CAPG-MYO: A Muscle-Computer Interface Supporting User-defined Gesture Recognition

Dai, Qingfeng; Li, Xiangdong; Wang, Geng; Jin, Weiliang; Liang, Xiubo

doi:10.1145/3479162.3479170

Cited by 8 publications

(4 citation statements)

References 20 publications

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“…With the rapid evolution of virtual and augmented reality, there is growing interest in novel interaction techniques that do not rely on physical devices, like mice or keyboards, as controllers. As such, there has been a renewed interest in the adoption of EMG technology in research (e.g., [43,74,171]) and commercial applications (e.g., [83]). However, we believe that for the successful adoption of EMG in HCI, we must deepen our understanding of its use and define new directions for improved systems that are not explored meaningfully in the body of prosthetics research.…”

Section: Emg In Hcimentioning

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: Emg In Hcimentioning

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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“…Koh et al [14] addressed this issue, by facilitating the use of an alternative form of VCM entry: hand gestures. Moreover, to fill this gap Dai et al [7] presented a training system, called CAPG-MYO, for userdefined hand gesture interaction. Takayama et al [22] perform two user studies to derive a user-defined gesture set allowing 13 types of table manipulation.…”

Section: User Interfaces For User-defined Hand Gesturementioning

confidence: 99%

User-Defined Hand Gesture Interface to Improve User Experience of Learning American Sign Language

Wang

Ivrissimtzis

et al. 2023

Lecture Notes in Computer Science

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Sign language can make possible effective communication between hearing and deaf-mute people. Despite years of extensive pedagogical research, learning sign language remains a formidable task, with the majority of the current systems relying extensively on online learning resources, presuming that users would regularly access them; yet, this approach can feel monotonous and repetitious. Recently, gamification has been proposed as a solution to the problem, however, the research focus is on game design, rather than user experience design. In this work, we present a system for user-defined interaction for learning static American Sign Language (ASL), supporting gesture recognition for user experience design, and enabling users to actively learn through involvement with user-defined gestures, rather than just passively absorbing knowledge. Early findings from a questionnaire-based survey show that users are more motivated to learn static ASL through user-defined interactions.

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“…The sEMG signal is recorded using electrode contact with the skin during the contraction of skeletal muscles [ 24 ], which is non-invasive and robust to environmental conditions. Recently, sEMG-based gesture recognition has attracted much attention due to its broad potential in the area of sign language, medical rehabilitation, virtual reality, and so on [ 25 ].…”

Section: Related Workmentioning

confidence: 99%

Improved Network and Training Scheme for Cross-Trial Surface Electromyography (sEMG)-Based Gesture Recognition

Dai,

Wong,

Kankanhali

et al. 2023

Bioengineering

Self Cite

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To enhance the performance of surface electromyography (sEMG)-based gesture recognition, we propose a novel network-agnostic two-stage training scheme, called sEMGPoseMIM, that produces trial-invariant representations to be aligned with corresponding hand movements via cross-modal knowledge distillation. In the first stage, an sEMG encoder is trained via cross-trial mutual information maximization using the sEMG sequences sampled from the same time step but different trials in a contrastive learning manner. In the second stage, the learned sEMG encoder is fine-tuned with the supervision of gesture and hand movements in a knowledge-distillation manner. In addition, we propose a novel network called sEMGXCM as the sEMG encoder. Comprehensive experiments on seven sparse multichannel sEMG databases are conducted to demonstrate the effectiveness of the training scheme sEMGPoseMIM and the network sEMGXCM, which achieves an average improvement of +1.3% on the sparse multichannel sEMG databases compared to the existing methods. Furthermore, the comparison between training sEMGXCM and other existing networks from scratch shows that sEMGXCM outperforms the others by an average of +1.5%.

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CAPG-MYO: A Muscle-Computer Interface Supporting User-defined Gesture Recognition

Cited by 8 publications

References 20 publications

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

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

User-Defined Hand Gesture Interface to Improve User Experience of Learning American Sign Language

Improved Network and Training Scheme for Cross-Trial Surface Electromyography (sEMG)-Based Gesture Recognition

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