迈向无处不在的传感：研究人员将WiFi信号转化为人类活动模式。

Toward ubiquitous sensing: Researchers turn WiFi signals into human activity patterns.

作者信息

Yang Jianfei, Chen Xinyan

机构信息

Nanyang Technological University, Singapore 639798, Singapore.

出版信息

Patterns (N Y). 2023 Mar 10;4(3):100707. doi: 10.1016/j.patter.2023.100707.

DOI:10.1016/j.patter.2023.100707

PMID:36960447

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10028417/

Abstract

Jianfei Yang, a principal investigator and postdoc at Nanyang Technological University (NTU), and his student Xinyan Chen have developed a comprehensive benchmark and library for WiFi sensing. Their paper highlights the advantages of deep learning for WiFi sensing and provides constructive suggestions on model selection, learning scheme, and training strategy for developers and data scientists in this field. They talk about their view of data science, their experience with interdisciplinary WiFi sensing research, and the future of WiFi sensing applications.

摘要

杨建飞是南洋理工大学的首席研究员和博士后，他与学生陈欣研共同开发了一个用于WiFi感知的综合基准和库。他们的论文突出了深度学习在WiFi感知方面的优势，并为该领域的开发者和数据科学家在模型选择、学习方案和训练策略方面提供了建设性建议。他们谈到了自己对数据科学的看法、跨学科WiFi感知研究的经验以及WiFi感知应用的未来。