The Health eHeart Study will use smartphone apps, sensors and other devices to gather data on a wide variety of measures associated with cardiovascular health—including blood pressure, physical activity, diet and sleep habits—in real time.
A Fujitsu research lab has developed software that can accurately measure a subject’s pulse using the small digital cameras attached to smartphones and tablets.
The technology is based on the fact that the brightness of an individual’s face changes slightly as their heart beats, due to their blood flow. Hemoglobin, which carries oxygen around the body, absorbs green light, so analyzing the change in color of parts of the face reveals their heart rate.
As most image sensors capture pixel information in red, blue and green, they have the ability to detect hemoglobin built in. Fujitsu’s technology keeps track of specific regions of the face over time to take pulse measurements.
It seems that every day a new app or device promising the ultimate in health or fitness monitoring enters the market. A startup has created a personal analytics dashboard which gives people a big picture view of their own aggregated data and underlying patterns, helping them make sense of the numbers.
Sensor technology will enable smartphone users to keep track of sleep patterns, heart rate, exercise, and weight.
Eliminating the elastomer backing makes the device one-thirtieth as thick, and thus “more conformal to the kind of roughness that’s present naturally on the surface of the skin,” says John Rogers at the University of Illinois. It can be worn for up to two weeks and can measure temperature, strain, and the hydration state of the skin, and also be used to monitor wound healing.
A proposed effort to map brain activity on a large scale, expected to be announced by the White House later this month, could help neuroscientists understand the origins of cognition, perception, and other phenomena. These brain activities haven’t been well understood to date, in part because they arise from the interaction of large sets of neurons whose coordinated efforts scientists cannot currently track.
An article published Thursday in Science online expands the project’s already ambitious goals beyond just recording the activity of all individual neurons in a brain circuit simultaneously. Researchers should also find ways to manipulate the neurons within those circuits and understand circuit function through new methods of data analysis and modeling.
Johns Hopkins engineers have developed a powerful new computer-based process that helps identify the dangerous conditions that lead to concussion-related brain injuries.
Professor K.T. Ramesh led a team that used a technique called diffusion tensor imaging, together with a computer model of the head, to identify injured axons, which are tiny but important fibers that carry information from one brain cell to another. These axons are concentrated in a kind of brain tissue known as “white matter,” and they appear to be injured during the so-called mild traumatic brain injury associated with concussions. Ramesh’s team has shown that the axons are injured most easily by strong rotations of the head, and the researchers’ process can calculate which parts of the brain are most likely to be injured during a specific event.
A doctor recently used his iPhone, in combination with AliveCor, a mounted sensor capable of delivering clinically accurate electrocardiograms, while in flight, to measure the vital signs of a passenger experiencing severe chest pains at 30,000 feet.
The results indicated that the passenger was having a heart attack. The doctor recommended an urgent landing, and the passenger survived after being rushed to the hospitall
PUMA measures six components to evaluate metabolic function: oxygen and carbon dioxide partial pressure, volume flow rate, heart rate, and gas pressure and temperature. From those measurements, PUMA can compute the oxygen uptake, carbon dioxide output and minute ventilation (average expired gas flow rate). A small, embedded computer takes readings of each sensor and relays the data wirelessly to a remote computer via Bluetooth.
Devices that collect personal medical information are growing both prolific and inexpensive. The biggest challenges lie not in collecting and transmitting the data, but in building the backend systems that can interpret it.