Person Identification with Wearable Sensing Using Missing Feature Encoding and Multi-Stage Modality Fusion

Payal Mohapatra; Akash Pandey; Sinan Keten; Wei Chen; Qi Zhu

ICASSP 2023accepted0 citations

Person Identification with Wearable Sensing Using Missing Feature Encoding and Multi-Stage Modality Fusion

Payal Mohapatra, Akash Pandey, Sinan Keten, Wei Chen, Qi Zhu

Abstract

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.

BibTeX

@inproceedings{icassp2023_personidentifica,
  title = {Person Identification with Wearable Sensing Using Missing Feature Encoding and Multi-Stage Modality Fusion},
  author = {Payal Mohapatra and Akash Pandey and Sinan Keten and Wei Chen and Qi Zhu},
  booktitle = {ICASSP 2023},
  year = {2023}
}