HiSync: Spatio-Temporally Aligning Hand Motion from Wearable IMU and On-Robot Camera for Command Source Identification in Long-Range HRI

TL;DR

HiSync is a novel optical-inertial fusion framework that aligns camera and IMU data to accurately identify command sources in long-range multi-user human-robot interaction scenarios, significantly outperforming previous methods.

Contribution

This paper introduces HiSync, a new multimodal fusion approach that aligns hand motion signals from wearable IMUs and robot-mounted cameras for reliable command source identification at long distances.

Findings

Achieves 92.32% CSI accuracy in 34m scenes

Outperforms previous state-of-the-art by 48.44%

Validated on real-robot deployment

Abstract

Long-range Human-Robot Interaction (HRI) remains underexplored. Within it, Command Source Identification (CSI) - determining who issued a command - is especially challenging due to multi-user and distance-induced sensor ambiguity. We introduce HiSync, an optical-inertial fusion framework that treats hand motion as binding cues by aligning robot-mounted camera optical flow with hand-worn IMU signals. We first elicit a user-defined (N=12) gesture set and collect a multimodal command gesture dataset (N=38) in long-range multi-user HRI scenarios. Next, HiSync extracts frequency-domain hand motion features from both camera and IMU data, and a learned CSINet denoises IMU readings, temporally aligns modalities, and performs distance-aware multi-window fusion to compute cross-modal similarity of subtle, natural gestures, enabling robust CSI. In three-person scenes up to 34m, HiSync achieves 92.32% CSI accuracy, outperforming the prior SOTA by 48.44%. HiSync is also validated on real-robot deployment. By making CSI reliable and natural, HiSync provides a practical primitive and design guidance for public-space HRI. https://github.com/OctopusWen/HiSync