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Title: Human activities recognition in smart living environment
Authors: Loh, Teck Wei
Keywords: DRNTU::Engineering
Issue Date: 2018
Abstract: Human activity recognition has been changing the way people live through smart homes. Machine learning algorithms are used to accurately detect human activities at home. The usage of cameras can be considered invasive to some home owners, therefore alternate kind of sensors have to be used. Mobile phones provide a good range of sensors to test and also to detect the various types of activities. This paper examines different data sets for comparison, how accelerometer, gyroscope as well as pressure sensors cam be used in detecting the various activities. MATLAB’s classificationLearner application will be used in this experiment to aid in quick and accurate testing, as well as visualising of data
Schools: School of Electrical and Electronic Engineering 
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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