ArticleNature communications2023
A myoelectric digital twin for fast and realistic modelling in deep learning.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed.
- Timesynth: A Temporal Fidelity Framework for Health Signal Digital Twins.Research square · 2026Article
- SR-FSL: Sample reconstruction enhanced few-shot learning for real-time motor unit identification from surface electromyogram.Journal of neuroengineering and rehabilitation · 2026Article
- Review
- Review of electroencephalography and electromyography research in robotics: opportunities and challenges.Visual computing for industry, biomedicine, and art · 2026Review
- Giga-Voxel Multiscale Composite Architecture Mirrored Through a Data-to-Model Closed-Loop Digital Twin.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- A scoping review of human digital twins in healthcare applications and usage patterns.NPJ digital medicine · 2025Article
- Peripheral neural interfaces for reading high-frequency brain signals.Nature biomedical engineering · 2025Review
- Physics-informed machine learning digital twin for reconstructing prostate cancer tumor growth via PSA tests.NPJ digital medicine · 2025Article
- A Technological Review of Digital Twins and Artificial Intelligence for Personalized and Predictive Healthcare.Healthcare (Basel, Switzerland) · 2025Review
- Transforming Healthcare: Intelligent Wearable Sensors Empowered by Smart Materials and Artificial Intelligence.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Unlocking the full potential of high-density surface EMG: novel non-invasive high-yield motor unit decomposition.The Journal of physiology · 2025Article
- Intelligent Assessment Techniques for Abnormal Movement Patterns in Neurological Disorders: Applications and Advances.Behavioural neurology · 2025Review
- Definitions and Characteristics of Patient Digital Twins Being Developed for Clinical Use: Scoping Review.Journal of medical Internet research · 2024Article
- Article
- Advances in Machine Learning for Wearable Sensors.ACS nano · 2024Review
- NeuroMotion: Open-source platform with neuromechanical and deep network modules to generate surface EMG signals during voluntary movement.PLoS computational biology · 2024Article
- Metrology in sEMG and movement analysis: the need for training new figures in clinical rehabilitation.Frontiers in rehabilitation sciences · 2024Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Muscle electrophysiology has emerged as a powerful tool to drive human machine interfaces, with many new recent applications outside the traditional clinical domains, such as robotics and virtual reality. However, more sophisticated, functional, and robust decoding algorithms are required to meet the fine control requirements of these applications. Deep learning has shown high potential in meeting these demands, but requires a large amount of high-quality annotated data, which is expensive and time-consuming to acquire. Data augmentation using simulations, a strategy applied in other deep learning applications, has never been attempted in electromyography due to the absence of computationally efficient models. We introduce a concept of Myoelectric Digital Twin - highly realistic and fast computational model tailored for the training of deep learning algorithms. It enables simulation of arbitrary large and perfectly annotated datasets of realistic electromyography signals, allowing new approaches to muscular signal decoding, accelerating the development of human-machine interfaces.
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What OpenQuestion holds
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.