Advancements in Tactile Hand Gesture Recognition for Enhanced Human-Machine Interaction
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917676449792000 |
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| author | Fumelli, Chiara Dutta, Anirvan Kaboli, Mohsen |
| author_facet | Fumelli, Chiara Dutta, Anirvan Kaboli, Mohsen |
| contents | Motivated by the growing interest in enhancing intuitive physical Human-Machine Interaction (HRI/HVI), this study aims to propose a robust tactile hand gesture recognition system. We performed a comprehensive evaluation of different hand gesture recognition approaches for a large area tactile sensing interface (touch interface) constructed from conductive textiles. Our evaluation encompassed traditional feature engineering methods, as well as contemporary deep learning techniques capable of real-time interpretation of a range of hand gestures, accommodating variations in hand sizes, movement velocities, applied pressure levels, and interaction points. Our extensive analysis of the various methods makes a significant contribution to tactile-based gesture recognition in the field of human-machine interaction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_17038 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Advancements in Tactile Hand Gesture Recognition for Enhanced Human-Machine Interaction Fumelli, Chiara Dutta, Anirvan Kaboli, Mohsen Human-Computer Interaction Artificial Intelligence Motivated by the growing interest in enhancing intuitive physical Human-Machine Interaction (HRI/HVI), this study aims to propose a robust tactile hand gesture recognition system. We performed a comprehensive evaluation of different hand gesture recognition approaches for a large area tactile sensing interface (touch interface) constructed from conductive textiles. Our evaluation encompassed traditional feature engineering methods, as well as contemporary deep learning techniques capable of real-time interpretation of a range of hand gestures, accommodating variations in hand sizes, movement velocities, applied pressure levels, and interaction points. Our extensive analysis of the various methods makes a significant contribution to tactile-based gesture recognition in the field of human-machine interaction. |
| title | Advancements in Tactile Hand Gesture Recognition for Enhanced Human-Machine Interaction |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2405.17038 |