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.

多元时间序列数据在智能医疗场景中的异常活动检测具有潜在的准确性，但是异常模式多样且在时间序列中变得难以察觉。为了解决这个问题，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和异常活动检测。我们在真实步态数据集上评估我们的方案，并实验结果表明 F1 得分为 0.839。

多变量时间序列医疗数据上的有效异常活动检测

Effective Abnormal Activity Detection on Multivariate Time Series  Healthcare Data

A classical approach to abnormal activity detection is to learn a
representation for normal activities from the training data and then use this
learned representation to detect abnormal activities while testing. Typically,
the methods based on this approach operate at a fixed timescale - either a
single time-instant (eg. frame-based) or a constant time duration (eg.
video-clip based). But human abnormal activities can take place at different
timescales. For example, jumping is a short term anomaly and loitering is a
long term anomaly in a surveillance scenario. A single and pre-defined
timescale is not enough to capture the wide range of anomalies occurring with
different time duration. In this paper, we propose a multi-timescale model to
capture the temporal dynamics at different timescales. In particular, the
proposed model makes future and past predictions at different timescales for a
given input pose trajectory. The model is multi-layered where intermediate
layers are responsible to generate predictions corresponding to different
timescales. These predictions are combined to detect abnormal activities. In
addition, we also introduce an abnormal activity data-set for research use that
contains 4,83,566 annotated frames. Data-set will be made available at
this https URL Our
experiments show that the proposed model can capture the anomalies of different
time duration and outperforms existing methods.

提出了一种多时间尺度模型，以在不同时间尺度上捕获异常活动，并通过对不同时间尺度的输入姿势轨迹进行未来和过去预测来检测异常活动。