Self-similarity matrix of morphological features for motion data analysis in manufacturing scenarios

There is a significant interest to evaluate the exposure that operators are subjected throughout the working day. The objective evaluation of occupational exposure with direct measurements and the need for automatic annotation of relevant events arose. Using time series retrieved from inertial sensors, this work proposes a method that is able to automatically: (1) detect anomalies, (2) segment the working cycles and (3) by means of query-by-example, identify sub segments along the working cycle. In a short summary, this technique firstly organizes the dataset provided by all inertial measurement units (IMUs) sensors placed over the dominant upper limb. After this, it retrieves a wide variety of features to an organized matrix and then calculates the respective self-similarity matrix (SSM). This method provides information by comparing each subsequence of the time series with the remaining subsequences. As the identified structures will provide information about how repe titive or anomalous is the behaviour of the data in function of time. The results show that the presented method is capable of identifying anomalies on this dataset with an accuracy of 82%, detect working cycles with a duration error of about 6% of the working cycle, and has the ability to find matches of sub-sequences of the working cycle.