These files contain data supporting all results reported in Lee et. al. Grab-n-Go: On-the-Go Microgesture Recognition with Objects in Hand. 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 differentiate among 30 distinct microgestures organized into 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%. Furthermore, a follow-up study expanded our dataset to include an additional 10 deformable objects, and we will release this enriched dataset to the research community. These results underscore the potential of Grab-n-Go to provide seamless, unobtrusive interactions without requiring modifications to existing objects.