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

Parvin P., Chessa S., Kaptein M., Paterno F.

Ambient assisted living  Elderly behavior analysis  BEHAVIOR  Health interventions  Software  CONTEXT  Remote monitoring  Anomaly detection 

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.

Source: Journal of ambient intelligence and smart environments (Print) 11 (2019): 453–469. doi:10.3233/AIS-190536

Publisher: IOS Press, Amsterdam , Paesi Bassi

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