# Surface Electromyography as a Natural Human-Machine Interface: A Review

arXiv: https://arxiv.org/abs/2101.04658 · HTML: https://mlsanity.com/paper/2101.04658/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jan 2021.

## Built on by (most cited first, 7 of 7)

- [Online Transformers with Spiking Neurons for Fast Prosthetic Hand Control](https://mlsanity.com/paper/2303.11860/) (2023)
- [A Non-Intrusive Neural Quality Assessment Model for Surface Electromyography Signals](https://mlsanity.com/paper/2402.05482/) (2024)
- [Random Channel Ablation for Robust Hand Gesture Classification with Multimodal Biosignals](https://mlsanity.com/paper/2407.10874/) (2024)
- [sEMG-Driven Physics-Informed Gated Recurrent Networks for Modeling Upper Limb Multi-Joint Movement Dynamics](https://mlsanity.com/paper/2408.16599/) (2024)
- [An Evolutionary Network Architecture Search Framework with Adaptive Multimodal Fusion for Hand Gesture Recognition](https://mlsanity.com/paper/2403.18208/) (2024)
- [Simultaneous Estimation of Manipulation Skill and Hand Grasp Force from Forearm Ultrasound Images](https://mlsanity.com/paper/2502.00275/) (2025)
- [Deep Neural Networks for Learning Intent from sEMG Signals to Support Hardware Devices for Post-Stroke Neurorehabilitation](https://mlsanity.com/paper/2609.09971/) (2026)

## Abstract (arXiv)

Surface electromyography (sEMG) is a non-invasive method of measuring neuromuscular potentials generated when the brain instructs the body to perform both fine and coarse locomotion. This technique has seen extensive investigation over the last two decades, with significant advances in both the hardware and signal processing methods used to collect and analyze sEMG signals. While early work focused mainly on medical applications, there has been growing interest in utilizing sEMG as a sensing modality to enable next-generation, high-bandwidth, and natural human-machine interfaces. In the first part of this review, we briefly overview the human skeletomuscular physiology that gives rise to sEMG signals followed by a review of developments in sEMG acquisition hardware. Special attention is paid towards the fidelity of these devices as well as form factor, as recent advances have pushed the limits of user comfort and high-bandwidth acquisition. In the second half of the article, we explore work quantifying the information content of natural human gestures and then review the various signal processing and machine learning methods developed to extract information in sEMG signals. Finally, we discuss the future outlook in this field, highlighting the key gaps in current methods to enable seamless natural interactions between humans and machines.