# Jointly Modeling Spatio-Temporal Features of Tactile Signals for Action Classification Source: [Jointly Modeling Spatio-Temporal Features of Tactile Signals for Action Classification]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:33:04.779Z Authors: Jimmy Lin, Junkai Li, Jiasi Gao, Weizhi Ma, Yang Liu Published: 2024-01-21T03:47:57.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Action Classification on Tactile foot signals dataset unseen subjects (test)](): Action Classification; Tactile foot signals dataset unseen subjects (test); Accuracy: 84.56. ## Original sources and implementation [Original paper]() [PDF]()