Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/157017
Title: Radio-frequency (RF) sensing for deep awareness of human physical status
Authors: Koh, Bernard Sheng Hui
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Engineering::Computer science and engineering::Computing methodologies::Pattern recognition
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Koh, B. S. H. (2022). Radio-frequency (RF) sensing for deep awareness of human physical status. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157017
Abstract: In recent years, there are a rise in studies of contactless Radio Frequency (RF) sensing for human physical status like one’s respiration behaviour. In these studies, a radar sensor will be used to collect the raw data from a human subject to provide insights on the respiration. This conventional method includes the use of complex signal processing and domain expert for feature engineering to produce a result. This came the motivation of using deep learning to leverage or avoid this complication. In this project, an attempt is made to implement deep learning techniques on relatively unexplored field of predicting human respiration rate. Techniques like Denoising Convolutional Autoencoder (DCAE) is applied to denoise the potential noisy data then coupled with a 1-Dimensional Convolutional Neural Network (1-D CNN) to learn from these processed data to make a prediction. Despite the prediction results shown in this project are far from ideal, the proposed approach can be a good foundation for future study to build on.
URI: https://hdl.handle.net/10356/157017
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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