SynthesisSensors (Basel, Switzerland)2020
Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity.
Synthesis in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
What it found
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Who cites it
30 citing papers in PubMed.
- Multigesture Electromyographic Control Complexity in Upper Limb Prostheses Actuated via Single Sensor Input Contraction Magnitude: Qualitative Study for Evaluating Performance and Cognitive Load.JMIR rehabilitation and assistive technologies · 2026Article
- A Deep Learning-Based EIT System for Robust Gesture Recognition Under Confounding Factors.Biosensors · 2026Article
- Muscle myography for human-machine interfaces: a review of sensing modalities and control interfaces.Frontiers in bioengineering and biotechnology · 2026Review
- Cloud-Based Personalized sEMG Classification Using Lightweight CNNs for Long-Term Haptic Communication in Deaf-Blind Individuals.Bioengineering (Basel, Switzerland) · 2025Article
- Facilitated Effects of Closed-Loop Assessment and Training on Trans-Radial Prosthesis User Rehabilitation.Sensors (Basel, Switzerland) · 2025Article
- A Consecutive Multi-Day High-Density Surface Electromyography Dataset Comprising 7 Grasps and 11 Gestures.Scientific data · 2025Article
- EMG Dataset for Gesture Recognition with Arm Translation.Scientific data · 2025Article
- Article
- Continuous reach-to-grasp motion recognition based on an extreme learning machine algorithm using sEMG signals.Physical and engineering sciences in medicine · 2024Article
- Surface electromyography dataset from different movements of the hand using a portable and a non-portable device.Data in brief · 2024Article
- Human-in-the-Loop Trajectory Optimization Based on sEMG Biofeedback for Lower-Limb Exoskeleton.Sensors (Basel, Switzerland) · 2024Article
- Unravelling Influence Factors in Pattern Recognition Myoelectric Control Systems: The Impact of Limb Positions and Electrode Shifts.Sensors (Basel, Switzerland) · 2024Article
- Context-informed incremental learning improves both the performance and resilience of myoelectric control.Journal of neuroengineering and rehabilitation · 2024Article
- Big data in myoelectric control: large multi-user models enable robust zero-shot EMG-based discrete gesture recognition.Frontiers in bioengineering and biotechnology · 2024Article
- Enhancing prosthetic hand control: A synergistic multi-channel electroencephalogram.Wearable technologies · 2024Article
- A multifaceted suite of metrics for comparative myoelectric prosthesis controller research.PloS one · 2024Article
- [Targeted muscle reinnervation: a surgical technique of human-machine interface for intelligent prosthesis].Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery · 2023Review
- Simultaneous assessment and training of an upper-limb amputee using incremental machine-learning-based myocontrol: a single-case experimental design.Journal of neuroengineering and rehabilitation · 2023Article
- Exploring the EMG transient: the muscular activation sequences used as novel time-domain features for hand gestures classification.Frontiers in neurorobotics · 2023Article
- Neuromuscular adaptations after osseointegration of a bone-anchored prosthesis in a unilateral transfemoral amputee - a case study.Annals of medicine · 2023Observational
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
This manuscript presents a hybrid study of a comprehensive review and a systematic(research) analysis. Myoelectric control is the cornerstone ofmany assistive technologies used in clinicalpractice, such as prosthetics and orthoses, and human-computer interaction, such as virtual reality control.Although the classification accuracy of such devices exceeds 90% in a controlled laboratory setting,myoelectric devices still face challenges in robustness to variability of daily living conditions.The intrinsic physiological mechanisms limiting practical implementations of myoelectric deviceswere explored: the limb position effect and the contraction intensity effect. The degradationof electromyography (EMG) pattern recognition in the presence of these factors was demonstratedon six datasets, where classification performance was 13% and 20% lower than the controlledsetting for the limb position and contraction intensity effect, respectively. The experimental designsof limb position and contraction intensity literature were surveyed. Current state-of-the-art trainingstrategies and robust algorithms for both effects were compiled and presented. Recommendationsfor future limb position effect studies include: the collection protocol providing exemplars of at least 6positions (four limb positions and three forearm orientations), three-dimensional space experimentaldesigns, transfer learning approaches, and multi-modal sensor configurations. Recommendationsfor future contraction intensity effect studies include: the collection of dynamic contractions, nonlinearcomplexity features, and proportional control.
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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.