Effective Abnormal Activity Detection on Multivariate Time Series Healthcare Data
Abstract
Multivariate time series (MTS) data collected from multiple sensors provide the potential for accurate abnormal activity detection in smart healthcare scenarios. However, anomalies exhibit diverse patterns and become unnoticeable in MTS data. Consequently, achieving accurate anomaly detection is challenging since we have to capture both temporal dependencies of time series and inter-relationships among variables. To address this problem, we propose a Residual-based Anomaly Detection approach, Rs-AD, for effective representation learning and abnormal activity detection. We evaluate our scheme on a real-world gait dataset and the experimental results demonstrate an F1 score of 0.839.
Cite
@article{arxiv.2309.05845,
title = {Effective Abnormal Activity Detection on Multivariate Time Series Healthcare Data},
author = {Mengjia Niu and Yuchen Zhao and Hamed Haddadi},
journal= {arXiv preprint arXiv:2309.05845},
year = {2023}
}
Comments
Poster accepted by the 29th Annual International Conference On Mobile Computing And Networking (ACM MobiCom 2023)