Personalized real-time anomaly detection and health feedback for older adults

Parvaneh Parvin*, Stefano Chessa, Maurits Kaptein, Fabio Paternò

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review


Rapid population aging and the availability of sensors and intelligent objects motivate the development of healthcare systems; these systems, in turn, meet the needs of older adults by supporting them to accomplish their day-to-day activities. Collecting information regarding older adults daily activity potentially helps to detect abnormal behavior. Anomaly detection can subsequently be combined with real-time, continuous and personalized interventions to help older adults actively enjoy a healthy lifestyle. This paper introduces a system that uses a novel approach to generate personalized health feedback. The proposed system models user’s daily behavior in order to detect anomalous behaviors and strategically generates interventions to encourage behaviors conducive to a healthier lifestyle. The system uses a Mamdani-type fuzzy rule-based component to predict the level of intervention needed for each detected anomaly and a sequential decision-making algorithm, Contextual Multi-armed Bandit, to generate suggestions to minimize anomalous behavior. We describe the system’s architecture in detail and we provide example implementations for the anomaly detection and corresponding health feedback.
Original languageEnglish
Pages (from-to)453-469
Number of pages17
JournalJournal of Ambient Intelligence and Smart Environments
Issue number5
Publication statusPublished - 2019


  • Ambient assisted living
  • anomaly detection
  • elderly behavior analysis
  • health interventions
  • remote monitoring


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