{"ID":2882043,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.11620","arxiv_id":"2508.11620","title":"Grab-n-Go: On-the-Go Microgesture Recognition with Objects in Hand","abstract":"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.","short_abstract":"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 microges...","url_abs":"https://arxiv.org/abs/2508.11620","url_pdf":"https://arxiv.org/pdf/2508.11620v1","authors":"[\"Chi-Jung Lee\",\"Jiaxin Li\",\"Tianhong Catherine Yu\",\"Ruidong Zhang\",\"Vipin Gunda\",\"François Guimbretière\",\"Cheng Zhang\"]","published":"2025-08-15T17:39:47Z","proceeding":"cs.HC","tasks":"[\"cs.HC\"]","methods":"[]","has_code":false,"code_links":[{"ID":610857,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2882043,"paper_url":"https://arxiv.org/abs/2508.11620","paper_title":"Grab-n-Go: On-the-Go Microgesture Recognition with Objects in Hand","repo_url":"https://github.com/cjlisalee/Grab-n-Go_Data","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}