Gesture Recognition System Using 24 GHz FMCW Radar Sensor Realized on Real-Time Edge Computing Platform

Gan, Liangyu; Liu, Yuan; Li, Yanzhong; Zhang, Runxi; Huang, Leilei; Shi, Chunqi

doi:10.1109/jsen.2022.3163449

Cited by 13 publications

(2 citation statements)

References 30 publications

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“…More parameters of target motion such as distance, speed, and angle should be captured by Frequency-Modulated Continuous Wave (FMCW) radar via fixed slope frequency modulated signals. Therefore, FMCW-based gestures recognition has conducted widespread thorough research 31 – 35 . With the rapid development of machine learning and deep learning, machine learning algorithms and neural network models have been applied to gesture recognition researches.…”

Section: Introductionmentioning

confidence: 99%

Millimeter wave gesture recognition using multi-feature fusion models in complex scenes

Hao,

Sun,

et al. 2024

Sci Rep

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As a form of body language, the gesture plays an important role in smart homes, game interactions, and sign language communication, etc. The gesture recognition methods have been carried out extensively. The existing methods have inherent limitations regarding user experience, visual environment, and recognition granularity. Millimeter wave radar provides an effective method for the problems lie ahead gesture recognition because of the advantage of considerable bandwidth and high precision perception. Interfering factors and the complexity of the model raise an enormous challenge to the practical application of gesture recognition methods as the millimeter wave radar is applied to complex scenes. Based on multi-feature fusion, a gesture recognition method for complex scenes is proposed in this work. We collected data in variety places to improve sample reliability, filtered clutters to improve the signal’s signal-to-noise ratio (SNR), and then obtained multi features involves range-time map (RTM), Doppler-time map (DTM) and angle-time map (ATM) and fused them to enhance the richness and expression ability of the features. A lightweight neural network model multi-CNN-LSTM is designed to gestures recognition. This model consists of three convolutional neural network (CNN) for three obtained features and one long short-term memory (LSTM) for temporal features. We analyzed the performance and complexity of the model and verified the effectiveness of feature extraction. Numerous experiments have shown that this method has generalization ability, adaptability, and high robustness in complex scenarios. The recognition accuracy of 14 experimental gestures reached 97.28%.

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Section: Introductionmentioning

confidence: 99%

Millimeter wave gesture recognition using multi-feature fusion models in complex scenes

Hao,

Sun,

et al. 2024

Sci Rep

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“…Wang Y. et al [25] employed range-Doppler maps (RDMs) and range-angle maps (RAMs) as feature maps, which were fed into a dual 3D convolutional neural network for feature extraction and classification. Gan et al [26] gathered echo data using a 24 GHz radar, extracted the range and Doppler information of gestures, and input the data into a 3D CNN-LSTM for gesture classification. These studies provide valuable insights by focusing on individual TFM or SMS features, but the inherent interconnections within these features are only partially captured due to the separate treatment of each features, which limits the scope for a more holistic and deeper understanding of gesture recognition.…”

Section: Introductionmentioning

confidence: 99%

Robust Hand Gesture Recognition Using a Deformable Dual-Stream Fusion Network Based on CNN-TCN for FMCW Radar

Zhu,

Zhang,

Wang

et al. 2023

Sensors

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Hand Gesture Recognition (HGR) using Frequency Modulated Continuous Wave (FMCW) radars is difficult because of the inherent variability and ambiguity caused by individual habits and environmental differences. This paper proposes a deformable dual-stream fusion network based on CNN-TCN (DDF-CT) to solve this problem. First, we extract range, Doppler, and angle information from radar signals with the Fast Fourier Transform to produce range-time (RT) and range-angle (RA) maps. Then, we reduce the noise of the feature map. Subsequently, the RAM sequence (RAMS) is generated by temporally organizing the RAMs, which captures a target’s range and velocity characteristics at each time point while preserving the temporal feature information. To improve the accuracy and consistency of gesture recognition, DDF-CT incorporates deformable convolution and inter-frame attention mechanisms, which enhance the extraction of spatial features and the learning of temporal relationships. The experimental results show that our method achieves an accuracy of 98.61%, and even when tested in a novel environment, it still achieves an accuracy of 97.22%. Due to its robust performance, our method is significantly superior to other existing HGR approaches.

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Human Motion Detection Using Ultra-Wide Band Radar

Saini

Sarkar

Gaurav

et al. 2023

Communications in Computer and Information Science

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Gesture Recognition System Using 24 GHz FMCW Radar Sensor Realized on Real-Time Edge Computing Platform

Cited by 13 publications

References 30 publications

Millimeter wave gesture recognition using multi-feature fusion models in complex scenes

Millimeter wave gesture recognition using multi-feature fusion models in complex scenes

Robust Hand Gesture Recognition Using a Deformable Dual-Stream Fusion Network Based on CNN-TCN for FMCW Radar

Human Motion Detection Using Ultra-Wide Band Radar

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