強縦断的な生体医学信号の深層学習と健康関連の人工知能応用

在2021财年，我们经历了一项富有成效的研究成果。我们成功发表了一篇概述论文，主题是利用人工智能和物联网技术改善老龄化社会。这篇论文已经由著名的IEEE信号处理杂志发表，本身就可以为广大社区提供很好的指导。此外，这篇综述论文总结了从我们的研究项目中学到的主要思想和/或技术。我们使用物联网传感器数据来监测老年人(独居)的身体和精神状态，这被发现是有效的，并发表在IEEE物联网杂志上。此外，我们还发表了关于使用最先进的计算机听力(CA)技术来衡量新冠肺炎患者健康状况的论文。这项研究结果发表在IEEE物联网杂志上。一种用于监测司机昏昏欲睡程度的多模式学习系统被顶级期刊IEEE智能交通系统学报接受并在线发表。IEEE《生物医学与健康信息学杂志》发表了一篇系统综述，介绍了用于鼾声定位的CA方法。此外，我们还与其他同事进行了一些协作工作。

In the FY2021, we have experienced a productive research achievement. We have successfully published an overview paper on the topic of using artificial intelligence and internet of things (AIoT) technologies to better the ageing society. This paper has been published by the prestigious top journal, IEEE Signal Processing Magazine, and can itself be a good guidance for a broad community. In addition, this overview paper summarized the main ideas and/or techniques that learnt from our research project. We have used IoT sensor data to monitor the physical and mental status of elderly people (who are living alone), which has been found efficient and published in the IEEE Internet of Things Journal. Moreover, we have published our paper on using the state-of-the-art computer audition (CA) techniques to measure the health status of patients suffering from the COVID-19. The results were published by the IEEE Internet of Things Journal. A multi-modal learning system for monitoring the driver’s drowsiness level was accepted and published online by the top journal IEEE Transactions on Intelligent Transportation Systems. A systematic review on CA methods for snore site localization was published by the IEEE Journal of Biomedical and Health Informatics. Moreover, we have some collaborative works with other colleagues.