# A modular architecture for IMU-based data gloves

arXiv: https://arxiv.org/abs/2401.13254 · HTML: https://mlsanity.com/paper/2401.13254/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2024.

## Built on by (most cited first, 3 of 3)

- [EasyMimic: A Low-Cost Framework for Robot Imitation Learning from Human Videos](https://mlsanity.com/paper/2602.11464/) (2026)
- [A Comparative Study of Human Activity Recognition: Motion, Tactile, and multi-modal Approaches](https://mlsanity.com/paper/2505.08657/) (2025)
- [A Distributed Multi-Modal Sensing Approach for Human Activity Recognition in Real-Time Human-Robot Collaboration](https://mlsanity.com/paper/2602.07024/) (2026)

## Abstract (arXiv)

The flexibility and range of motion in human hands play a crucial role in human interaction with the environment and have been studied across different fields. Researchers explored various technological solutions for gathering information from the hands. These solutions include tracking hand motion through cameras or wearable sensors and using wearable sensors to measure the position and pressure of contact points. Data gloves can collect both types of information by utilizing inertial measurement units, flex sensors, magnetic trackers for motion tracking, and force resistors or touch sensors for contact measurement. Although there are commercially available data gloves, researchers often create custom data gloves to achieve the desired flexibility and control over the hardware. However, the existing literature lacks standardization and the reuse of previously designed data gloves. As a result, many gloves with unclear characteristics exist, which makes replication challenging and negatively impacts the reproducibility of studies. This work proposes a modular, open hardware and software architecture for creating customized data gloves based on IMU technology. We also provide an architecture implementation along with an experimental protocol to evaluate device performance.