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dc.contributor.authorYe, Yuchen
dc.description.abstractToday, in the mobile internet era, sensors are now widely used around the world. The development and application of sensors benefits humans in many fields. In the recent years, sensor-based activities recognition has made great progress. Among them, activities recognition research based on wearable sensors and smartphones’ sensors have occupied a major position and provide a lot of support of application in human’s daily life. Smartphone is easy to carry, due to this advantage, a large number of researchers use smartphone to collect sensor data and research. In this project, MATLAB was used to process accelerometer sensor data from smartphone, then examines the best accuracy that can be achieved by using different machine learning algorithms including K-Near Neighbor (K-NN), Support Vector Machines (SVM) and Ensemble Learner. MATLAB is a convenient software to do machine learning. MATLAB’s Classification Learner app which provides users several classifiers and visual interface of sensor data will be used in this experiment. The result of experiment shows that all the machine learning algorithms can reach 85 or higher. The highest accuracy that can be achieved is 94.74% by using Cubic SVM.en_US
dc.format.extent63 p.en_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineeringen_US
dc.titleHuman activities recognition in smart living environmenten_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorSoh Yeng Chaien_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineering (Electrical and Electronic Engineering)en_US
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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