  Tactile signals collected by wearable electronics are essential in modeling
and understanding human behavior. One of the main applications of tactile
signals is action classification, especially in healthcare and robotics.
However, existing tactile classification methods fail to capture the spatial
and temporal features of tactile signals simultaneously, which results in
sub-optimal performances. In this paper, we design Spatio-Temporal Aware
tactility Transformer (STAT) to utilize continuous tactile signals for action
classification. We propose spatial and temporal embeddings along with a new
temporal pretraining task in our model, which aims to enhance the transformer
in modeling the spatio-temporal features of tactile signals. Specially, the
designed temporal pretraining task is to differentiate the time order of
tubelet inputs to model the temporal properties explicitly. Experimental
results on a public action classification dataset demonstrate that our model
outperforms state-of-the-art methods in all metrics.
