Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/153497
Title: Touchless detection and classification of finger actions using radar sensor and machine learning
Authors: Shen, Chen
Keywords: Engineering::Electrical and electronic engineering::Antennas, wave guides, microwaves, radar, radio
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Shen, C. (2021). Touchless detection and classification of finger actions using radar sensor and machine learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/153497
Project: P3021-201
Abstract: With the increasing threat of viruses to people in today's society, there is a growing expectation that we can minimize various contact actions in our daily lives, reducing unnecessary contact and thus reducing the spread of the virus and reducing the chance of infection. In this project, we will explore the use of low-cost millimeter-wave radar to remotely sense the corresponding gestures to replace some popular finger touch gestures, and then analyze and filter the collected gestures through the MATLAB program and convert them to the corresponding gestures. The action instructions are output to the operation panel and executed, which can then form a complete set of non-contact operating systems to provide convenience for people's daily lives. For example, it can be used for simple on/off buttons, and it can also be used for digital buttons for more complex applications, such as replacing traditional elevator buttons to enter buildings. This report focuses on how to use millimeter-wave radar to capture gestures. After the gesture signals are obtained, the captured gestures can be analyzed and filtered through the MATLAB program. The MATLAB program for analyzing gestures will mainly be applied to the Convolutional Neural Network (CNN). Technology to establish a recognition system, an important part of which is the confusion matrix algorithm. Its function is to run a series of recognition and calculations on the collected gesture signals, thereby improving the accuracy of recognizing gesture signals.
URI: https://hdl.handle.net/10356/153497
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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