Human Similar Activity Recognition Using Millimeter-Wave Radar Based on CNN-BiLSTM and Class Activation Mapping
Human activity recognition (HAR) is an important field in the application of millimeter-wave radar. Radar-based HARs typically use Doppler signatures as primary data. However, some common similar human activities exhibit similar features that are difficult to distinguish. Therefore, the identificati...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Eng |
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| Online Access: | https://www.mdpi.com/2673-4117/6/3/44 |
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| author | Xiaochuan Wu Zengyi Ling Xin Zhang Zhanchao Ma Weibo Deng |
| author_facet | Xiaochuan Wu Zengyi Ling Xin Zhang Zhanchao Ma Weibo Deng |
| author_sort | Xiaochuan Wu |
| collection | DOAJ |
| description | Human activity recognition (HAR) is an important field in the application of millimeter-wave radar. Radar-based HARs typically use Doppler signatures as primary data. However, some common similar human activities exhibit similar features that are difficult to distinguish. Therefore, the identification of similar activities is a great challenge. Given this problem, a recognition method based on convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM), and class activation mapping (CAM) is proposed in this paper. The spectrogram is formed by processing the radar echo signal. The high-dimensional features are extracted by CNN, and then the corresponding feature vectors are fed into the BiLSTM to obtain the recognition results. Finally, the class activation mapping is used to visualize the decision recognition process of the model. Based on the data of four similar activities of different people collected by mm-wave radar, the experimental results show that the recognition accuracy of the proposed model reached 94.63%. Additionally, the output results of this model have strong robustness and generalization ability. It provides a new way to improve the accuracy of human similar posture recognition. |
| format | Article |
| id | doaj-art-a4d0bf58f77b49ac925d90b078246cf4 |
| institution | Kabale University |
| issn | 2673-4117 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Eng |
| spelling | doaj-art-a4d0bf58f77b49ac925d90b078246cf42025-08-20T03:43:37ZengMDPI AGEng2673-41172025-02-01634410.3390/eng6030044Human Similar Activity Recognition Using Millimeter-Wave Radar Based on CNN-BiLSTM and Class Activation MappingXiaochuan Wu0Zengyi Ling1Xin Zhang2Zhanchao Ma3Weibo Deng4School of Electronic Engineering, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Information and Communication Engineering, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Electronic Engineering, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Information and Communication Engineering, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Electronic Engineering, Harbin Institute of Technology, Harbin 150001, ChinaHuman activity recognition (HAR) is an important field in the application of millimeter-wave radar. Radar-based HARs typically use Doppler signatures as primary data. However, some common similar human activities exhibit similar features that are difficult to distinguish. Therefore, the identification of similar activities is a great challenge. Given this problem, a recognition method based on convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM), and class activation mapping (CAM) is proposed in this paper. The spectrogram is formed by processing the radar echo signal. The high-dimensional features are extracted by CNN, and then the corresponding feature vectors are fed into the BiLSTM to obtain the recognition results. Finally, the class activation mapping is used to visualize the decision recognition process of the model. Based on the data of four similar activities of different people collected by mm-wave radar, the experimental results show that the recognition accuracy of the proposed model reached 94.63%. Additionally, the output results of this model have strong robustness and generalization ability. It provides a new way to improve the accuracy of human similar posture recognition.https://www.mdpi.com/2673-4117/6/3/44mm-wave radarconvolutional neural networks (CNN)human similar activity recognition (HSAR)bidirectional long short-term memory (BiLSTM)class activation mapping (CAM) |
| spellingShingle | Xiaochuan Wu Zengyi Ling Xin Zhang Zhanchao Ma Weibo Deng Human Similar Activity Recognition Using Millimeter-Wave Radar Based on CNN-BiLSTM and Class Activation Mapping Eng mm-wave radar convolutional neural networks (CNN) human similar activity recognition (HSAR) bidirectional long short-term memory (BiLSTM) class activation mapping (CAM) |
| title | Human Similar Activity Recognition Using Millimeter-Wave Radar Based on CNN-BiLSTM and Class Activation Mapping |
| title_full | Human Similar Activity Recognition Using Millimeter-Wave Radar Based on CNN-BiLSTM and Class Activation Mapping |
| title_fullStr | Human Similar Activity Recognition Using Millimeter-Wave Radar Based on CNN-BiLSTM and Class Activation Mapping |
| title_full_unstemmed | Human Similar Activity Recognition Using Millimeter-Wave Radar Based on CNN-BiLSTM and Class Activation Mapping |
| title_short | Human Similar Activity Recognition Using Millimeter-Wave Radar Based on CNN-BiLSTM and Class Activation Mapping |
| title_sort | human similar activity recognition using millimeter wave radar based on cnn bilstm and class activation mapping |
| topic | mm-wave radar convolutional neural networks (CNN) human similar activity recognition (HSAR) bidirectional long short-term memory (BiLSTM) class activation mapping (CAM) |
| url | https://www.mdpi.com/2673-4117/6/3/44 |
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