November 29, 2010
Discussions about the cloud and healthcare have been hovering for some time now but with the regulatory environment, moving large-scale medical systems into the cloud seems rather far off. On the personal level, however, there are some unique ideas filtering to the general public powered by the clouds—and the mobile devices that can tap into them.
NTT Communications, the University of Tokyo, and a handful of smaller companies have just announced a new service coming in January called Health Enhancement Assist Service, which will analyze daily eating and activity habits in order to provide real-time recommendations.
Reports state that this service will “analyze photos of food taken with the user’s mobile phone to estimate the number of calories as well as the nutritional category and quantity of the food…using location information and acceleration data from the sensors in the mobile phone, the service will gauge how far and fast the user walks each day to estimate daily metabolism.”
The food photos will be handled by an algorithm developed at the University of Tokyo and will be housed on a system called FoodLo that enables users to keep track of their dietary habits by storing images of everything they eat. As one might imagine, as the number of users grows, so too will the database of food images to run from.
All of the data generated will be stored in the cloud where it can be pulled to produce customized recommendations about exercise and eating habits. Furthermore, users can look back at their patterns to see what trends emerge or share the information with healthcare providers.
The report also claims that “data from digital scales, pedometers and other devices can be easily stored via the service, which will be network-free and will support multiple devices, enabling access virtually anywhere and anytime.”
It is a little mind-boggling to think there’s an app that can do all of this—particularly in terms of using the University of Tokyo-developed food image recognition algorithm to accurately determine food items and more interestingly, their exact proportions. Still, this experiment, which will go public in April, is a sign of what might lie ahead at the intersection of cloud-stored data, mobile technology and personal health.
Full story at Benzinga
Large-scale, worldwide scientific initiatives rely on some cloud-based system to both coordinate efforts and manage computational efforts at peak times that cannot be contained within the combined in-house HPC resources. Last week at Google I/O, Brookhaven National Lab’s Sergey Panitkin discussed the role of the Google Compute Engine in providing computational support to ATLAS, a detector of high-energy particles at the Large Hadron Collider (LHC).
Frank Ding, engineering analysis & technical computing manager at Simpson Strong-Tie, discussed the advantages of utilizing the cloud for occasional scientific computing, identified the obstacles to doing so, and proposed workarounds to some of those obstacles.
The private industry least likely to adopt public cloud services for data storage are financial institutions. Holding the most sensitive and heavily-regulated of data types, personal financial information, banks and similar institutions are mostly moving towards private cloud services – and doing so at great cost.
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