Wei Sun1, Zhanhe Du2,*
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1181-1195, 2023, DOI:10.32604/iasc.2023.037497
Abstract Currently, it is difficult to extract the depth feature of the frontal emergency stops dangerous activity signal, which leads to a decline in the accuracy and efficiency of the frontal emergency stops the dangerous activity. Therefore, a recognition for frontal emergency stops dangerous activity algorithm based on Nano Internet of Things Sensor (NIoTS) and transfer learning is proposed. First, the NIoTS is installed in the athlete’s leg muscles to collect activity signals. Second, the noise component in the activity signal is removed using the de-noising method based on mathematical morphology. Finally, the depth feature of the activity signal is extracted… More >
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