Temporal-Range-Doppler Features Interpretation and Recognition of Hand Gestures Using mmW FMCW Radar Sensors

Zhang, Guiyuan; Lan, Shengchang; Zhang, Kang; Ye, Linting

doi:10.23919/eucap48036.2020.9135694

Cited by 15 publications

(7 citation statements)

References 10 publications

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“…The paper also investigated the impact of training data size on recognition accuracy. The proposed methods showed applicability in recognizing subtle finger motions, and preliminary experimental results were provided, comparing them with other baseline methods [20]. 2 provides a summary of the accuracy achieved by sign language recognition methods when utilizing input images.…”

Section: • Cnnmentioning

confidence: 99%

Untitled Document

Krishnan

2024

Preprint

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This paper presents a comprehensive review of the robustness of hand gesture recognition systems in low light intensity environments spanning the years 2018 to 2023. The primary objective is to assess the progress made during this period and identify areas requiring further attention. An extraction of 20 relevant journals from reputable online databases was conducted using selected keywords. Most of the reviewed articles delve into three crucial aspects of hand gesture recognition systems: data acquisition, data environment, and hand gesture representation. The system performance evaluation reveals that machine learning models achieve recognition accuracy between 94% and 98%, while computer vision models report accuracy within the range of 90% to 95%. The deep learning approach shows a broader accuracy range, spanning from 90% to 98%. Notably, the studies reviewed utilized datasets comprising 37 hand gestures, including 26 letters of American Sign Language (ASL) and numeric gestures ranging from 0 to 9. This paper sheds light on the current state of hand gesture recognition in low light environments and provides insights into potential opportunities for further research and development.

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Section: • Cnnmentioning

confidence: 99%

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Krishnan

2024

Preprint

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“…However, it also has a higher computational cost compared to CNN. In addition, there are many studies that have applied CNN-LSTM networks [31,76,77], which benefit from the strengths of both CNN and LSTM. It uses CNN to learn spatial features from input data and then feeds these features to LSTM to learn temporal patterns.…”

Section: Classification Algorithmsmentioning

confidence: 99%

Dynamic Gesture Recognition Based on FMCW Millimeter Wave Radar: Review of Methodologies and Results

Tang,

Wu,

2023

Sensors

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As a convenient and natural way of human-computer interaction, gesture recognition technology has broad research and application prospects in many fields, such as intelligent perception and virtual reality. This paper summarized the relevant literature on gesture recognition using Frequency Modulated Continuous Wave (FMCW) millimeter-wave radar from January 2015 to June 2023. In the manuscript, the widely used methods involved in data acquisition, data processing, and classification in gesture recognition were systematically investigated. This paper counts the information related to FMCW millimeter wave radar, gestures, data sets, and the methods and results in feature extraction and classification. Based on the statistical data, we provided analysis and recommendations for other researchers. Key issues in the studies of current gesture recognition, including feature fusion, classification algorithms, and generalization, were summarized and discussed. Finally, this paper discussed the incapability of the current gesture recognition technologies in complex practical scenes and their real-time performance for future development.

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“…However, that research requires a high computational device to implement it. Another approach has also been done by [6] which was using a higher frequency to increase the gesture detection probability. However, it was working on a shorter distance.…”

Section: Introductionmentioning

confidence: 99%

Finger Gesture Detection Experiment using Software Defined Radio Radar

Edwar,

Ali,

Feriska

et al. 2023

TELKA

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Human gesture detection for Human to Machine (H2M) Communication is usually done by using a camera. However, this device has a dependency of light illumination. To overcome this limitation, a radar can be utilized to read the finger gesture because it uses electromagnetic wave that is independent of the light appereance. Radar device is usually complicated and consists of components. In this research, a Software Defined Radio (SDR) was used to simplify the radar device circuit. The SDR generated the continuous wave (CW) signal that is suitable for detecting moving object such as finger gesture. The radar was working on 2.4 GHz of frequency. The results of the CW radar system using SDR shows that each gesture has a different reflected signal pattern, amplitude, and phases so we can conclude that each gesture has a character that can be distinguished through the characterization of reflected electromagnetic waves. This promising result can be the future application such as H2M) communication.

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Temporal-Range-Doppler Features Interpretation and Recognition of Hand Gestures Using mmW FMCW Radar Sensors

Cited by 15 publications

References 10 publications

Untitled Document

Untitled Document

Dynamic Gesture Recognition Based on FMCW Millimeter Wave Radar: Review of Methodologies and Results

Finger Gesture Detection Experiment using Software Defined Radio Radar

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