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In this work we propose a solution to the UbiComp 2021 Challenge by Stabiloin which handwritten mathematical terms are supposed to be automaticallyclassified based on time series sensor data captured on the DigiPen. The inputdata set contains data of different writers, with label strings constructedfrom a total of 15 different possible characters. The label should first besplit into separate characters to classify them one by one. This issue issolved by applying a data-dependant and rule-based information extractionalgorithm to the labeled data. Using the resulting data, two classifiers areconstructed. The first is a binary classifier that is able to predict, forunknown data, if a sample is part of a writing activity, and consists of a DeepNeural Network feature extractor in concatenation with a Random Forest that istrained to classify the extracted features at an F1 score of >90%. The secondclassifier is a Deep Neural Network that combines convolution layers withrecurrent layers to predict windows with a single label, out of the 15 possibleclasses, at an F1 score of >60%. A simulation of the challenge evaluationprocedure reports a Levensthein Distance of 8 and shows that the chosenapproach still lacks in overall accuracy and real-time applicability.

Detecting Handwritten Mathematical Terms with Sensor Based Data