On the Detection of Anomalies with the Use of Choquet Integral and Their Interpretability in Motion Capture Data

Modern information technologies allow for the collection, processing, and data analysis. A very important role of these systems can be observed in the analysis of medical records, particularly in the analysis of motion capture data. Detection and interpretation of data collected from movement recording devices enables for a fast diagnosis, e.g. disease or misfunction. Moreover, it can be assistive in the introduction of appropriate treatment or rehabilitation. Anomaly detection methods play a key role in the evaluation of this type of medical research results. In this study, we introduce an innovative approach based on the aggregation of the results of eleven anomaly detection classifiers outcomes with the fuzzy Choquet integral. Furthermore, the results of numerical experiments are confronted with the assessments of the experts in medical field. The results show the great potential of our method in supporting the decision-making process based on the motion capture data analysis. Moreover, we have caught the differences between the expert's understanding of anomaly and the anomalies in data found by the modern machine learning methods.