Abstract
Prosthetic legs play a pivotal role in clinical rehabilitation, allowing individuals with lower-limb amputations the ability to regain mobility and improve their quality of life. Gait analysis is fundamental for optimizing prosthesis design and alignment, directly impacting the mobility and life quality of individuals with lower-limb amputations. Vision-based machine learning (ML) methods offer a scalable and non-invasive solution to gait analysis, but face challenges in correctly detecting and analyzing prosthesis, due to their unique appearances and new movement patterns. In this paper, we aim to bridge this gap by introducing a multi-purpose dataset, namely ProGait, to support multiple vision tasks including Video Object Segmentation, 2D Human Pose Estimation, and Gait Analysis (GA). ProGait provides 412 video clips from four above-knee amputees when testing multiple newly-fitted prosthetic legs through walking trials, and depicts the presence, contours, poses, and gait patterns of human subjects with transfemoral prosthetic legs. Alongside the dataset itself, we also present benchmark tasks and fine-tuned baseline models to illustrate the practical application and performance of the ProGait dataset. We compared our baseline models against pre-trained vision models, demonstrating improved generalizability when applying the ProGait dataset for prosthesis-specific tasks. Our code is available at this https URL and dataset at this https URL.
Abstract (translated)
假肢在临床康复中扮演着至关重要的角色，能够帮助下肢截肢者恢复行动能力，并提高其生活质量。步态分析对于优化假肢的设计和对准至关重要，直接关系到下肢截肢者的移动能力和生活品质。基于视觉的机器学习（ML）方法为步态分析提供了一种可扩展且非侵入性的解决方案，但由于假肢的独特外观及新的运动模式，这种方法在正确检测和分析假肢方面面临挑战。 本文旨在通过介绍一个多用途数据集——ProGait来填补这一空白，以支持包括视频对象分割、2D人体姿态估计以及步态分析（GA）在内的多种视觉任务。该数据集包含来自四名大腿截肢者在测试多款新装配的假肢时进行行走试验所拍摄的412段视频片段，并描绘了使用股骨上端假肢的人体对象的存在、轮廓、姿势及步态模式。 除了提供数据集本身之外，我们还介绍了基准任务和微调后的基线模型，以展示ProGait数据集的实际应用及其性能。我们将我们的基线模型与预训练的视觉模型进行了比较，结果显示在应用于特定假肢任务时，使用ProGait数据集可以提高泛化能力。 我们的代码可在[此链接](https://this_https_URL)获取，而数据集则可通过[此链接](https://this_https_URL)访问。
URL
https://arxiv.org/abs/2507.10223
