# A Framework and Call to Action for the Future Development of EMG-Based Input in HCI

arXiv: https://arxiv.org/abs/2304.00582 · HTML: https://mlsanity.com/paper/2304.00582/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2023.

## Built on by (most cited first, 4 of 4)

- [Posture-Informed Muscular Force Learning for Robust Hand Pressure Estimation](https://mlsanity.com/paper/2410.23629/) (2024)
- [Detecting Domain Shifts in Myoelectric Activations: Challenges and Opportunities in Stream Learning](https://mlsanity.com/paper/2508.21278/) (2025)
- [On-Demand Myoelectric Control Using Wake Gestures to Eliminate False Activations During Activities of Daily Living](https://mlsanity.com/paper/2402.10050/) (2024)
- [Towards Real-Time ECG and EMG Modeling on μNPUs](https://mlsanity.com/paper/2604.18067/) (2026)

## Abstract (arXiv)

Electromyography (EMG) has been explored as an HCI input modality following a long history of success for prosthesis control. While EMG has the potential to address a range of hands-free interaction needs, it has yet to be widely accepted outside of prosthetics due to a perceived lack of robustness and intuitiveness. To understand how EMG input systems can be better designed, we sampled the ACM digital library to identify limitations in the approaches taken. Leveraging these works in combination with our research group's extensive interdisciplinary experience in this field, four themes emerged (1) interaction design, (2) model design, (3) system evaluation, and (4) reproducibility. Using these themes, we provide a step-by-step framework for designing EMG-based input systems to strengthen the foundation on which EMG-based interactions are built. Additionally, we provide a call-to-action for researchers to unlock the hidden potential of EMG as a widely applicable and highly usable input modality.