Goal: Gait analysis using inertial measurement units (IMUs) has emerged as a promising method for monitoring movement disorders. However, the lack of public data and easy-to-use open-source algorithms hinders method comparison and clinical application development. To address these challenges, this publication introduces the gaitmap ecosystem, a comprehensive set of open source Python packages for gait analysis using foot-worn IMUs. Methods: This initial release includes over 20 state-of-the-art algorithms, enables easy access to seven datasets, and provides eight benchmark challenges with reference implementations. Together with its extensive documentation and tooling, it enables rapid development and validation of new algorithm and provides a foundation for novel clinical applications. Conclusion: The published software projects represent a pioneering effort to establish an open-source ecosystem for IMU-based gait analysis. We believe that this work can democratize the access to high-quality algorithm and serve as a driver for open and reproducible research in the field of human gait analysis and beyond.

中文翻译：

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Gaitmap——基于 IMU 的人类步态分析和算法基准测试的开放生态系统

目标：使用惯性测量单元 (IMU) 进行步态分析已成为监测运动障碍的一种有前途的方法。然而，缺乏公开数据和易于使用的开源算法阻碍了方法比较和临床应用开发。为了应对这些挑战，本出版物介绍了步态图生态系统，这是一套全面的开源 Python 包，用于使用脚戴式 IMU 进行步态分析。方法：这个初始版本包括 20 多种最先进的算法，可以轻松访问七个数据集，并提供八个基准挑战和参考实现。连同其广泛的文档和工具，它可以快速开发和验证新算法，并为新颖的临床应用奠定基础。结论：已发布的软件项目代表了为基于 IMU 的步态分析建立开源生态系统的开创性努力。我们相信这项工作可以使高质量算法的获取民主化，并成为人类步态分析及其他领域开放和可重复研究的驱动力。