SE-WiGR: A WiFi Gesture Recognition Approach Incorporating the Squeeze–Excitation Mechanism and VGG16
Source abstract
Abstract
With advancements in IoT and smart home tech, WiFi-driven gesture recognition is attracting more focus due to its non-contact nature and user-friendly design. However, WiFi signals are affected by multipath effects, attenuation, and interference, resulting in complex and variable signal patterns that pose challenges for accurately modeling gesture characteristics. This study proposes SE-WiGR, an innovative WiFi gesture recognition method to address these challenges. First, channel state information (CSI) related to gesture actions is collected using commercial WiFi devices. Next, the data is preprocessed, and Doppler-shift image data is extracted as input for the network model. Finally, the method integrates the squeeze-and-excitation (SE) mechanism with the VGG16 network to classify gestures. The method achieves a recognition accuracy of 94.12% across multiple scenarios, outperforming the standalone VGG16 network by 4.13%. This improvement confirms that the SE module effectively enhances gesture feature extraction while suppressing background noise.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Hand Gesture Recognition Systems · Indoor and Outdoor Localization Technologies · Speech and Audio Processing
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Information Engineering, Gansu Minzu Normal University, Hezuo 747000, China
School of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China
Funding
National Natural Science Foundation of China · Natural Science Foundation of Gansu Province
References (29)
Show source-provided references
1. Wi-TCG: A WiFi gesture recognition method based on transfer learning and conditional generative adversarial networks10.1088/2631-8695/ad9981 ↗
2. Chiu, P., Kim, C., and Oda, H. (2018, January 25–28). Recognizing gestures on projected button widgets with an RGB-D camera using a CNN. Proceedings of the 2018 ACM International Conference on Interactive Surfaces and Spaces, Tokyo, Japan.10.1145/3279778.3279907 ↗
3. Matching of visible and infrared images based on Cy-cleGAN-SIFT10.37188/OPE.20223005.0602 ↗
4. Dang, X., Ke, W., Hao, Z., Jin, P., Deng, H., and Sheng, Y. (2023). mm-TPG: Traffic policemen gesture recognition based on millimeter wave radar point cloud. Sensors, 23.10.3390/s23156816 ↗
5. Human activity recognition with smartphone and wearable sensors using deep learning techniques: A review10.1109/JSEN.2021.3069927 ↗
6. (2009). IEEE Standard for Information Technology—Local and Metropolitan Area Networks—Specific Requirements—Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications Amendment 5: Enhancements for Higher Throughput (Standard No. IEEE 802.11n-2009).
7. A novel deep learning Bi-GRU-I model for real-time human activity recognition using inertial sensors10.1109/JSEN.2022.3148431 ↗
8. Pu, Q., Gupta, S., Gollakota, S., and Patel, S. (October, January 30). Whole-home gesture recognition using wireless signals. Proceedings of the 19th Annual International Conference on Mobile Computing & Networking—MobiCom’13, Miami, FL, USA.
9. WiMorse: A contactless morse code text input system using ambient WiFi signals10.1109/JIOT.2019.2934904 ↗
10. Hao, Z., Duan, Y., Dang, X., Liu, Y., and Zhang, D. (2020). Wi-SL: Contactless fine-grained gesture recognition uses channel state information. Sensors, 20.10.3390/s20144025 ↗
11. WiReader: Adaptive air handwriting recognition based on commercial WiFi signal10.1109/JIOT.2020.2997053 ↗
12. Abdelnasser, H., Youssef, M., and Harras, K.A. (May, January 26). Wigest: A ubiquitous WiFi-based gesture recognition system. Proceedings of the 2015 IEEE Conference on Computer Communications (INFOCOM), Hong Kong.10.1109/INFCOMW.2015.7179321 ↗
13. Gu, Y., Quan, L., and Ren, F. (2014, January 28–30). Wifi-assisted human activity recognition. Proceedings of the 2014 IEEE Asia Pacific Conference on Wireless and Mobile, Bali, Indonesia.
14. Device-free single-user activity recognition using diversified deep ensemble learning10.1016/j.asoc.2020.107066 ↗
15. He, W., Wu, K., Zou, Y., and Ming, Z. (2015, January 3–6). WiG: WiFi-based gesture recognition system. Proceedings of the 2015 24th International Conference on Computer Communication and Networks (ICCCN), Las Vegas, NV, USA.10.1109/ICCCN.2015.7288485 ↗
16. WiGeR: WiFi-based gesture recognition system10.3390/ijgi5060092 ↗
17. Virmani, A., and Shahzad, M. (2017, January 19–23). Position and orientation agnostic gesture recognition using WiFi. Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services, Niagara Falls, NY, USA.10.1145/3081333.3081340 ↗
18. AirFi: Empowering WiFi-based passive human gesture recognition to unseen environment via domain generalization
19. Gesture recognition system based on cross-domain CSI extracted from Wi-Fi devices combined with the 3D CNN10.1007/s11760-023-02545-8 ↗
20. Zou, H., Zhou, Y., Yang, J., Jiang, H., and Spanos, C.J. (2018, January 12–15). WiFi-enabled device-free gesture recognition for smart home automation. Proceedings of the 2018 IEEE 14th International Conference on Control and Automation (ICCA), Anchorage, AK, USA.10.1109/ICCA.2018.8444331 ↗
21. Continuous authentication through finger gesture interaction for smart homes using WiFi10.1109/TMC.2020.2994955 ↗
22. A survey on behavior recognition using WiFi channel state information10.1109/MCOM.2017.1700082 ↗
23. WiFi CSI based passive human activity recognition using attention based BLSTM10.1109/TMC.2018.2878233 ↗
24. Zhou, Z., Liu, C., Yu, X., Yang, C., Duan, P., and Cao, Y. (2019, January 19–23). Deep-WiID: WiFi-based contactless human identification via deep learning. Proceedings of the 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), Leicester, UK.10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00179 ↗
25. AI-based malware detection in IoT networks within smart cities: A survey10.1016/j.comcom.2025.108055 ↗
26. CrowdWatch: Privacy-Preserving Monitoring Leveraging Wi-Fi Multiple Access Information10.1109/JIOT.2025.3528226 ↗
27. Daniş, F.S. (2022, January 5–7). Live rssi filtering for indoor positioning with bluetooth low-energy. Proceedings of the 2022 IEEE 12th International Conference on Indoor Positioning and Indoor Navigation (IPIN), Beijing, China.10.1109/IPIN54987.2022.9918138 ↗
28. SenseFi: A library and benchmark on deep-learning-empowered WiFi human sensing10.1016/j.patter.2023.100703 ↗
29. Wang, H., Wang, Q., Huang, L., Shi, C., and Zhang, R. (2024, January 19–22). A PCA acceleration algorithm for WiFi sensing and its hardware implementation. Proceedings of the 2024 IEEE International Symposium on Circuits and Systems (ISCAS), Singapore.10.1109/ISCAS58744.2024.10557859 ↗