Human activities recognition in smart living environment

Today, 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...

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Main Author: Ye, Yuchen
Other Authors: Soh Yeng Chai
Format: Final Year Project
Language:English
Published: 2019
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Online Access:http://hdl.handle.net/10356/78432
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-784322023-07-07T16:18:50Z Human activities recognition in smart living environment Ye, Yuchen Soh Yeng Chai School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering Today, 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. Bachelor of Engineering (Electrical and Electronic Engineering) 2019-06-20T02:45:28Z 2019-06-20T02:45:28Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/78432 en Nanyang Technological University 63 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering
Ye, Yuchen
Human activities recognition in smart living environment
description Today, 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.
author2 Soh Yeng Chai
author_facet Soh Yeng Chai
Ye, Yuchen
format Final Year Project
author Ye, Yuchen
author_sort Ye, Yuchen
title Human activities recognition in smart living environment
title_short Human activities recognition in smart living environment
title_full Human activities recognition in smart living environment
title_fullStr Human activities recognition in smart living environment
title_full_unstemmed Human activities recognition in smart living environment
title_sort human activities recognition in smart living environment
publishDate 2019
url http://hdl.handle.net/10356/78432
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