Indoor location identification of patients for directing virtual care: An AI approach using machine learning and knowledge-based methods
作者：
Highlights：
• Accurately locating individuals indoors for virtual care scenarios is difficult.
• We combine knowledge- and data-driven methods, incl. ontologies and machine learning.
• We improve location accuracy by training ML models and clustering similar locations.
• We create semantic location models to validate locations and provide rich semantics.
• We empirically validate our approach in a real-world hospital emergency department.
摘要
•Accurately locating individuals indoors for virtual care scenarios is difficult.•We combine knowledge- and data-driven methods, incl. ontologies and machine learning.•We improve location accuracy by training ML models and clustering similar locations.•We create semantic location models to validate locations and provide rich semantics.•We empirically validate our approach in a real-world hospital emergency department.
论文关键词：Virtual care,Ambient sensors,Indoor localization,Machine learning,Semantic web,eHealth platform,Data fusion,Self-management,Ambient assisted living,Activities of daily living
论文评审过程：Received 28 January 2020, Revised 18 May 2020, Accepted 11 July 2020, Available online 21 July 2020, Version of Record 28 July 2020.
论文官网地址：https://doi.org/10.1016/j.artmed.2020.101931
