Surface Electromyography (sEMG) has been commonly applied for analysing the electrical activities of skeletal muscles. The sensory system of maintaining posture balance includes vision, proprioception and vestibular senses. In this work, an attempt is made to classify whether the body is missing one of the sense during balance control by using sEMG signals. A trial of combination with different features and muscles is also developed. The results demonstrate that the classification accuracy between vision loss and the normal condition is higher than the one between vestibular sense loss and normal condition. When using different features and muscles, the impact on classification results is also different. The outcomes of this study could aid the development of sEMG based classification for the function of sensory systems during human balance movement.

Download full-text PDF

Source
http://dx.doi.org/10.1109/EMBC.2017.8037278DOI Listing

Publication Analysis

Top Keywords

semg signals
8
features muscles
8
loss normal
8
normal condition
8
classification
4
classification "equilibrium
4
"equilibrium triad"
4
triad" sensory
4
sensory loss
4
loss based
4

Similar Publications

Objectives: In recent years, significant progress has been made in the research of gesture recognition using surface electromyography (sEMG) signals based on machine learning and deep learning techniques. The main motivation for sEMG gesture recognition research is to provide more natural, convenient, and personalized human-computer interaction, which makes research in this field have considerable application prospects in rehabilitation technology. However, the existing gesture recognition algorithms still need to be further improved in terms of global feature capture, model computational complexity, and generalizability.

View Article and Find Full Text PDF

In human-computer interaction, gesture recognition based on physiological signals offers advantages such as a more natural and fast interaction mode and less constrained by the environment than visual-based. Surface electromyography-based gesture recognition has significantly progressed. However, since individuals have physical differences, researchers must collect data multiple times from each user to train the deep learning model.

View Article and Find Full Text PDF

Background: Surface electromyography (sEMG) has been used in a wide range of studies conducted in the field of dysphagia.

Objectives: The main aim of this case-control study is to obtain how submental and infrahyoid sEMG signals differ based on residue, penetration and aspiration.

Methods: A total of 100 participants (50 patients with suspected dysphagia and 50 healthy controls) were enrolled in the present study.

View Article and Find Full Text PDF

Gesture-controlled reconfigurable metasurface system based on surface electromyography for real-time electromagnetic wave manipulation.

Nanophotonics

January 2025

Key Laboratory for Information Science of Electromagnetic Waves, School of Information Science and Technology, Fudan University, Shanghai 200433, China.

Gesture recognition plays a significant role in human-machine interaction (HMI) system. This paper proposes a gesture-controlled reconfigurable metasurface system based on surface electromyography (sEMG) for real-time beam deflection and polarization conversion. By recognizing the sEMG signals of user gestures through a pre-trained convolutional neural network (CNN) model, the system dynamically modulates the metasurface, enabling precise control of the deflection direction and polarization state of electromagnetic waves.

View Article and Find Full Text PDF

3D printable and myoelectrically sensitive hydrogel for smart prosthetic hand control.

Microsyst Nanoeng

January 2025

Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, 511442, P. R. China.

Surface electromyogram (sEMG) serves as a means to discern human movement intentions, achieved by applying epidermal electrodes to specific body regions. However, it is difficult to obtain high-fidelity sEMG recordings in areas with intricate curved surfaces, such as the body, because regular sEMG electrodes have stiff structures. In this study, we developed myoelectrically sensitive hydrogels via 3D printing and integrated them into a stretchable, flexible, and high-density sEMG electrodes array.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!