  Smart devices in the Internet of Things (IoT) paradigm provide a variety of
unobtrusive and pervasive means for continuous monitoring of bio-metrics and
health information. Furthermore, automated personalization and authentication
through such smart systems can enable better user experience and security. In
this paper, simultaneous estimation and monitoring of body mass index (BMI) and
user identity recognition through a unified machine learning framework using
smart beds is explored. To this end, we utilize pressure data collected from
textile-based sensor arrays integrated onto a mattress to estimate the BMI
values of subjects and classify their identities in different positions by
using a deep multitask neural network. First, we filter and extract 14 features
from the data and subsequently employ deep neural networks for BMI estimation
and subject identification on two different public datasets. Finally, we
demonstrate that our proposed solution outperforms prior works and several
machine learning benchmarks by a considerable margin, while also estimating
users' BMI in a 10-fold cross-validation scheme.
