Effective Abnormal Activity Detection on Multivariate Time Series Healthcare Data
This work addresses anomaly detection in smart healthcare, offering a method for monitoring patient activities, but it appears incremental as it builds on existing representation learning techniques.
The paper tackled the challenge of detecting diverse and subtle anomalies in multivariate time series healthcare data by proposing a residual-based anomaly detection approach, achieving an F1 score of 0.839 on a real-world gait dataset.
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.