Abstract
Wireless signal-based device-free human behavior sensing is an innovative method for accurate sensing and understanding human behaviors, which is the core technology to enable high level human computer interaction via off-the-shelf IoT devices. Currently, tremendous on-site human behavior sensing methods with wireless signals collected from IoT devices were proposed to use for fall detection, daily behaviors sensing, finger gesture recognition and other potential applications. However, wireless signal-based human behavior sensing often occurs in different indoor environments with different structures, room sizes, and obstacles. Therefore, it is difficult to get the empirical parameters for accurate human behavior sensing in practical use, the history data presented in current works also could not fit all the scenarios. In order to resolve this issue, this paper proposes FreeSee, a parameter-independent pattern-based human behaviour sensing system, the main contributions include i) we added time-domain features to the training data to accurate sense human behaviours both by coarse-grained and fine-grained wireless signatures; ii) we extract the dominant parameters from each module as the decision variables; and iii) we propose to use a genetic algorithm (GA) to find the optimized parameters for accurate human behaviour sensing which could be adapted in the multiple scenarios. Experimental results show that FreeSee could optimize the parameters in decision variables according to different datasets with accepted converge time.
Supported by Science and Technology Project of Jilin Provincial Department of Education (JJKH20210457KJ), Undergraduate Training Programs for Innovation and Entrepreneurship Project of Jilin Province (2021JLSFDX-JSJ03) and Innovation capacity building Foundation of Jilin Provincial Development and Reform Commission, grant number 2021C038-7.
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Sun, H., Zhang, X., Lu, Y., Chen, CL., Song, X. (2022). FreeSee: A Parameter-Independent Pattern-Based Device-Free Human Behaviour Sensing System with Wireless Signals of IoT Devices. In: Li, B., et al. Advanced Data Mining and Applications. ADMA 2022. Lecture Notes in Computer Science(), vol 13087. Springer, Cham. https://doi.org/10.1007/978-3-030-95405-5_23
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