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Fujitsu has developed technology which can measure a person’s pulse in real time by analyzing video of their face.

“As blood circulates through the body, the amount of light absorbed by the face varies, depending on how much blood there is in it. The first point about this technology is, it identifies minute changes in light intensity on the face, and converts them to a pulse. Also, it accurately detects people’s movements, to distinguish noise. Consequently, it can make a measurement in as little as five seconds.”

When the user is sitting still, the system continuously detects changes in light intensity on the users face, as shown by the green waveform. The red waveform shows the resulting wave with noise associated with movement removed. Fujitsu has found that that accuracy of the system is within about three beats per minute.

“The main point about this technology is, it can make the measurements naturally. All the person needs to do is be in front of the camera, without operating a device. For example, when you’re working on a computer, you often stop moving for at least five seconds while you’re thinking. We think that, by detecting those moments and measuring your pulse rate, this system could be used to support health management, by recording changes throughout the day.”

“In the case of a security camera, it might be possible to detect suspicious persons, based on the assumption that people about to do something risky have a high pulse rate. However, we don’t think that can be done using this technology alone. We think it might be possible through all-round analysis, by combining this with other technologies.”

“We’d like to release this as a device embedded in our products. Right now, we’re working to bring such products out this year, including smartphones as well as PCs.”

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A group at Tokyo Institute of Technology, led by Dr. Osamu Hasegawa, has succeeded in making further advances with SOINN, their machine learning algorithm, which can now use the internet to learn how to perform new tasks.

“Image searching technology is quite practical now. So, by linking our algorithm to that, we’ve enabled the system to identify which characteristics are important by itself, and to remember that what kind of thing the subject is.”

These are pictures of rickshaws, taken in India by the Group. When one of these pictures is loaded, the system hasn’t yet learned what it is. So, it recognizes the subject as a “car,” which it has already learned. The system is then given the keyword “rickshaw.” From the Internet, the system picks out the main characteristics of pictures related to rickshaws, and learns by itself what a rickshaw is. After learning, even if a different picture of a rickshaw is loaded, the system recognizes it as a rickshaw.

“In the case of a rickshaw, there may be other things in the picture, or people may be riding in the rickshaw, but the system picks out only those features common to many cases, such as large wheels, a platform above the wheels, and a roof, and it learns that what people call a rickshaw includes these features. So, even with an object it hasn’t seen before, if the object has those features, the system can recognize it.”

“With previous methods, for example, face recognition by digital cameras, it’s necessary to teach the system quite a lot of things about faces. When subjects become diverse, it’s very difficult for people to tell the system what sort of characteristics they have, and how many features are sufficient to recognize things. SOINN can pick those features out for itself. It doesn’t need models, which is a very big advantage.”

The Group is also developing ways to transfer learned characteristic data to other things. For example, the system has already learned knives and pens, and possesses the characteristic data that they are “pointed objects” and “stick-shaped objects” respectively. To make the system recognize box cutters, it’s made to look at the similarities between box cutters, and knives and pens, which it has already learned. And it’s made to transfer the basic characteristic of being stick-shaped and pointed. If characteristic data for box cutters can be obtained from other systems, SOINN can guess from the transferred data that the objects are box cutters.

“Here, you’ve seen how this works for pictures. But SOINN can handle other types of information flexibly. For example, we think we could teach it to pick out features from audio or video data. Then, it could also utilize data from robot sensors.”

“With previous pet robots, such as AIBO, training involved patterns that were decided in advance. When those possibilities are exhausted, the robot can’t do any more. So, people come to understand what it’s going to do, and get bored with it. But SOINN can remember an amount of changes. So, in principle, it can develop without a scripted scenario.”

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Fujitsu Laboratories has developed a next generation user interface which can accurately detect the users finger and what it is touching, creating an interactive touchscreen-like system, using objects in the real word.

“We think paper and many other objects could be manipulated by touching them, as with a touchscreen. This system doesn’t use any special hardware; it consists of just a device like an ordinary webcam, plus a commercial projector. Its capabilities are achieved by image processing technology.”

Using this technology, information can be imported from a document as data, by selecting the necessary parts with your finger.

