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 91.36% on test data. Additionally, our experiments confirm findings from previous works that kinetic features derived from the accelerometer in-deed contain more discriminative features for person identification task.