Bibliographic Details
| Title: |
Deep Multitask Learning for Pervasive BMI Estimation and Identity Recognition in Smart Beds |
| Authors: |
Davoodnia, Vandad, Slinowsky, Monet, Etemad, Ali |
| Source: |
Journal of Ambient Intelligence and Humanized Computing 14 (2023) 5463-5477 |
| Publication Year: |
2020 |
| Collection: |
Computer Science |
| Subject Terms: |
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Machine Learning |
| More Details: |
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.
Comment: This is a pre-print of an article published in journal of Ambient Intelligence and Humanized Computing. The final authenticated version is available online at: https://doi.org/10.1007/s12652-020-02210-9 |
| Document Type: |
Working Paper |
| DOI: |
10.1007/s12652-020-02210-9 |
| Access URL: |
http://arxiv.org/abs/2006.10453 |
| Accession Number: |
edsarx.2006.10453 |
| Database: |
arXiv |
