Already popular in Japan, today’s New York Times reports on the developing trend of robotic companions for the elderly.
A typical Japanese example is the Tsukuba University created Hybrid Assistive Limb. The battery-powered suit senses and amplifies the wearer’s muscle action when carrying or lifting heavy objects. Caregivers can also use the suit to aid them while lifting patients from a bed, and patients can wear it to support their movements. Other Japanese devices include a small, battery-powered trolley to aid independent walking; a portable, self-cleaning bedside toilet; and a monitoring robot which tracks and reports the location of dementia patients.
The Times describes several interesting US developed robots: Cody, a Georgia Tech created robotic nurse cable of bathing patients; HERB, a Carnegie Mellon developed butler which retrieves objects and cleans; Hector, a University of Reading robot which provides medication reminders, locates lost objects, and can assist in a fall; and Paro, a baby seal looking robot which calms dementia patients.
On Capitol Hill, IBM representatives described the supercomputer’s new health-care related features, including the ability to ingest patients’ medical information and synthesize thousands of medical journals and other reference materials along with patient preferences to suggest treatment options.
The Watson team has collaborated with the Memorial Sloan-Kettering Cancer Center and insurer Well Point to teach the computer about the medical world.
The W/Me sensor has the ability to capture electrical impulses relayed from the sinoatrial (SA) node, a group of specialized cells in the right atrium. It uses a proprietary algorithm to measure heart rate variability, map the autonomic nervous system, and indicate mental state.
Tobii ATI has unveiled a new tablet-like product line, the i-Series, designed to let users with ALS, stroke, and cerebral palsy use email and Skype through eye-gaze tracking. A mouse is no longer needed.
Google, NASA and the Universities Space Research Association will put a 512 qubit machine from D-Wave at the disposal of researchers around the globe. The USRA will invite teams of scientists and engineers to share time on the unique supercomputer. The goal is to study how quantum computing might be leveraged to advance machine learning.
Professor Bradley Nelson and researchers at ETH Zurich have created a miniature robot that can be injected into the eye to precisely measure the retina’s oxygen supply. Many diseases, including Glaucoma, can interfere with oxygen delivery to the retina. Rapid diagnosis and treatment is essential in the attempt to preserve vision.
New technology developed at UC Berkeley uses wireless signals to provide real-time, noninvasive diagnoses of brain swelling or bleeding. The device analyzes data from low-energy electromagnetic waves, similar to the kind used to transmit radio and mobile signals. It is sensitive enough to distinguish between a normal brain and a diseased brain with one single noncontact set of measurements.
Professor Todd Coleman of UCSD is developing foldable, stretchable electrode arrays that can non-invasively measure neural signals. They can also provide more in-depth analysis by including thermal sensors to monitor skin temperature and light detectors to analyze blood oxygen levels. The device is powered by micro solar panels and uses antennae to wirelessly transmit or receive data. Professor Coleman wants to use the device on premature babies to monitor their mental state and detect the onset of seizures that can lead to brain development problems such as epilepsy.
A team led by Mitchell Lerner at the University of Pennsylvania has developed a carbon nanotube based transistor that can detect glucose levels in body fluids, including saliva. The nanotubes are coated with molecules of pyrene-1-boronic acid, which makes them highly sensitive for glucose detection. When exposed to glucose, the nanotube transistor’s current-voltage curve changes, and that change can be measured to indicate the glucose concentration.
fMRI-driven neurofeedback has been used in various contexts, but never applied to the treatment of anxiety.
Yale University researchers used fMRI to display the activity of the orbitofrontal cortex, a brain region just above the eyes, to subjects in real time. Through a process of trial and error, the subjects learned to control their brain activity. This neurofeedback led to changes in brain connectivity and increased control over anxiety. The changes were still present several days after the exercise.