Posture-Informed Muscular Force Learning for Robust Hand Pressure Estimation

TL;DR

This paper introduces PiMForce, a framework that combines 3D hand posture data with sEMG signals to improve the accuracy and robustness of hand pressure estimation during diverse hand-object interactions.

Contribution

The paper presents a novel multimodal system integrating 3D hand posture, sEMG, and pressure data, along with a new dataset for robust hand pressure estimation.

Findings

Enhanced pressure estimation accuracy in complex interactions

Mitigation of traditional sEMG and vision-based method limitations

Comprehensive dataset for hand pressure and posture analysis

Abstract

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.