For people suffering with obesity, logging calorie counts and different dietary facts at each meal is a proven manner to shed pounds. The technique does require consistency and accuracy, but, and when it fails, it's generally because people don't have the time to find and report all the information they want.
a few years in the past, a crew of nutritionists from Tufts college who were experimenting with cell-telephone apps for recording caloric consumption approached individuals of the Spoken Language structures group at MIT's computer technology and artificial Intelligence Laboratory (CSAIL), with the idea of a spoken-language application that might make meal logging even less difficult.
This week, on the international convention on Acoustics, Speech, and signal Processing in Shanghai, the MIT researchers are supplying an internet-based prototype in their speech-managed nutrients-logging gadget.
With it, the user verbally describes the contents of a meal, and the machine parses the outline and automatically retrieves the pertinent dietary records from a web database maintained by means of the U.S. branch of Agriculture (USDA).
The information is displayed collectively with pictures of the corresponding foods and pull-down menus that permit the consumer to refine their descriptions -- choosing, as an instance, precise portions of meals. but those refinements also can be made verbally. A user who starts by announcing, "For breakfast, I had a bowl of oatmeal, bananas, and a glass of orange juice" can then make the amendment, "I had half of a banana," and the device will replace the facts it presentations about bananas at the same time as leaving the relaxation unchanged.
"What [the Tufts nutritionists] have experienced is that the apps that had been out there to help human beings try and log food tended to be a bit tedious, and consequently humans failed to maintain up with them," says James Glass, a senior research scientist at CSAIL, who leads the Spoken Language structures institution. "so they had been seeking out ways that have been correct and easy to enter statistics."
the first writer on the new paper is Mandy Korpusik, an MIT graduate student in electric engineering and computer technology. She's joined through Glass, who is her thesis advisor; her fellow graduate scholar Michael charge; and with the aid of Calvin Huang, an undergraduate researcher in Glass's group.
inside the paper, the researchers document the consequences of experiments with a speech-recognition machine that they advanced specially to deal with meals-associated terminology. but that wasn't the principle focus of their work; certainly, an online demo of their meal-logging device rather makes use of Google's loose speech-recognition app.
Their studies concentrated on two other problems. One is figuring out words' useful position: The gadget desires to understand that if the consumer statistics the phrase "bowl of oatmeal," dietary statistics on oatmeal is pertinent, but if the word is "oatmeal cookie," it's now not.
the alternative trouble is reconciling the person's phraseology with the entries inside the USDA database. as an instance, the USDA information on oatmeal is recorded below the heading "oats"; the word "oatmeal" indicates up nowhere inside the access.
To cope with the first trouble, the researchers used gadget mastering. through the Amazon Mechanical Turk crowdsourcing platform, they recruited workers who really described what that they had eaten at current meals, then categorized the pertinent words inside the description as names of meals, quantities, logo names, or modifiers of the meals names. In "bowl of oatmeal," "bowl" is a amount and "oatmeal" is a food, however in "oatmeal cookie," oatmeal is a modifier.
once they had roughly 10,000 labeled meal descriptions, the researchers used machine-gaining knowledge of algorithms to locate styles within the syntactic relationships among words that might become aware of their functional roles.
To translate among customers' descriptions and the labels in the USDA database, the researchers used an open-source database referred to as Freebase, which has entries on greater than eight,000 not unusual food objects, many of which encompass synonyms. wherein synonyms have been lacking, they again recruited Mechanical Turk employees to deliver them.
The version of the gadget offered at the convention is intended chiefly to illustrate the viability of its method to natural-language processing; it reviews calorie counts however doesn't yet overall them mechanically. A model that does is inside the works, however, and while it's entire, the Tufts researchers plan to behavior a user study to determine whether or not it indeed makes nutrients logging easier.