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.

翻译：从多个传感器采集的多变量时间序列（MTS）数据为智能医疗场景中精准的异常活动检测提供了可能。然而，异常现象在MTS数据中呈现出多样化的模式且难以察觉。因此，实现准确的异常检测具有挑战性，因为我们既要捕捉时间序列的时序依赖性，又要捕捉变量间的相互关系。为解决该问题，我们提出了一种基于残差的异常检测方法Rs-AD，用于有效的表示学习与异常活动检测。我们在真实世界的步态数据集上评估了该方案，实验结果表明其F1分数达到0.839。