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|Title:||Exploration study of 60 GHz radar sensor for radar assisted living||Authors:||Zhao, Ziyue||Keywords:||Engineering::Electrical and electronic engineering::Antennas, wave guides, microwaves, radar, radio||Issue Date:||2022||Publisher:||Nanyang Technological University||Source:||Zhao, Z. (2022). Exploration study of 60 GHz radar sensor for radar assisted living. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158098||Project:||P3044-202||Abstract:||Millimeter wave (mmWave) radar is an emerging technology with wide applications. As the world's population ages, there is an increasing demand for using mmWave radar for ambient assisted living, especially in living and working environment to provide care with independence for a growing elderly population. In addition, research show that there is a close relation between the sleep state of people and their physical and psychological health. then it is particularly important to pay attention to the sleep state of the elderly. The project is divided into 3 parts. The first part focuses on radar concepts, the knowledge of the radar hardware, radar environment setup and software installation and testing. The second part is radar data acquisition. I will use 60 GHz radar sensor to collect the data of the radar to the target and the action during simulated sleep, including lying down, lying on the side and rolling over etc. In the research of data visualization, I also used MATLAB to assist. The third part is processing and analyzing the radar data through python program. In this part we use convolutional neural networks (CNN) which is a type of deep learning model to analyze and test the data of actions. The ultimate goal is to realize the sleep motion recognition solution into the Raspberry Pi, eliminating the need for an external laptop or computer to increase portability realize the processing on the Raspberry Pi and improve portability. This report also contains the introduction of the Raspberry Pi.||URI:||https://hdl.handle.net/10356/158098||Schools:||School of Electrical and Electronic Engineering||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Student Reports (FYP/IA/PA/PI)|
Updated on Dec 10, 2023
Updated on Dec 10, 2023
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