Feature-Based Hand Gesture Recognition Using Two-Antenna Doppler Radar System

Wang, Pengcheng; Liang, Tingxuan; Xu, Hongtao

doi:10.1109/iws55252.2022.9977736

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…Similar to the analysis of feature fusion methods in Section 4.2, due to the different experimental conditions of classification algorithms, we cannot judge the advantages and disadvantages of different classification methods by directly comparing the results of different studies, but we can find the common values or rules from the statistics. The widely used machine learning methods in gesture recognition are support vector machine (SVM) [27,73], K-nearest neighbor method (KNN) [66], and hidden Markov model (HMM) [74]. These methods are easy to implement but lack robustness and computational efficiency.…”

Section: Classification Algorithmsmentioning

confidence: 99%

“…Additional studies have enhanced the DTW algorithm for gesture recognition by adding path constraints and refining the matching procedure for gesture recognition [81]. According to the data in statistics [27,44,66,73,75], it can be found that machine learning algorithms are more suitable for problems with simple gestures, small sample categories, and quantity in gesture recognition, and deep learning algorithms tend to have better performance in gesture recognition problems with complex gestures and a larger number of samples.…”

Section: Classification Algorithmsmentioning

confidence: 99%

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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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