Abstract
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 this https URL.
Abstract (translated)
本文介绍了一种名为SSSUMO的半监督深度学习方法，用于次运动分解，该方法实现了最先进的准确性和速度。虽然次运动分析为对运动控制的理解提供了有价值的见解，但现有方法在重建精度、计算成本和验证方面面临着挑战，这主要是由于难以获取手动标注的数据所导致的问题。我们通过使用一种基于合成数据的半监督学习框架来解决这些问题。该框架首先从最小化位移的原则生成初始数据，并且通过不断迭代地适应未标记的人体运动数据来进行优化。我们的全卷积架构和可微重构显著超越了现有的方法，不仅在合成数据集上，在多样化的人类动作数据集上也表现出色，即使在高噪音条件下也能展现出强大的鲁棒性。特别重要的是，该模型可以在实时环境中运行（每秒输入处理时间少于一毫秒），这远远优于基于优化的方法。这种性能的提升为人类与计算机交互、康复医学和运动控制研究等新领域的应用开辟了道路。我们通过驾驶、旋转、指向、物体移动、书写以及鼠标控制游戏等一系列多样化的人类任务，展示了该模型的有效性，并且在传统方法表现不佳的数据集上显示出了显著的进步。训练和基准测试的源代码，以及预训练模型权重已公开提供，在此网址：[URL]（请将[URL]替换为实际提供的链接）。
URL
https://arxiv.org/abs/2507.08028
