论文标题
深度多任务学习，用于智能床中普遍存在的BMI估计和身份识别
Deep Multitask Learning for Pervasive BMI Estimation and Identity Recognition in Smart Beds
论文作者
论文摘要
物联网（IoT）范式中的智能设备提供了各种不引人注目的和普遍的手段，用于持续监视生物光学和健康信息。此外，通过此类智能系统自动化的个性化和身份验证可以实现更好的用户体验和安全性。在本文中，探索了使用智能床的统一机器学习框架对体重指数（BMI）和用户身份识别的同时估计和监测。为此，我们利用从集成到床垫上的基于纺织的传感器阵列收集的压力数据来估计受试者的BMI值，并通过使用深层多任务神经网络在不同位置对其身份进行分类。首先，我们从数据中过滤和提取14个功能，然后在两个不同的公共数据集上使用深层神经网络进行BMI估计和主题识别。最后，我们证明我们提出的解决方案的表现优于先前的工作和几个机器学习基准，同时还可以在10倍的交叉验证方案中估算用户的BMI。
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.
