Abstract
Aiming at the problem of pedestrian navigation motion gait classification and recognition, this paper proposed a pedestrian gait recognition method driven by inertial data. Firstly, human motion gait features are analyzed, and then the smallest characteristic parameters extraction method for gait recognition is given in the case of only using acceleration time-domain information. Furthermore, multiple motion gait classifier based on SVM is designed, and human motion gait is recognized by setting the extracted gait characteristic parameters as input of the SVM classifier. On this basis, four kinds of typical motion gait include walking, running, going up the stairs and going down the stairs are tested. The results show that the acceleration time-domain information can effectively identify human motion gait types, and the recognition accuracy is more than 88%, especially, the recognition accuracy is 100% in distinguishing between running and other three movement gait.
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This work is supported by National Natural Science Foundation of China (NSFC) under grant No. 62003050 and 62003051.
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Tian, X., Luo, X., Zhang, L. (2023). A Pedestrian Gait Recognition Method Driven by Inertial Data. In: Yan, L., Duan, H., Deng, Y. (eds) Advances in Guidance, Navigation and Control. ICGNC 2022. Lecture Notes in Electrical Engineering, vol 845. Springer, Singapore. https://doi.org/10.1007/978-981-19-6613-2_497
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DOI: https://doi.org/10.1007/978-981-19-6613-2_497
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