Jointly Modeling Spatio-Temporal Features of Tactile Signals for Action Classification
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866909179819589632 |
|---|---|
| author | Lin, Jimmy Li, Junkai Gao, Jiasi Ma, Weizhi Liu, Yang |
| author_facet | Lin, Jimmy Li, Junkai Gao, Jiasi Ma, Weizhi Liu, Yang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_15279 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Jointly Modeling Spatio-Temporal Features of Tactile Signals for Action Classification Lin, Jimmy Li, Junkai Gao, Jiasi Ma, Weizhi Liu, Yang Signal Processing Artificial Intelligence 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. |
| title | Jointly Modeling Spatio-Temporal Features of Tactile Signals for Action Classification |
| topic | Signal Processing Artificial Intelligence |
| url | https://arxiv.org/abs/2404.15279 |