Radio-frequency (RF) sensing for deep awareness of human physical status - part II
The application of radar signal processing techniques, specifically with ultra-wideband (UWB) radar is investigated in this study. The intention is to non-invasively detect and classify human emotions using UWB radar. Features related to breathing and heart rate are extracted from the signal. Mac...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/175289 |
| _version_ | 1811676546634612736 |
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| author | Kng, Yew Chian |
| author2 | Luo Jun |
| author_facet | Luo Jun Kng, Yew Chian |
| author_sort | Kng, Yew Chian |
| collection | NTU |
| description | The application of radar signal processing techniques, specifically with ultra-wideband (UWB)
radar is investigated in this study. The intention is to non-invasively detect and classify human
emotions using UWB radar. Features related to breathing and heart rate are extracted from the
signal. Machine learning techniques are then applied to classify between emotions defined by the arousal-valence theory. The dataset was created by exposing subjects to different videos meant to incite different responses while being monitored by a UWB radar. |
| first_indexed | 2024-10-01T02:23:12Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/175289 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:23:12Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1752892024-04-26T15:44:34Z Radio-frequency (RF) sensing for deep awareness of human physical status - part II Kng, Yew Chian Luo Jun School of Computer Science and Engineering junluo@ntu.edu.sg Computer and Information Science Machine learning Radar The application of radar signal processing techniques, specifically with ultra-wideband (UWB) radar is investigated in this study. The intention is to non-invasively detect and classify human emotions using UWB radar. Features related to breathing and heart rate are extracted from the signal. Machine learning techniques are then applied to classify between emotions defined by the arousal-valence theory. The dataset was created by exposing subjects to different videos meant to incite different responses while being monitored by a UWB radar. Bachelor's degree 2024-04-23T06:18:26Z 2024-04-23T06:18:26Z 2024 Final Year Project (FYP) Kng, Y. C. (2024). Radio-frequency (RF) sensing for deep awareness of human physical status - part II. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/175289 https://hdl.handle.net/10356/175289 en application/pdf Nanyang Technological University |
| spellingShingle | Computer and Information Science Machine learning Radar Kng, Yew Chian Radio-frequency (RF) sensing for deep awareness of human physical status - part II |
| title | Radio-frequency (RF) sensing for deep awareness of human physical status - part II |
| title_full | Radio-frequency (RF) sensing for deep awareness of human physical status - part II |
| title_fullStr | Radio-frequency (RF) sensing for deep awareness of human physical status - part II |
| title_full_unstemmed | Radio-frequency (RF) sensing for deep awareness of human physical status - part II |
| title_short | Radio-frequency (RF) sensing for deep awareness of human physical status - part II |
| title_sort | radio frequency rf sensing for deep awareness of human physical status part ii |
| topic | Computer and Information Science Machine learning Radar |
| url | https://hdl.handle.net/10356/175289 |
| work_keys_str_mv | AT kngyewchian radiofrequencyrfsensingfordeepawarenessofhumanphysicalstatuspartii |