ArticleSensors (Basel, Switzerland)2019
Synergistic Myoelectrical Activities of Forearm Muscles Improving Robust Recognition of Multi-Fingered Gestures.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Rectified Latent Variable Model-Based EMG Factorization of Inhibitory Muscle Synergy Components Related to Aging, Expertise and Force-Tempo Variations.Sensors (Basel, Switzerland) · 2024Article
- Reducing Power Line Interference from sEMG Signals Based on Synchrosqueezed Wavelet Transform.Sensors (Basel, Switzerland) · 2023Article
- Muscle Synergy of Lower Limb Motion in Subjects with and without Knee Pathology.Diagnostics (Basel, Switzerland) · 2021Article
- A Gesture Elicitation Study of Nose-Based Gestures.Sensors (Basel, Switzerland) · 2020Article
- Gyroscope-Based Continuous Human Hand Gesture Recognition for Multi-Modal Wearable Input Device for Human Machine Interaction.Sensors (Basel, Switzerland) · 2019Article
- An Recognition-Verification Mechanism for Real-Time Chinese Sign Language Recognition Based on Multi-Information Fusion.Sensors (Basel, Switzerland) · 2019Article
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Authors and funding
7 authors.
Funding
Abstract
Currently, surface electromyography (sEMG) features of the forearm multi-tendon muscles are widely used in gesture recognition, however, there are few investigations on the inherent physiological mechanism of muscle synergies. We aimed to study whether the muscle synergies could be used for gesture recognition. Five healthy participants executed five gestures of daily life (pinch, fist, open hand, grip, and extension) and the sEMG activity was acquired from six forearm muscles. A non-negative matrix factorization (NMF) algorithm was employed to decompose the pre-treated six-channel sEMG data to obtain the muscle synergy matrixes, in which the weights of each muscle channel determined the feature set for hand gesture classification. The results showed that the synergistic features of forearm muscles could be successfully clustered in the feature space, which enabled hand gestures to be recognized with high efficiency. By augmenting the number of participants, the mean recognition rate remained at more than 96% and reflected high robustness. We showed that muscle synergies can be well applied to gesture recognition.
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