Abstract
Surface electromyography (sEMG) signals show promise for effective human-computer interfaces, particularly in rehabilitation and prosthetics. However, challenges remain in developing systems that respond quickly and reliably to user intent, across different subjects and without requiring time-consuming calibration. In this work, we propose a framework for EMG-based intent detection that addresses these challenges. Unlike traditional gesture recognition models that wait until a gesture is completed before classifying it, our approach uses a segmentation strategy to assign intent labels at every timestep as the gesture unfolds. We introduce a novel masked modeling strategy that aligns muscle activations with their corresponding user intents, enabling rapid onset detection and stable tracking of ongoing gestures. In evaluations against baseline methods, considering both accuracy and stability for device control, our approach surpasses state-of-the-art performance in zero-shot transfer conditions, demonstrating its potential for wearable robotics and next-generation prosthetic systems. Our project page is available at: this https URL
Abstract (translated)
表面肌电图（sEMG）信号在人机接口方面显示出巨大的潜力，尤其是在康复和假肢领域。然而，在不同用户间快速且可靠地响应用户意图的系统开发仍面临挑战，特别是在不需要耗时校准的情况下。为此，我们提出了一种基于EMG的意图检测框架，旨在解决这些问题。 与传统的手势识别模型等待完整的手势完成后再进行分类不同，我们的方法采用了一种分段策略，在每个时间点上随着手势的发展分配意图标签。我们引入了一种新颖的掩码建模策略，该策略将肌肉激活与相应的用户意图对齐，从而实现快速起始检测和正在进行的手势的稳定跟踪。 在与基线方法进行评估时，我们的方法在零样本迁移条件下（即无需特定于每个用户的校准）实现了设备控制的准确性和稳定性方面的性能超越。这表明了其在可穿戴机器人技术和下一代假肢系统中的巨大潜力。 项目页面在此网址: [this https URL] (请将方括号内的内容替换为实际链接)
URL
https://arxiv.org/abs/2506.19815
