SSSUMO：实时半监督子运动分解
SSSUMO: Real-Time Semi-Supervised Submovement Decomposition
AI总结：
本文提出半监督深度学习方法SSSUMO用于子运动分解，通过全卷积架构与可微重建从未标注数据中学习，实现高精度实时运行，在人机交互等任务中显著超越传统方法。
摘要：
本文介绍了SSSUMO，一种用于子运动分解的半监督深度学习方法，实现了最先进的准确率和速度。虽然子运动分析为运动控制提供了有价值的见解，但由于难以获取手工标注数据，现有方法在重建准确率、计算成本和验证方面存在困难。我们使用半监督学习框架来解决这些挑战。该框架从合成数据中学习，这些数据最初基于最小急动度原理生成，随后通过适应未标注的人类运动数据进行迭代优化。我们的带有可微重建的全卷积架构在合成和多样化的人类运动数据集上均显著超越了现有方法，即使在高噪声条件下也展现出鲁棒性。关键的是，该模型能实时运行（每秒输入耗时不到一毫秒），相比基于优化的技术有了大幅提升。这种增强的性能促进了人机交互、康复医学和运动控制研究中的新应用。我们展示了模型在引导、旋转、指向、物体移动、手写和鼠标控制游戏等多样化人类执行任务中的有效性，特别是在传统方法基本失效的具有挑战性的数据集上显示出显著改善。训练和基准测试源代码以及预训练模型权重已在 https://github.com/dolphin-in-a-coma/sssumo 公开。
英文摘要：
This paper introduces a SSSUMO, semi-supervised deep learning approach for submovement decomposition that achieves state-of-the-art accuracy and speed. While submovement analysis offers valuable insights into motor control, existing methods struggle with reconstruction accuracy, computational cost, and validation, due to the difficulty of obtaining hand-labeled data. We address these challenges using a semi-supervised learning framework. This framework learns from synthetic data, initially generated from minimum-jerk principles and then iteratively refined through adaptation to unlabeled human movement data. Our fully convolutional architecture with differentiable reconstruction significantly surpasses existing methods on both synthetic and diverse human motion datasets, demonstrating robustness even in high-noise conditions. Crucially, the model operates in real-time (less than a millisecond per input second), a substantial improvement over optimization-based techniques. This enhanced performance facilitates new applications in human-computer interaction, rehabilitation medicine, and motor control studies. We demonstrate the model's effectiveness across diverse human-performed tasks such as steering, rotation, pointing, object moving, handwriting, and mouse-controlled gaming, showing notable improvements particularly on challenging datasets where traditional methods largely fail. Training and benchmarking source code, along with pre-trained model weights, are made publicly available at https://github.com/dolphin-in-a-coma/sssumo.
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。
