Abstract
This paper describes SRCB-LUL team’s person identification system submitted to track 1 of the ICASSP 2023 Person Identification and Relapse Detection from Continuous Recordings of Biosignals (e-Prevention) challenge, which aims to identify the wearer of the smartwatch. At first, abnormal values of the multi-channel physiological signals are replaced, and the valid data are divided into multiple fine-grained fragments. Then, these fragments are fed into a 1D-CNN, and the user ID of a day is predicted through the voting of fragment results. Based on this framework, we train multiple base networks with different input lengths or different signal channels and aggregate these base networks for ensemble learning. The accuracy of our system is 96.16/95.00% on the validation/test set.
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