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Using machine learning for real-time activity recognition and estimation of energy expenditure

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dc.contributor.advisor Kent Larson. en_US Munguia Tapia, Emmanuel, 1978- en_US
dc.contributor.other Massachusetts Institute of Technology. Dept. of Architecture. Program in Media Arts and Sciences. en_US 2009-03-20T19:31:34Z 2009-03-20T19:31:34Z 2008 en_US 2008 en_US
dc.description Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2008. en_US
dc.description This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. en_US
dc.description Includes bibliographical references (p. 481-493). en_US
dc.description.abstract Obesity is now considered a global epidemic and is predicted to become the number one preventive health threat in the industrialized world. Presently, over 60% of the U.S. adult population is overweight and 30% is obese. This is of concern because obesity is linked to leading causes of death, such as heart and pulmonary diseases, stroke, and type 2 diabetes. The dramatic rise in obesity rates is attributed to an environment that provides easy access to high caloric food and drink and promotes low levels of physical activity. Unfortunately, many people have a poor understanding of their own daily energy (im)balance: the number of calories they consume from food compared with what they expend through physical activity. Accelerometers offer promise as an objective measure of physical activity. In prior work they have been used to estimate energy expenditure and activity type. This work further demonstrates how wireless accelerometers can be used for real-time automatic recognition of physical activity type, intensity, and duration and estimation of energy expenditure. The parameters of the algorithms such as type of classifier/regressor, feature set, window length, signal preprocessing, sensor set utilized and their placement on the human body are selected by performing a set of incremental experiments designed to identify sets of parameters that may balance system usability with robust, real-time performance in low processing power devices such as mobile phones. The algorithms implemented are evaluated using a dataset of examples of 52 activities collected from 20 participants at a gymnasium and a residential home. The algorithms presented here may ultimately allow for the development of mobile phone-based just-in-time interventions to increase self-awareness of physical activity patterns and increases in physical activity levels in real-time during free-living that scale to large populations. en_US
dc.description.abstract (cont.) KEYWORDS: Activity recognition, context awareness, energy expenditure, physical activity, wearable sensors, obesity, mobile phone, pattern recognition, machine learning, ubiquitous, pervasive, just-in-time. en_US
dc.description.statementofresponsibility by Emmanuel Munguia Tapia. en_US
dc.format.extent 493 p. en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. en_US
dc.rights.uri en_US
dc.subject Architecture. Program in Media Arts and Sciences. en_US
dc.title Using machine learning for real-time activity recognition and estimation of energy expenditure en_US
dc.type Thesis en_US Ph.D. en_US
dc.contributor.department Massachusetts Institute of Technology. Dept. of Architecture. Program in Media Arts and Sciences. en_US
dc.identifier.oclc 300459396 en_US

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