New Scientist article The idea that you can track people from space using their own eyes is just one of many possibilities in the field of wearable sensors.
Now researchers at the University of Oxford have developed a gephy sensor that can detect the movement of people’s eyes using their skin.
The team, led by Professor David Smith, found that their sensor can also detect the eye movements of other parts of the body.
They found that it works as a real-time sensor and can track the movement from the head to the feet without needing to have a pair of eyes in the right place.
“We found that the sensor was extremely robust, and can detect motion even when the eyes are moved, which is an important feature,” said Smith.
“The device can track a person’s eye movements without having to be looking at them.”
The team says their work could lead to a range of future wearable applications.
One such application is to make a device that could track an animal’s heartbeat, which might be used in tracking an epileptic patient or a patient with a neurological disorder.
“These sensors could also be used as an input device for medical sensors, where the eyes could be connected to the heart rate, and it would automatically translate the sensor’s information into a patient’s heart rate,” said Professor Smith.
Another application could be a heart monitor that tracks the heartbeats of a patient or patient’s family members.
Other potential applications of the sensor are the tracking of movements in the environment, and could be used by an ambulance service or a drone to detect and respond to emergencies.
“If you’re looking at the heart beat of a bird and you’re tracking that, you might have to wait for it to catch up with the ambulance,” said Dr Matthew Linnane, who led the study.
“That’s not good enough.
We need a device for detecting when an animal is about to pass by, and for the camera to capture that.
So we developed a sensor that captures the heartbeat of a person and then transmits that to the camera.”
In addition to the sensors, the team also developed a prototype that uses a camera to detect the movements of the wearer’s eyes.
The device works in two different ways, and is not compatible with other kinds of sensors.
The first method is to use a microcontroller to control the sensor.
“This microcontroller is really easy to program, and uses the Arduino microcontroller’s SPI bus,” said Linnanes co-author Professor Stephen Jones.
“It’s a very small device, so we didn’t have to worry about getting the chip out of the package.
We could then control the chip and use it for whatever we wanted to do with the device.”
The second method uses a processor to perform the sensing.
The processor, called the “dynamic processor”, can calculate the eye movement using the eye position of the person in the image.
The dynamic processor then translates this to the human eye movement, which in turn can be interpreted as a motion.
The sensor is a simple piece of electronics, with a small LCD screen that can be placed on a person to record the eye tracking.
The researchers hope to further improve the sensors in the future.
“In the future we’re hoping to make the device smaller and cheaper,” said Jones.
The team are now in the process of building a second prototype, which will have a larger LCD screen.
They have plans to create a third prototype, in which the eye tracker can be made to work with a different sensor that measures the wearer.
The next step will be to add a second sensor that will track the wearer on a separate device, to improve the performance of the device.
“Our aim is to have an eye tracker that’s able to measure the eye and the wearer simultaneously,” said James Linnanas team leader.
We want to build a device which can be built into an eyewear, and then we can use the data from the gp sensor to create an optical image.””
So we want to be able to track a real person in a real environment, not a digital image.
We want to build a device which can be built into an eyewear, and then we can use the data from the gp sensor to create an optical image.”
If we can get that right, we will be able track a lot more people in real time.
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