# AutoMR: A Universal Time Series Motion Recognition Pipeline

arXiv: https://arxiv.org/abs/2502.15228 · HTML: https://mlsanity.com/paper/2502.15228/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 31 references that are in the map.
- First posted on arXiv 21 Feb 2025.

## Builds on (most cited first, 10 of 10)

- [QuartzNet: Deep Automatic Speech Recognition with 1D Time-Channel Separable Convolutions](https://mlsanity.com/paper/1910.10261/) (2019)
- [Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning](https://mlsanity.com/paper/1801.07756/) (2018)
- [XAIR: A Framework of Explainable AI in Augmented Reality](https://mlsanity.com/paper/2303.16292/) (2023)
- [sEMG Gesture Recognition with a Simple Model of Attention](https://mlsanity.com/paper/2006.03645/) (2020)
- [A Comparative study of Hyper-Parameter Optimization Tools](https://mlsanity.com/paper/2201.06433/) (2022)
- [The Imaginative Generative Adversarial Network: Automatic Data Augmentation for Dynamic Skeleton-Based Hand Gesture and Human Action Recognition](https://mlsanity.com/paper/2105.13061/) (2021)
- [Layered TPOT: Speeding up Tree-based Pipeline Optimization](https://mlsanity.com/paper/1801.06007/) (2018)
- [Human Activity Recognition Using Multichannel Convolutional Neural Network](https://mlsanity.com/paper/2101.06709/) (2021)
- [Towards Open-World Gesture Recognition](https://mlsanity.com/paper/2401.11144/) (2024)
- [RingGesture: A Ring-Based Mid-Air Gesture Typing System Powered by a Deep-Learning Word Prediction Framework](https://mlsanity.com/paper/2410.18100/) (2024)

## Abstract (arXiv)

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.