A context-aware approach for long-term behavioural change detection and abnormality prediction in ambient assisted living
作者：
Highlights：
• We developed a novel change detection framework for context-aware monitoring.
• We described a Hidden Markov Model for detecting abnormalities in daily activities.
• We developed a statistical process to identify irregularity in routine behaviours.
• We presented a disease forecasting technique using Holt׳s liner trend method.
• We built a rule-based fuzzy fusion model for making context-aware decision.
摘要
Highlights•We developed a novel change detection framework for context-aware monitoring.•We described a Hidden Markov Model for detecting abnormalities in daily activities.•We developed a statistical process to identify irregularity in routine behaviours.•We presented a disease forecasting technique using Holt׳s liner trend method.•We built a rule-based fuzzy fusion model for making context-aware decision.
论文关键词：Context-aware,Ambient assisted living,Remote monitoring,Healthcare,Eldercare,Cloud computing,Change detection,Pattern recognition,Trend detection,Hidden Markov Model
论文评审过程：Received 15 January 2014, Revised 16 May 2014, Accepted 5 July 2014, Available online 18 July 2014.
论文官网地址：https://doi.org/10.1016/j.patcog.2014.07.007
