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arXiv 2602.07024cs.ROcs.CV

一种用于实时人机协作中人类活动识别的分布式多模态传感方法

A Distributed Multi-Modal Sensing Approach for Human Activity Recognition in Real-Time Human-Robot Collaboration

University of Genoa（热那亚大学）；The University of Danang–University of Science and Technology（丹江-科学技术大学）；Japan Advanced Institute of Science and Technology（日本先进科学研究院）

Valerio Belcamino, Nhat Minh Dinh Le, Quan Khanh Luu, Alessandro Carfì, Van Anh Ho, Fulvio Mastrogiovanni

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AI总结：

本文提出一种结合数据手套和视觉触觉传感器的多模态方法，用于实时人机协作中的人类活动识别，实验显示该方法在不同场景下均表现出高精度。

摘要：

人类活动识别（HAR）在人机协作（HRC）中至关重要，使机器人能够响应并动态适应人类意图。本文介绍了一种结合模块化数据手套（配备惯性测量单元）和基于视觉的触觉传感器的HAR系统，以捕捉与机器人接触时的手部活动。我们测试了我们的活动识别方法在不同条件下的表现，包括分段序列的离线分类、静态条件下的实时分类，以及现实的HRC场景。实验结果表明，所有任务都具有高精度，表明多种协作设置可能从这种多模态方法中受益。

英文摘要：

Human activity recognition (HAR) is fundamental in human-robot collaboration (HRC), enabling robots to respond to and dynamically adapt to human intentions. This paper introduces a HAR system combining a modular data glove equipped with Inertial Measurement Units and a vision-based tactile sensor to capture hand activities in contact with a robot. We tested our activity recognition approach under different conditions, including offline classification of segmented sequences, real-time classification under static conditions, and a realistic HRC scenario. The experimental results show a high accuracy for all the tasks, suggesting that multiple collaborative settings could benefit from this multi-modal approach.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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