{"id":159211,"date":"2010-03-22T00:00:00","date_gmt":"2010-03-22T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/automatic-classification-of-daily-fluid-intake\/"},"modified":"2018-10-16T21:38:48","modified_gmt":"2018-10-17T04:38:48","slug":"automatic-classification-of-daily-fluid-intake","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/automatic-classification-of-daily-fluid-intake\/","title":{"rendered":"Automatic Classification of Daily Fluid Intake"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Despite the potential health benefits of being able to monitor and log one\u2019s food and drink intake, manually performing this task is notoriously hard. While researchers are still exploring methods of automating this process for food, less work has been done in automatically classifying beverage intake. In this paper, we present a novel method that utilizes optical, ion selective electrical pH, and conductivity sensors in order to sense and classify liquid in a cup in a practical way. We describe two experiments, one that uses a high end commercial off-the-shelf spectrometer, and the other which uses a cheap sensor package that we engineered. Results show both that this method is feasible and relatively accurate (up to 79% classification for 68 different drinks), but also that we would be able to build this in such a way as to make it practical for real-world deployment. We describe the vision for building a sensor rich cup capable of determining the kind of liquid a person is drinking, as well as the opportunities that the success of such sensors may open.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Despite the potential health benefits of being able to monitor and log one\u2019s food and drink intake, manually performing this task is notoriously hard. While researchers are still exploring methods of automating this process for food, less work has been done in automatically classifying beverage intake. In this paper, we present a novel method that [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13554],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-159211","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-human-computer-interaction","msr-locale-en_us"],"msr_publishername":"IEEE","msr_edition":"Pervasive Health 2010 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