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The use of multiple clocks has been a favoured approach to modelling the multiple timescales of sequential data. Previous work based on clocks and multi-timescale studies in general have not clearly accounted for multidimensionality of data such that each dimension has its own timescale(s). Focusing on body movement data which has independent yet coordinating degrees of freedom, we propose a Movement in Multiple Time (MiMT) neural network. Our MiMT models multiple timescales by learning different levels of movement interpretation (i.e. labels) and further allows for separate timescales across movements dimensions. We obtain 0.75 and 0.58 average F1 scores respectively for binary frame-level and three-class window-level classification of pain behaviour based on the MiMT. Findings in ablation studies suggest that these two elements of the MiMT are valuable to modelling multiple timescales of multidimensional sequential data.

A Movement in Multiple Time Neural Network for Automatic Detection of Pain Behaviour