Human activities recognition in smart living environment
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. Mobil...
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| Online Access: | http://hdl.handle.net/10356/75269 |
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sg-ntu-dr.10356-752692023-07-07T16:21:02Z Human activities recognition in smart living environment Loh, Teck Wei Soh Yeng Chai School of Electrical and Electronic Engineering DRNTU::Engineering 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 Bachelor of Engineering 2018-05-30T06:58:22Z 2018-05-30T06:58:22Z 2018 Final Year Project (FYP) http://hdl.handle.net/10356/75269 en Nanyang Technological University 68 p. application/pdf |
| institution |
Nanyang Technological University |
| building |
NTU Library |
| continent |
Asia |
| country |
Singapore Singapore |
| content_provider |
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| language |
English |
| topic |
DRNTU::Engineering |
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DRNTU::Engineering Loh, Teck Wei Human activities recognition in smart living environment |
| description |
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 |
| author2 |
Soh Yeng Chai |
| author_facet |
Soh Yeng Chai Loh, Teck Wei |
| format |
Final Year Project |
| author |
Loh, Teck Wei |
| author_sort |
Loh, Teck Wei |
| 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 |
2018 |
| url |
http://hdl.handle.net/10356/75269 |
| _version_ |
1772826290290688000 |