A Framework and Call to Action for the Future Development of EMG-Based Input in HCI
Ethan Eddy, Erik J. Scheme, Scott Bateman
摘要
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.
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- Posture-Informed Muscular Force Learning for Robust Hand Pressure EstimationKyung Jin Seo, Junghoon Seo, Hanseok Jeong, Sangpil Kim 等NeurIPS 2024 · 被引用 14 次
- From Pose to Muscle: Multimodal Learning for Piano Hand Muscle ElectromyographyRuofan Liu, Yichen Peng, Takanori Oku, Chen-Chieh Liao 等NeurIPS 2025 · 被引用 6 次
- What We Talk About When We Talk About Frameworks in HCIShitao Fang, Koji Yatani, Kasper HornbækCHI 2026 · 被引用 4 次
- SparseEMG: Computational Design of Sparse EMG Layouts for Sensing GesturesAnand Kumar, Antony Albert Raj Irudayaraj, Ishita Chandra, Adwait Sharma 等UIST 2025 · 被引用 4 次
- Myo Action: Accelerating Voluntary Actions via Electromyography and Muscle StimulationYudai Tanaka, Che-Wei Hsu, Bruno Felalaga, Pedro LopesCHI 2026 · 被引用 2 次
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