Deep Multitask Learning for Pervasive BMI Estimation and Identity Recognition in Smart Beds

TL;DR

This paper presents a deep multitask learning approach using pressure sensor data from smart beds to simultaneously estimate BMI and recognize user identity, enhancing unobtrusive health monitoring and security.

Contribution

It introduces a unified deep neural network framework that outperforms existing methods in BMI estimation and user identification using pressure data from textile sensors.

Findings

Outperforms prior methods and benchmarks

Achieves accurate BMI estimation in cross-validation

Effective user identification across different positions

Abstract

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.