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.

利用连续触觉信号进行动作分类的一项主要应用是医疗保健和机器人技术，然而，现有的触觉分类方法未能同时捕捉触觉信号的空间和时间特征，从而导致性能不佳。本文设计了一种名为 Spatio-Temporal Aware tactility Transformer (STAT) 的模型，利用连续触觉信号进行动作分类，通过在模型中引入空间和时间嵌入以及新的时间预训练任务，旨在增强变压器对触觉信号的空间和时间特征建模能力。实验结果表明，在公共动作分类数据集上，我们的模型在所有评估指标上优于现有的方法。