Deep Hybrid Learning for Anomaly Detection in Behavioral Monitoring
Συγγραφέας
Georgakopoulos S.V., Tasoulis S.K., Vrahatis A.G., Moustakidis S., Tsaopoulos D.E., Plagianakos V.P.Ημερομηνία
2022Γλώσσα
en
Λέξη-κλειδί
Επιτομή
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.