AutoMR: A Universal Time Series Motion Recognition Pipeline

TL;DR

AutoMR is an automated, end-to-end pipeline for multimodal time series motion recognition that simplifies data preprocessing, hyperparameter tuning, and evaluation, achieving state-of-the-art results across diverse datasets.

Contribution

The paper introduces AutoMR, a universal framework that automates motion recognition tasks, addressing data variability and hyperparameter tuning challenges with a comprehensive, adaptable solution.

Findings

Achieves state-of-the-art performance on 10 diverse datasets.

Automates hyperparameter tuning and data preprocessing.

Demonstrates robustness across various real-world scenarios.

Abstract

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.