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Communication Dans Un Congrès Année : 2020

Learning from partially labeled sequences for behavioral signal annotation

Résumé

Herewith, we present a learning procedure that allows to deal with a partially labeled sequence dataset, i.e. when each sequence in the train dataset may contain labeled as well as unlabeled chunks. In our application case, this occurs when motor activity has been manually annotated (due to the recognition based on the video recording) and independently registered by the measuring system of high precision (touch sensors): human annotation misses some events that have been captured by the sensors. In the general setting, we aim at predicting the labels for a new fully unlabeled movement sequence, while the training has been performed on the partially labeled dataset. For this purpose we propose to use classical sequence model (hidden Markov model) that is furnished with a constrained Viterbi algorithm, which gives us a quick access to the hard approximation of the correct labeling sequences. We demonstrate, that this simple modification that constrained Viterbi provide, allows the HMM model to be trained on sparse data, and overall results in surprisingly high log-likelihood and accuracy level in annotating the partially labeled behavioral sequences in climbing. The same time we show the way to access correct labeling of the unannotated signal that can be helpful in various sport science studies for movement pattern sequential prediction.
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Dates et versions

hal-04465133 , version 1 (19-02-2024)

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Anna Aniszewska-Stępień, Romain Hérault, Guillaume Hacques, Ludovic Seifert, Gilles Gasso. Learning from partially labeled sequences for behavioral signal annotation. International Workshop on Machine Learning and Data Mining for Sports Analytics, Sep 2020, Ghent, Belgium. pp.126-139, ⟨10.1007/978-3-030-64912-8_11⟩. ⟨hal-04465133⟩
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