Evaluation of spatial-temporal anomalies in the analysis of human movement

In industrial contexts, the performed tasks consist of sets of predetermined movements that are continuously repeated. The execution of improper movements and the existence of events that might prejudice the productive system are regarded as anomalies. In this work, it is proposed a framework capable of detecting anomalies in generic repetitive time series, adequate to handle human motion from industrial scenarios. The proposed framework consists of (1) a new unsupervised segmentation algorithm; (2) feature extraction, selection and dimensionality reduction; (3) unsupervised classification based on Density-Based Spatial Clustering Algorithm for applications with Noise. The proposed solution was applied in four different datasets. The yielded results demonstrated that anomaly detection in human motion is possible with an accuracy of 73±19%, specificity of 74±21% and sensitivity of 74±35%, and also that the developed framework is generic and may be applied in general repetitive time series with little adaptation effort for different domains.