of an electrode-studded hair cap that uses wet electrodes, adhesives and wires to connect with computer equipment that interprets the signals. 


Researchers are taking advantage of a new class of flexible, wireless sensors and electronics that can be easily applied to the skin. The system includes three primary components: highly flexible, hair-mounted electrodes that make direct contact with the scalp through hair; an ultrathin nanomembrane electrode; and soft, flexible circuity with a Bluetooth telemetry unit. The recorded EEG data from the brain is processed in the flexible circuitry, and then wirelessly delivered to a tablet computer via Bluetooth from up to 15 meters away. Beyond the sensing requirements, detecting and analyzing SSVEP signals have been challenging because of the low signal amplitude, which is in the range of tens of micro-volts, similar to electrical noise in the body. Researchers also must deal with variation in human brains. Yet accurately measuring the signals is essential to determining what the user wants the system to do. To address those challenges, the research team turned to deep learning neural network algorithms running on the flexible circuitry. In addition, the researchers used deep learning models to identify which electrodes are the most useful for gathering information to classify EEG signals. The system was evaluated with six human subjects.

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20 September 2019

AI Detects Heart Failure From A Single Heartbeat

Researchers have developed a neural network approach that can accurately identify congestive heart failure with 100 percent accuracy through analysis of just one raw electrocardiogram (ECG) heartbeat, a new study reports. Congestive heart failure (CHF) is a chronic progressive condition that affects the pumping power of the heart muscles. Associated with high prevalence, significant mortality rates and sustained healthcare costs, clinical practitioners and health systems urgently require efficient detection processes.


This research drastically improves existing CHF detection methods typically focused on heart rate variability that are time-consuming and prone to errors. Their new model uses a combination of advanced signal processing and machine learning tools on raw ECG signals, delivering 100 percent accuracy. Researchers trained and tested the CNN model on large publicly available ECG datasets featuring subjects with CHF as well as healthy, non-arrhythmic hearts. Models delivered 100 percent accuracy: by checking just one heartbeat we are able detect whether or not a person has heart failure.

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16 September 2019

Human Touch Nerve-like Optical Lace

A new synthetic material that creates a linked sensory network similar to a biological nervous system could enable soft robots to sense how they interact with their environment and adjust their actions accordingly. The stretchable optical lace material was developed by researchers at the Organics Robotics Lab at Cornell University.


The optical lace would not be used as a skin coating for robots, but would be more like the flesh itself. Robots fitted with the material would be better suited for the health care industry and manufacturing. While the optical lace does not have as much sensitivity as a human fingertip, which is jam-packed with nerve receptors, the material is more sensitive to touch than the human back.

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12 September 2019

VS-GAMES '19 Conference

The 11th International Conference on Games and Virtual Worlds for Serious Applications (VS-Games 2019) was held on 4-6 September 2019 in Vienna, Austria. VS-Games 2019 was jointly organised by 7reasons Medien GmbH, Austria, and the Human Computer Interaction Laboratory (HCI Lab), Faculty of Informatics, Masaryk University, Czech Republic. VS-Games 2019 was awarded technical co-sponsorship by the Institute of Electrical and Electronics Engineers (IEEE). VS-Games 2019 hosted three world class keynote speakers: Prof. Michael Wimmer, Prof. Panos Markopoulos and Noah Falstein. In conjunction with the conference, the 4th H2020 TERPSICHORE summer school tool place.

 
VS-Games 2019 had submissions from 20 different countries from all over the world and programme committee members from 22 different countries. VS-Games accepts 3 types of papers: full papers (8 pages), short papers (4 pages) and poster papers (2 pages), including references. In total, we received 92 submissions in two different calls for papers (full and short/poster) and each paper had 3 blind reviews. From the submissions, 16 full papers were accepted, 27 short papers and 11 poster papers. In the full-paper call, we had 44 submissions and 16 full papers were accepted (approximately 36% acceptance rate). The short-paper and poster call received 48 submissions.

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29 August 2019

Wearable Biocompatible Magnetic Skin

Researchers at King Abdullah University of Science and Technology have recently developed a flexible and imperceptible magnetic skin that adds permanent magnetic properties to all surfaces to which it is applied. This artificial skin could have numerous interesting applications. For instance, it could enable the development of more effective tools to aid people with disabilities, help biomedical professionals to monitor their patients' vital signs, and pave the way for new consumer tech.


The artificial skin is magnetic, thin and highly flexible. When it is worn by a human user, it can be easily tracked by a nearby magnetic sensor (i.e. if a user wears it on his eyelid, it allows for his eye movements to be tracked or if worn on fingers, it can help to monitor a person's physiological responses or even to control switches without touching them). The magnetic skin is easy to assemble, as it is made by mixing an elastomer matrix with magnetic powder and then drying this mixture at room temperature.

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