融合手部姿态与肌电信号，提升自然交互下的压力估计算法鲁棒性。
Posture-Informed Muscular Force Learning for Robust Hand Pressure Estimation
- 结合3D手姿与肌电数据，构建多模态压力估计模型。
- 在21名参与者、多种姿势下实现高精度压力预测。
- 适合需要精准手部力觉反馈的机器人操控与康复应用。
我们提出PiMForce框架，通过引入3D手部姿态信息来增强前臂表面肌电（sEMG）信号，以提升手部压力估计的准确性与鲁棒性。该方法结合3D手姿的空间信息与sEMG的动态肌肉活动，实现了在多样化手-物体交互场景下的全手压力测量。我们构建了多模态数据采集系统，集成压力手套、sEMG臂带和无标记手指追踪模块。基于21名受试者的数据，采集了在不同手姿和交互情境下同步的手部姿态、肌电信号与施加压力数据，形成全面数据集。所提方法显著缓解了传统基于sEMG或视觉的方法局限，有效提升了复杂自然交互中的压力估计性能。视频演示、数据与代码已公开。
原文摘要 · Abstract (English)
We present PiMForce, a novel framework that enhances hand pressure estimation by leveraging 3D hand posture information to augment forearm surface electromyography (sEMG) signals. Our approach utilizes detailed spatial information from 3D hand poses in conjunction with dynamic muscle activity from sEMG to enable accurate and robust whole-hand pressure measurements under diverse hand-object interactions. We also developed a multimodal data collection system that combines a pressure glove, an sEMG armband, and a markerless finger-tracking module. We created a comprehensive dataset from 21 participants, capturing synchronized data of hand posture, sEMG signals, and exerted hand pressure across various hand postures and hand-object interaction scenarios using our collection system. Our framework enables precise hand pressure estimation in complex and natural interaction scenarios. Our approach substantially mitigates the limitations of traditional sEMG-based or vision-based methods by integrating 3D hand posture information with sEMG signals. Video demos, data, and code are available online.
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