LibEMG: An Open Source Library to Facilitate the Exploration of Myoelectric Control

Abstract

Myoelectric control has been used predominantly in the field of prosthetics, but is an increasingly promising hands-free input modality for emerging consumer markets such as mixed reality. Developing robust machine learning-enabled EMG control systems, however, has historically required substantial domain expertise. This has presented a significant barrier to entry for researchers, impeded progress in EMG-based interaction design, and contributed to the perception that such systems lack the robustness and intuitiveness required for real-world use. To overcome these challenges, we present LibEMG, an open-source Python library for performing offline EMG analyses and developing online EMG-based interactions. By abstracting the challenges and nuances surrounding myoelectric control, including hardware interfacing, data acquisition, feature extraction/selection, classification, post-processing, and evaluation, we eliminate many of the significant barriers limiting the exploration of this technology. Combining expertise from the prosthetics and human-computer interaction communities into a shared library, extensive examples, and documentation, we provide researchers with an accessible tool to accelerate research and improve reproducibility in myoelectric control. In doing so, we aim to facilitate the exploration of this technology, particularly outside prosthesis control, to unlock its potential as a widely applicable hands-free input modality. Documentation: https://libemg.github.io/libemg/.

Keywords

• EMG
• electromyography
• toolkit
• library
• myoelectric control
• gesture recognition