Grab-n-Go: On-the-Go Microgesture Recognition with Objects in Hand
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arXiv
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| Format: | Preprint |
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2025
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| author | Lee, Chi-Jung Li, Jiaxin Yu, Tianhong Catherine Zhang, Ruidong Gunda, Vipin Guimbretière, François Zhang, Cheng |
| author_facet | Lee, Chi-Jung Li, Jiaxin Yu, Tianhong Catherine Zhang, Ruidong Gunda, Vipin Guimbretière, François Zhang, Cheng |
| contents | As computing devices become increasingly integrated into daily life, there is a growing need for intuitive, always-available interaction methods, even when users' hands are occupied. In this paper, we introduce Grab-n-Go, the first wearable device that leverages active acoustic sensing to recognize subtle hand microgestures while holding various objects. Unlike prior systems that focus solely on free-hand gestures or basic hand-object activity recognition, Grab-n-Go simultaneously captures information about hand microgestures, grasping poses, and object geometries using a single wristband, enabling the recognition of fine-grained hand movements occurring within activities involving occupied hands. A deep learning framework processes these complex signals to identify 30 distinct microgestures, with 6 microgestures for each of the 5 grasping poses. In a user study with 10 participants and 25 everyday objects, Grab-n-Go achieved an average recognition accuracy of 92.0%. A follow-up study further validated Grab-n-Go's robustness against 10 more challenging, deformable objects. These results underscore the potential of Grab-n-Go to provide seamless, unobtrusive interactions without requiring modifications to existing objects. The complete dataset, comprising data from 18 participants performing 30 microgestures with 35 distinct objects, is publicly available at https://github.com/cjlisalee/Grab-n-Go_Data with the DOI: https://doi.org/10.7298/7kbd-vv75. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_11620 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Grab-n-Go: On-the-Go Microgesture Recognition with Objects in Hand Lee, Chi-Jung Li, Jiaxin Yu, Tianhong Catherine Zhang, Ruidong Gunda, Vipin Guimbretière, François Zhang, Cheng Human-Computer Interaction As computing devices become increasingly integrated into daily life, there is a growing need for intuitive, always-available interaction methods, even when users' hands are occupied. In this paper, we introduce Grab-n-Go, the first wearable device that leverages active acoustic sensing to recognize subtle hand microgestures while holding various objects. Unlike prior systems that focus solely on free-hand gestures or basic hand-object activity recognition, Grab-n-Go simultaneously captures information about hand microgestures, grasping poses, and object geometries using a single wristband, enabling the recognition of fine-grained hand movements occurring within activities involving occupied hands. A deep learning framework processes these complex signals to identify 30 distinct microgestures, with 6 microgestures for each of the 5 grasping poses. In a user study with 10 participants and 25 everyday objects, Grab-n-Go achieved an average recognition accuracy of 92.0%. A follow-up study further validated Grab-n-Go's robustness against 10 more challenging, deformable objects. These results underscore the potential of Grab-n-Go to provide seamless, unobtrusive interactions without requiring modifications to existing objects. The complete dataset, comprising data from 18 participants performing 30 microgestures with 35 distinct objects, is publicly available at https://github.com/cjlisalee/Grab-n-Go_Data with the DOI: https://doi.org/10.7298/7kbd-vv75. |
| title | Grab-n-Go: On-the-Go Microgesture Recognition with Objects in Hand |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2508.11620 |