Wi-Fi-based based human activity recognition in indoor environment
With the emergence of Internet of Things (IoT) applications in smart homes, Human Activity Recognition (HAR) has acquired pivotal importance in this field. Conventional approaches to HAR include installing sensors in homes, wearable devices and Computer Vision solutions, but these approaches are not...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2020
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| Online Access: | https://hdl.handle.net/10356/140384 |
| _version_ | 1811688688052076544 |
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| author | Wei, Zuoli |
| author2 | Xie Lihua |
| author_facet | Xie Lihua Wei, Zuoli |
| author_sort | Wei, Zuoli |
| collection | NTU |
| description | With the emergence of Internet of Things (IoT) applications in smart homes, Human Activity Recognition (HAR) has acquired pivotal importance in this field. Conventional approaches to HAR include installing sensors in homes, wearable devices and Computer Vision solutions, but these approaches are not feasible in home applications due to their respective inconveniences. Studies have shown that the fluctuations in Wi-Fi signals can help reveal the movements of occupants. Therefore, in this project, Wi-Fi signals are exploited for classifications of human activities. This project underwent from collection of Wi-Fi signals (CSI frames), constructing and training deep learning models from scratch for such classification purposes. The classifier trained in this project can distinguish among seven different human activities with an accuracy of over 92%. |
| first_indexed | 2024-10-01T05:36:11Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/140384 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:36:11Z |
| publishDate | 2020 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1403842023-07-07T18:43:26Z Wi-Fi-based based human activity recognition in indoor environment Wei, Zuoli Xie Lihua School of Electrical and Electronic Engineering elhxie@ntu.edu.sg Engineering::Electrical and electronic engineering With the emergence of Internet of Things (IoT) applications in smart homes, Human Activity Recognition (HAR) has acquired pivotal importance in this field. Conventional approaches to HAR include installing sensors in homes, wearable devices and Computer Vision solutions, but these approaches are not feasible in home applications due to their respective inconveniences. Studies have shown that the fluctuations in Wi-Fi signals can help reveal the movements of occupants. Therefore, in this project, Wi-Fi signals are exploited for classifications of human activities. This project underwent from collection of Wi-Fi signals (CSI frames), constructing and training deep learning models from scratch for such classification purposes. The classifier trained in this project can distinguish among seven different human activities with an accuracy of over 92%. Bachelor of Engineering (Electrical and Electronic Engineering) 2020-05-28T08:26:13Z 2020-05-28T08:26:13Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/140384 en A1235-191 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Wei, Zuoli Wi-Fi-based based human activity recognition in indoor environment |
| title | Wi-Fi-based based human activity recognition in indoor environment |
| title_full | Wi-Fi-based based human activity recognition in indoor environment |
| title_fullStr | Wi-Fi-based based human activity recognition in indoor environment |
| title_full_unstemmed | Wi-Fi-based based human activity recognition in indoor environment |
| title_short | Wi-Fi-based based human activity recognition in indoor environment |
| title_sort | wi fi based based human activity recognition in indoor environment |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/140384 |
| work_keys_str_mv | AT weizuoli wifibasedbasedhumanactivityrecognitioninindoorenvironment |