Deep Hybrid Learning for Anomaly Detection in Behavioral Monitoring
Author
Georgakopoulos S.V., Tasoulis S.K., Vrahatis A.G., Moustakidis S., Tsaopoulos D.E., Plagianakos V.P.Date
2022Language
en
Keyword
Abstract
The task of understanding human behavior through intelligent systems is crucial in various domains from medical health and well-being to financial and social platforms. In this work, we propose a complete framework that takes advantage of collected sensor accelerometer data to generate a human activity behavioral model that can be supportive in predicting future development of human movement disabilities such as Osteoarthritis or even in the individual's rehabilitation after a surgery for Osteoarthritis. More precisely, we focus on estimating uncommon behaviors within daily activities as an indication for further examination. Challenge-point of the proposed methodology is the agnostic knowledge of different behaviours of individual's movement. Based on accelerometer sensor data collected from mobile devices, the proposed framework utilizes state-of-the-art Machine Learning models for Human Activity Recognition and introduces new Deep Hybrid Models for outlier detection suggesting a solid basis for further developments and wider applicability. © 2022 IEEE.