Deep Hybrid Learning for Anomaly Detection in Behavioral Monitoring
Spiros V. Georgakopoulos, Sotiris K. Tasoulis, Aristidis G. Vrahatis, Serafeim Moustakidis, Dimitrios E. Tsaopoulos, Vassilis P. Plagianakos · 2022 International Joint Conference on Neural Networks (IJCNN) · 2022
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.