Finger Motion Estimation Based on Sparse Multi-Channel Surface Electromyography Signals Using Convolutional Neural Network

Asai, Keisuke; Takase, Norio

doi:10.1145/3316551.3316572

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…Similarly, the challenges with recruiting amputees may have normalized the use of small numbers of participants in EMG research. HCI EMG research has tended towards smaller studies with very few participants (often less than 10), with some testing on as few as one pilot participant [10,99,156]. The issues around the lack of external and ecological validity and small sample sizes in these studies drastically limit the conclusions that can be drawn from previous work.…”

Section: System Evaluationmentioning

confidence: 99%

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

Eddy

Scheme

Bateman

2023

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

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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. CCS CONCEPTS• Human-centered computing → Interaction techniques; Gestural input; Interaction design.

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Section: System Evaluationmentioning

confidence: 99%