A Framework and Call to Action for the Future Development of EMG-Based Input in HCI
Ethan Eddy, Erik J. Scheme, Scott Bateman
Abstract
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.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 54e4ee16-1cfb-4664-b0ef-1e0966af21cdCited by top-tier papers7
- Posture-Informed Muscular Force Learning for Robust Hand Pressure EstimationKyung Jin Seo, Junghoon Seo, Hanseok Jeong, Sangpil Kim et al.NeurIPS 2024 · 14 citations
- From Pose to Muscle: Multimodal Learning for Piano Hand Muscle ElectromyographyRuofan Liu, Yichen Peng, Takanori Oku, Chen-Chieh Liao et al.NeurIPS 2025 · 6 citations
- What We Talk About When We Talk About Frameworks in HCIShitao Fang, Koji Yatani, Kasper HornbækCHI 2026 · 4 citations
- SparseEMG: Computational Design of Sparse EMG Layouts for Sensing GesturesAnand Kumar, Antony Albert Raj Irudayaraj, Ishita Chandra, Adwait Sharma et al.UIST 2025 · 4 citations
- Myo Action: Accelerating Voluntary Actions via Electromyography and Muscle StimulationYudai Tanaka, Che-Wei Hsu, Bruno Felalaga, Pedro LopesCHI 2026 · 2 citations
Related papers
- Reading Your Actions: Learning Generalizable Action Representations via Pre-training AEMGZhenghao Huang, Huilin Yao, Kaikai Wang, Lin ShuCVPR 2026
- Understanding User Acceptance of Electrical Muscle Stimulation in Human-Computer InteractionSarah Faltaous, Julie R. Williamson, Marion Koelle, Max Pfeiffer et al.CHI 2024 · 10 citations
- Open, Accurate, and Calibration-Free Muscle-Computer InterfacesEthan Eddy, Evan Campbell, Erik J. Scheme, Scott BatemanCHI 2026 · 1 citation
- Imagine, Interact: Eliciting Accessible Interactions from Users with Motor Impairments via Imagined Input DevicesRadu-Daniel Vatavu, Ovidiu-Ciprian UngureanCHI 2026 · 1 citation
- WR-Hand: Wearable Armband Can Track User's HandYang Liu, Chengdong Lin, Zhenjiang LiUbiComp 2021 · 30 citations
