μDPad: A Large-Scale Multimodal PPG and IMU Dataset for Wrist-Worn Microgesture Recognition

Abstract

Gestures provide a natural interaction modality for mobile and wearable devices. However, most prior work focuses on macro gestures involving large wrist or arm movements, which are fatiguing and socially conspicuous. In contrast, microgestures, subtle, low-effort finger movements that can be performed one-handed and without visual attention, offer a promising alternative but remain significantly harder to sense and recognize. In this work, we present a large-scale multimodal dataset for wrist-based microgesture recognition, collected with an integrated wearable prototype that combines an IMU with an 8-channel spatial PPG sensor configuration similar to one emerging in commercial fitness watches. Because such devices typically do not expose raw optical signals, our goal is to provide the community with a resource for studying whether spatial PPG can capture the minute tissue deformations associated with microgestures. Our dataset comprises 25 hours of synchronized IMU and PPG recordings from 66 participants, including 9 hours of aligned video annotations, and captures substantial variability in users, hand poses, and execution styles. To establish reference performance, we benchmark a range of recognition pipelines and present a simple multimodal baseline. This baseline already shows that adding spatial PPG improves microgesture detection over IMU-only sensing, reaching 83.7% accuracy with a 3.7% false-positive rate, while also underscoring that microgesture recognition remains substantially harder than recognizing coarser macro-gesture vocabularies. By releasing the dataset, we aim to enable more substantive algorithmic advances toward practical, low-effort, and socially acceptable microgesture interaction on future wrist-worn devices. Project page: siplab.org/projects/microDPad.