Abstract
Activity recognition methods using Wi-Fi Channel State Information (CSI) have been actively studied in the mobile and ubiquitous computing community. Many prior studies on CSI-based context recognition systems employ CSI data collected at a high and constant sampling rate, resulting in always high computation costs for context recognition. In this study, we propose a CSI-based activity recognition method that adaptively adjusts the sampling rate using reinforcement learning. In the proposed method, the “action” in the reinforcement learning is defined as the selection of a sampling rate of CSI, and the “state” is defined as an intermediate output of a neural network for activity recognition in the environment, which is expected to include information describing the complexity of the current activity. Moreover, we design an activity recognition model that can accept CSI inputs collected at an arbitrary sampling rate in principle, and extract sampling-rate-independent intermediate representations in its intermediate layers, enabling the reinforcement learning agent to switch to an appropriate sampling rate regardless of the current sampling rate. We evaluated the proposed approach using data collected in real environments.
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References
Halperin, D., Hu, W., Sheth, A., Wetherall, D.: Tool release: gathering 802.11n traces with channel state information. Comput. Commun. Rev. 41(1), 53 (2011). https://doi.org/10.1145/1925861.1925870
Hessel, M., et al.: Rainbow: combining improvements in deep reinforcement learning. CoRR abs/1710.02298 (2017). http://arxiv.org/abs/1710.02298
Mnih, V., et al.: Playing atari with deep reinforcement learning. Computing Research Repository, CoRR abs/1312.5602 (2013). http://arxiv.org/abs/1312.5602
Ohara, K., Maekawa, T., Matsushita, Y.: Detecting state changes of indoor everyday objects using wi-fi channel state information. In: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, IMWUT, vol. 1, no. 3, pp. 88:1–88:28 (2017)
Ravi, N., Dandekar, N., Mysore, P., Littman, M.L.: Activity recognition from accelerometer data. In: The Twentieth National Conference on Artificial Intelligence and the Seventeenth Innovative Applications of Artificial Intelligence Conference, pp. 1541–1546 (2005)
Ulutan, O., Rallapalli, S., Srivatsa, M., Manjunath, B.S.: Actor conditioned attention maps for video action detection. Computing Research Repository, CoRR abs/1812.11631 (2018). http://arxiv.org/abs/1812.11631
Wang, W., Liu, A.X., Shahzad, M., Ling, K., Lu, S.: Device-free human activity recognition using commercial WiFi devices. IEEE J. Sel. Areas Commun. 35(5), 1118–1131 (2017). https://doi.org/10.1109/JSAC.2017.2679658
Wang, Y., Liu, J., Chen, Y., Gruteser, M., Yang, J., Liu, H.: E-eyes: device-free location-oriented activity identification using fine-grained WiFi signatures. In: The 20th Annual International Conference on Mobile Computing and Networking, MobiCom 2014, pp. 617–628 (2014). https://doi.org/10.1145/2639108.2639143
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This study is partially supported by JSPS JP16H06539 and JP21H03428.
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Tanno, Y., Maekawa, T., Hara, T. (2022). Wi-Fi CSI-Based Activity Recognition with Adaptive Sampling Rate Selection. In: Hara, T., Yamaguchi, H. (eds) Mobile and Ubiquitous Systems: Computing, Networking and Services. MobiQuitous 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-030-94822-1_31
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DOI: https://doi.org/10.1007/978-3-030-94822-1_31
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