用手套+视觉触觉传感器实时识别人机协作中的手部动作
A Distributed Multi-Modal Sensing Approach for Human Activity Recognition in Real-Time Human-Robot Collaboration
- 融合惯性传感器手套与视觉触觉传感，实现多模态数据采集
- 在真实人机协作场景中达到高识别准确率
- 适合需要实时响应的工业机器人协作系统
人机协作（HRC）中的人体活动识别（HAR）是使机器人能够响应并动态适应人类意图的基础。本文提出一种结合配备惯性测量单元（IMU）的数据手套与基于视觉的触觉传感器的HAR系统，用于捕捉与机器人接触时的手部活动。我们在不同条件下测试了该识别方法：包括分段序列的离线分类、静态条件下的实时分类，以及真实的HRC场景。实验结果表明，所有任务均表现出高准确率，表明该多模态方法可广泛适用于多种协同场景。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。