Gesture Recognition with Residual LSTM Attention Using Millimeter-Wave Radar
Gesture recognition technology based on millimeter-wave radar can recognize and classify user gestures in non-contact scenarios. To address the complexity of data processing with multi-feature inputs in neural networks and the poor recognition performance with single-feature inputs, this paper propo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/2/469 |
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| author | Weiqing Bai Siyu Chen Jialiang Ma Ying Wang Chong Han |
| author_facet | Weiqing Bai Siyu Chen Jialiang Ma Ying Wang Chong Han |
| author_sort | Weiqing Bai |
| collection | DOAJ |
| description | Gesture recognition technology based on millimeter-wave radar can recognize and classify user gestures in non-contact scenarios. To address the complexity of data processing with multi-feature inputs in neural networks and the poor recognition performance with single-feature inputs, this paper proposes a gesture recognition algorithm based on <b>R</b>esNet <b>L</b>ong Short-Term Memory with an <b>A</b>ttention Mechanism (RLA). In the aspect of signal processing in RLA, a range–Doppler map is obtained through the extraction of the range and velocity features in the original mmWave radar signal. Regarding the network architecture in RLA, the relevant features of the residual network with channel and spatial attention modules are combined to prevent some useful information from being neglected. We introduce a residual attention mechanism to enhance the network’s focus on gesture features and avoid the impact of irrelevant features on recognition accuracy. Additionally, we use a long short-term memory network to process temporal features, ensuring high recognition accuracy even with single-feature inputs. A series of experimental results show that the algorithm proposed in this paper has higher recognition performance. |
| format | Article |
| id | doaj-art-b23a00b880974fe3a089d4aac1e4fcf5 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-b23a00b880974fe3a089d4aac1e4fcf52025-01-24T13:49:03ZengMDPI AGSensors1424-82202025-01-0125246910.3390/s25020469Gesture Recognition with Residual LSTM Attention Using Millimeter-Wave RadarWeiqing Bai0Siyu Chen1Jialiang Ma2Ying Wang3Chong Han4College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaCollege of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaCollege of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaCollege of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaCollege of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaGesture recognition technology based on millimeter-wave radar can recognize and classify user gestures in non-contact scenarios. To address the complexity of data processing with multi-feature inputs in neural networks and the poor recognition performance with single-feature inputs, this paper proposes a gesture recognition algorithm based on <b>R</b>esNet <b>L</b>ong Short-Term Memory with an <b>A</b>ttention Mechanism (RLA). In the aspect of signal processing in RLA, a range–Doppler map is obtained through the extraction of the range and velocity features in the original mmWave radar signal. Regarding the network architecture in RLA, the relevant features of the residual network with channel and spatial attention modules are combined to prevent some useful information from being neglected. We introduce a residual attention mechanism to enhance the network’s focus on gesture features and avoid the impact of irrelevant features on recognition accuracy. Additionally, we use a long short-term memory network to process temporal features, ensuring high recognition accuracy even with single-feature inputs. A series of experimental results show that the algorithm proposed in this paper has higher recognition performance.https://www.mdpi.com/1424-8220/25/2/469millimeter-wave radargesture recognitionsignal preprocessingdeep learning |
| spellingShingle | Weiqing Bai Siyu Chen Jialiang Ma Ying Wang Chong Han Gesture Recognition with Residual LSTM Attention Using Millimeter-Wave Radar Sensors millimeter-wave radar gesture recognition signal preprocessing deep learning |
| title | Gesture Recognition with Residual LSTM Attention Using Millimeter-Wave Radar |
| title_full | Gesture Recognition with Residual LSTM Attention Using Millimeter-Wave Radar |
| title_fullStr | Gesture Recognition with Residual LSTM Attention Using Millimeter-Wave Radar |
| title_full_unstemmed | Gesture Recognition with Residual LSTM Attention Using Millimeter-Wave Radar |
| title_short | Gesture Recognition with Residual LSTM Attention Using Millimeter-Wave Radar |
| title_sort | gesture recognition with residual lstm attention using millimeter wave radar |
| topic | millimeter-wave radar gesture recognition signal preprocessing deep learning |
| url | https://www.mdpi.com/1424-8220/25/2/469 |
| work_keys_str_mv | AT weiqingbai gesturerecognitionwithresiduallstmattentionusingmillimeterwaveradar AT siyuchen gesturerecognitionwithresiduallstmattentionusingmillimeterwaveradar AT jialiangma gesturerecognitionwithresiduallstmattentionusingmillimeterwaveradar AT yingwang gesturerecognitionwithresiduallstmattentionusingmillimeterwaveradar AT chonghan gesturerecognitionwithresiduallstmattentionusingmillimeterwaveradar |