Extending knowledge-driven activity models through data-driven learning techniques
作者：
Highlights：
• We combine knowledge- and data-driven approaches for activity modeling.
• We develop a novel clustering algorithm that uses prior domain expert knowledge.
• A new learning algorithm to model activities from extracted clusters.
• We model a pervasive home environment with real users’ inputs for experiments.
• Automatically learn 100% of activity variations performed by users.
摘要
•We combine knowledge- and data-driven approaches for activity modeling.•We develop a novel clustering algorithm that uses prior domain expert knowledge.•A new learning algorithm to model activities from extracted clusters.•We model a pervasive home environment with real users’ inputs for experiments.•Automatically learn 100% of activity variations performed by users.
论文关键词：Activity recognition,Knowledge-driven,Learning,Activity model
论文评审过程：Available online 11 December 2014.
论文官网地址：https://doi.org/10.1016/j.eswa.2014.11.063