This technology measures the shape of real-world objects, and automatically adjusts the coordinate systems for the camera, projector, and real world. In this way, it can coordinate the display with touching, not only for flat surfaces like tables and paper, but also for the curved surfaces of objects such as books.

“Until now, gesturing has often been used to operate PCs and other devices. But with this interface, we’re not operating a PC, but touching actual objects directly, and combining them with ICT equipment.”

“The system is designed not to react when you make ordinary motions on a table. It can be operated when you point with one finger. What this means is, the system serves as an interface combining analog operations and digital devices.”

To detect touch accurately, the system needs to detect fingertip height accurately. In particular, with the low-resolution camera used here (320 x 180), if fingertip detection is off by a single pixel, the height changes by 1 cm. So, the system requires technology for recognizing fingertips with high precision.

“Using a low-res webcam gives a fuzzy picture, but the system calculates 3D positions with high precision, by compensating through image processing.”

This system also includes technology for controlling color and brightness, in line with the ambient light, and correcting for individual differences in hand color. In this way, it can identify fingertips consistently, with little influence from the environment or individual differences.

Also, in situations that don’t use touch, the system can be operated by gesturing. In this demo, when you move your fist, you can manipulate the viewpoint for 3D CAD data. So, there could be applications for this touch system by combining it with current gesture systems.

“For example, we think this system could be used to show detailed information at a travel agent’s counter, or when you need to fill in forms at City Hall.”

“We aim to develop a commercial version of this system by fiscal 2014. It’s still at the demonstration level, so it’s not been used in actual settings. Next, we’d like to get people to use it for actual tasks, see what issues arise, and evaluate usability. We want to reflect such feedback in this system.”

This Video is provided to you by DigInfo.tv, AkihabaraNews Official Partner.

Panasonic, together with the Belgium-based research institution IMEC, has developed a DNA testing chip that automates all stages of obtaining genetic information, including preprocessing.

This development is expected to enable personalized, tailor-made therapy to become widespread.

“This is the chip we’ve actually developed. As you can see, it’s less than half the size of a business card. It contains everything needed for testing DNA. Once a drop of blood is inserted, the chip completes the entire process, up to SNP detection.”

SNPs are variations in a single DNA base among individuals.

Detecting SNPs makes it possible to check whether genetically transmitted diseases are present, evaluate future risks, and identify genes related to illness.

“By investigating SNPs, we can determine that this drug will work for this person, or this drug will have severe side-effects on that person. Investigating SNPs enables tailor-made therapy. But with the current method, it has to be done in a specialized lab, so it actually takes three to four days. In the worst case, it takes a week from sending the sample to getting the result. Our equipment can determine a patient’s SNPs in just an hour after receiving the blood.”

Testing is done simply by injecting the blood and a chemical into the chip, and setting it in the testing system.

First of all, the blood and chemical are mixed. DNA is then extracted from the mixed solution. The regions containing SNPs are then cut out and amplified. DNA amplification uses technology called PCR, which cuts out the desired sections by varying the temperature. With the conventional method, this process took two hours.

“Through careful attention to thermal separation design, we’ve achieved high-speed PCR, where 30 temperature cycles are completed in nine minutes. We think this is one of the fastest PCR systems in the world.”

The amplified DNA is then sent through a micropump to a DNA filter. Here, the DNA is separated for each section length. Then, a newly developed electrochemical sensor identifies SNPs while the DNA is dissolved in the chemical.

“To implement this system on one chip, and make detection easy, the first thing we focused on was the actuators. This system requires a very small, powerful pump. In our case, we used a conductive polymer for the actuators. A feature of these actuators is they’re powerful, yet extremely compact. They can exert a pressure of up to 30MPa.”

“Ultimately, we’d like to make this system battery-powered. We think that would enable genetically modified foods to be tested while still in the warehouse.”

This Video is provided to you by DigInfo.tv, AkihabaraNews Official Partner.

Via: Panasonic, IMEC

Researchers at Panasonic’s imaging division have found a way to increase the sensitivity of digital camera sensors, which in turn equates to almost double the brightness in photos taken in low light conditions. But the discovery has nothing to do with the sensor itself; instead, the company’s improved the color processing filter placed in front of it. More »