PERSON IDENTIFICATION WITH WEARABLE SENSING USING MISSING FEATURE ENCODING AND MULTI-STAGE MODALITY FUSION
Payal Mohapatra (Northwestern University); Akash Pandey (Northwestern University ); Sinan Keten (Northwestern University); Wei Chen (" Northwestern University, UK"); Zhu Qi (Northwestern University)
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10 Jun 2023
We present a missingness aware fusion network(MAFN) to identify a person’s digital phenotype from continuously measured longitudinal multi-modal wearable data. This work is done as a part of Track 1 of e-Prevention: Person Identification and Relapse Detection from Continuous Recordings of Biosignals Signal Processing Grand Challenge at International Conference on Acoustics, Speech, & Signal Processing (ICASSP) 2023. MAFN achieves an accuracy of 91.37% on
test data against a baseline of 62%.