AutoMR自动处理多模态动作识别，省去手动调参和数据格式适配。
AutoMR: A Universal Time Series Motion Recognition Pipeline
- 端到端流程整合预处理、训练、调参与评估，支持多源传感器数据。
- 在10个不同数据集上实现领先性能，减少人工干预耗时。
- 适合需要快速部署动作识别系统的研发团队使用。
本文提出一个端到端的自动化动作识别（AutoMR）流程，适用于多模态数据集。该框架无缝集成数据预处理、模型训练、超参数调优与评估，可在多样场景下保持稳健表现。针对两大挑战：1）不同数据集间传感器数据格式与参数差异大，传统方法需为每项任务定制机器学习方案；2）超参数调优复杂且耗时。所提工具库采用QuartzNet作为核心模型，集成自动化超参数优化与全面指标追踪。在10个不同数据集上的大量实验验证了其有效性，达到当前最优性能。本工作为在多种真实应用中部署动作捕捉系统奠定了坚实基础。
原文摘要 · Abstract (English)
In this paper, we present an end-to-end automated motion recognition (AutoMR) pipeline designed for multimodal datasets. The proposed framework seamlessly integrates data preprocessing, model training, hyperparameter tuning, and evaluation, enabling robust performance across diverse scenarios. Our approach addresses two primary challenges: 1) variability in sensor data formats and parameters across datasets, which traditionally requires task-specific machine learning implementations, and 2) the complexity and time consumption of hyperparameter tuning for optimal model performance. Our library features an all-in-one solution incorporating QuartzNet as the core model, automated hyperparameter tuning, and comprehensive metrics tracking. Extensive experiments demonstrate its effectiveness on 10 diverse datasets, achieving state-of-the-art performance. This work lays a solid foundation for deploying motion-capture solutions across varied real-world applications.
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