Abstract
Human activity recognition based on radio frequency identification (RFID) has become an essential part of Internet of Things. At present, most RFID-based human activity recognition research is focused on domain-specific recognition. However, existing solutions usually match global features for domain adaptation when solving cross-domain problems, and lack consideration of untransferable features, which lead to degradation of recognition accuracy. This paper proposes a novel human activity recognition model TAHAR which adapts transferable attention and adversarial learning to eliminate the negative influence of untransferable features and domain-specific features. In TAHAR, the feature extractor extracts spatio-temporal information of phase and Received Signal Strength Indicator from the RFID signal. Then we utilize an attention module to weight features to minimize the influence of untransferable features and negative transfer. Additionally, discriminators and batch spectral penalization are used to remove domain-specific information, thereby enhancing the transferability and discriminability. Results show that TAHAR achieves an accuracy and recall of 90.17% and 85.46% respectively, achieving an outstanding performance compared with several state-of-the-art methods.
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References
Chen, K., Zhang, D., Yao, L., Guo, B., Yu, Z., Liu, Y.: Deep learning for sensor-based human activity recognition: overview, challenges, and opportunities. ACM Comput. Surv. 54(4), 1–40 (2021). https://doi.org/10.1145/3447744
Han, M., et al.: Dual-AI: dual-path actor interaction learning for group activity recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2990–2999 (2022)
Li, X., Zhang, Y., Marsic, I., Sarcevic, A., Burd, R.S.: Deep learning for rfid-based activity recognition. In: Proceedings of the 14th ACM Conference on Embedded Network Sensor Systems CD-ROM, pp. 164–175 (2016)
Liang, R.-H., Yang, S.-Y., Chen, B.-Y.: Indexmo: exploring finger-worn rfid motion tracking for activity recognition on tagged objects. In: Proceedings of the 23rd International Symposium on Wearable Computers, pp. 129–134 (2019)
Wang, F., Liu, J., Gong, W.: Multi-adversarial in-car activity recognition using rfids. IEEE Trans. Mob. Comput. 20(6), 2224–2237 (2020)
Yu, Y., Wang, D., Zhao, R., Zhang, Q.: RFID based real-time recognition of ongoing gesture with adversarial learning,” in Proceedings of the 17th Conference on Embedded Networked Sensor Systems, 2019, pp. 298–310
Chen, X., Wang, S., Long, M., Wang, J.: Transferability vs. discriminability: Batch spectral penalization for adversarial domain adaptation. In: International Conference on Machine Learning. PMLR, pp. 1081–1090 (2019)
Liu, Z., Liu, X., Li, K.: Deeper exercise monitoring for smart gym using fused rfid and cv data. In: IEEE INFOCOM 2020-IEEE Conference on Computer Communications, pp. 11–19. IEEE (2020)
Ma, Z., Zhang, S., Liu, J., Liu, X., Wang, W., Wang, J., Guo, S.: RF-SIAMESE: approaching accurate RFID gesture recognition with one sample. IEEE Trans. Mob. Comput. 2022, 1–15 (2022). https://doi.org/10.1109/TMC.2022.3217487
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Goodfellow, I., et al.: Generative adversarial networks. Commun. ACM 63(11), 139–144 (2020)
Zhang, C., Zhao, Q., Wang, Y.: Transferable attention networks for adversarial domain adaptation. Inf. Sci. 539, 422–433 (2020)
Zhao, M., Yue, S., Katabi, D., Jaakkola, T.S., Bianchi, M.T.: Learning sleep stages from radio signals: a conditional adversarial architecture. In: International Conference on Machine Learning, pp. 4100–4109. PMLR (2017)
Itoh, K.: Analysis of the phase unwrapping algorithm. Appl. Opt. 21(14), 2470 (1982)
Savitzky, A., Golay, M.J.: Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36(8), 1627–1639 (1964)
Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: International Conference on Machine Learning, pp. 1180–1189. PMLR (2015)
Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7167–7176 (2017)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Hearst, M.A., Dumais, S.T., Osuna, E., Platt, J., Scholkopf, B.: Support vector machines. IEEE Intell. Syst. Appl. 13(4), 18–28 (1998)
Donahue, J., et al.: Decaf: A deep convolutional activation feature for generic visual recognition. In: International Conference on Machine Learning, pp. 647–655. PMLR (2014)
Acknowledgements
This paper was supported by the 2021 Fujian Foreign Cooperation Project (No. 202110001): Research on Human Behavior Recognition Based on RFID and Deep Learning; 2021 Project of Xiamen University (No. 20213160A0474): Zijin International Digital Operation Platform Research and Consulting; State Key Laboratory of Process Automation in Mining & Metallurgy, Beijing Key Laboratory of Process Automation in Mining & Metallurgy (No. BGRIMM-KZSKL-2022-14): Research and application of mine operator positioning based on RFID and deep learning; National Key R&D Program of China-Sub-project of Major Natural Disaster Monito ing, Early Warning and Prevention (No. 2020YFC1522604): Research on key technologies of comprehensive information application platform for cultural relic safety based on big data technology.
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Chen, D., Yang, L., Cao, H., Wang, Q., Dong, W., Yu, B. (2023). TAHAR: A Transferable Attention-Based Adversarial Network for Human Activity Recognition with RFID. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14087. Springer, Singapore. https://doi.org/10.1007/978-981-99-4742-3_20
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