ArticleSensors (Basel, Switzerland)2018
Latent Factors Limiting the Performance of sEMG-Interfaces.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
11 citing papers in PubMed.
- Electrode-Level Low-Dimensionality Does Not Guarantee Sensor Redundancy: Dual-Dataset, Participant-Grouped Validation of Parsimonious Myoelectric Gesture Decoding.Biomimetics (Basel, Switzerland) · 2026Article
- Article
- Machine learning for hand pose classification from phasic and tonic EMG signals during bimanual activities in virtual reality.Frontiers in neuroscience · 2024Article
- Study on the methods of feature extraction based on electromyographic signal classification.Medical & biological engineering & computing · 2023Article
- Neurointerface with oscillator motifs for inhibitory effect over antagonist muscles.Frontiers in neuroscience · 2023Article
- Feature-Based Information Retrieval of Multimodal Biosignals with a Self-Similarity Matrix: Focus on Automatic Segmentation.Biosensors · 2022Article
- Multi-user motion recognition using sEMGFrontiers in neurorobotics · 2022Article
- A Novel EMG-Based Hand Gesture Recognition Framework Based on Multivariate Variational Mode Decomposition.Sensors (Basel, Switzerland) · 2021Article
- Development of a Low-Cost, Modular Muscle-Computer Interface for At-Home Telerehabilitation for Chronic Stroke.Sensors (Basel, Switzerland) · 2021Article
- Critical Issues and Imminent Challenges in the Use of sEMG in Return-To-Work Rehabilitation of Patients Affected by Neurological Disorders in the Epoch of Human-Robot Collaborative Technologies.Frontiers in neurology · 2020Article
- A Neuromuscular Interface for Robotic Devices Control.Computational and mathematical methods in medicine · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advances in recording and real-time analysis of surface electromyographic signals (sEMG) have fostered the use of sEMG human-machine interfaces for controlling personal computers, prostheses of upper limbs, and exoskeletons among others. Despite a relatively high mean performance, sEMG-interfaces still exhibit strong variance in the fidelity of gesture recognition among different users. Here, we systematically study the latent factors determining the performance of sEMG-interfaces in synthetic tests and in an arcade game. We show that the degree of muscle cooperation and the amount of the body fatty tissue are the decisive factors in synthetic tests. Our data suggest that these factors can only be adjusted by long-term training, which promotes fine-tuning of low-level neural circuits driving the muscles. Short-term training has no effect on synthetic tests, but significantly increases the game scoring. This implies that it works at a higher decision-making level, not relevant for synthetic gestures. We propose a procedure that enables quantification of the gestures' fidelity in a dynamic gaming environment. For each individual subject, the approach allows identifying "problematic" gestures that decrease gaming performance. This information can be used for optimizing the training strategy and for adapting the signal processing algorithms to individual users, which could be a way for a qualitative leap in the development of future sEMG-interfaces.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.