Next Article in Journal
STASISM: A Versatile Serious Gaming Multi-Sensor Platform for Personalized Telerehabilitation and Telemonitoring
Previous Article in Journal
Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sensors
Volume 24
Issue 2
10.3390/s24020349
sensors-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Hand Gesture Recognition Using FSK Radar Sensors

by
Kimoon Yang
1,2,
Minji Kim
1,2,
Yunho Jung
3,4 and
Seongjoo Lee
2,5,*
1
Department Semiconductor Systems Engineering, Sejong University, Gunja-dong, Gwangjin-gu, Seoul 05006, Republic of Korea
2
Department of Convergence Engineering of Intelligent Drone, Sejong University, Gunja-dong, Gwangjin-gu, Seoul 05006, Republic of Korea
3
Department of Smart Drone Convergence, Korea Aerospace University, Goyang 10540, Gyeonggi-do, Republic of Korea
4
School of Electronics and Information Engineering, Korea Aerospace University, Goyang 10540, Gyeonggi-do, Republic of Korea
5
Department Electrical Engineering, Sejong University, Gunja-dong, Gwangjin-gu, Seoul 05006, Republic of Korea
*
Author to whom correspondence should be addressed.
Sensors 2024, 24(2), 349; https://doi.org/10.3390/s24020349
Submission received: 8 December 2023 / Revised: 25 December 2023 / Accepted: 5 January 2024 / Published: 6 January 2024
(This article belongs to the Section Radar Sensors)
Browse Figures
Versions Notes

Abstract

Hand gesture recognition, which is one of the fields of human–computer interaction (HCI) research, extracts the user’s pattern using sensors. Radio detection and ranging (RADAR) sensors are robust under severe environments and convenient to use for hand gestures. The existing studies mostly adopted continuous-wave (CW) radar, which only shows a good performance at a fixed distance, which is due to its limitation of not seeing the distance. This paper proposes a hand gesture recognition system that utilizes frequency-shift keying (FSK) radar, allowing for a recognition method that can work at the various distances between a radar sensor and a user. The proposed system adopts a convolutional neural network (CNN) model for the recognition. From the experimental results, the proposed recognition system covers the range from 30 cm to 180 cm and shows an accuracy of 93.67% over the entire range.
Keywords: human–computer interaction; hand gesture recognition; micro-Doppler signature; FSK radar; Doppler radar; convolutional neural network; data preprocessing human–computer interaction; hand gesture recognition; micro-Doppler signature; FSK radar; Doppler radar; convolutional neural network; data preprocessing

Share and Cite

MDPI and ACS Style

Yang, K.; Kim, M.; Jung, Y.; Lee, S. Hand Gesture Recognition Using FSK Radar Sensors. Sensors 2024, 24, 349. https://doi.org/10.3390/s24020349

AMA Style

Yang K, Kim M, Jung Y, Lee S. Hand Gesture Recognition Using FSK Radar Sensors. Sensors. 2024; 24(2):349. https://doi.org/10.3390/s24020349

Chicago/Turabian Style

Yang, Kimoon, Minji Kim, Yunho Jung, and Seongjoo Lee. 2024. "Hand Gesture Recognition Using FSK Radar Sensors" Sensors 24, no. 2: 349. https://doi.org/10.3390/s24020349

APA Style

Yang, K., Kim, M., Jung, Y., & Lee, S. (2024). Hand Gesture Recognition Using FSK Radar Sensors. Sensors, 24(2), 349. https://doi.org/10.3390/s24020349

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop