Open AccessLLAD: Life-Log Anomaly Detection Based on Recurrent Neural Network LSTM
Author(s) -
Ermal Elbasani,
JeongDong Kim
Publication year - 2021
Publication title -
journal of healthcare engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.509
H-Index - 29
eISSN - 2040-2309
pISSN - 2040-2295
DOI - 10.1155/2021/8829403
Subject(s) - anomaly detection , anomaly (physics) , computer science , data mining , term (time) , data collection , artificial neural network , long short term memory , artificial intelligence , recurrent neural network , machine learning , statistics , mathematics , physics , quantum mechanics , condensed matter physics
Life-Log is a term used for the daily monitoring of health conditions and recognizing anomalies from data generated by sensor devices. The development of smart sensors enables collection of health data, which can be considered as a solution to risks associated with personal healthcare by raising awareness regarding health conditions and wellness. Therefore, Life-Log analysis methods are important for real-life monitoring and anomaly detection. This study proposes a method for the improvement and combination of previous methods and techniques in similar fields to detect anomalies in health log data generated by various sensors. Recurrent neural networks with long short-term memory units are used for analyzing the Life-Log data. The results indicate that the proposed model performs more effectively than conventional health data analysis methods, and the proposed approach can yield a satisfactory accuracy in anomaly detection.