A myoelectric digital twin for fast and realistic modelling in deep learning
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Summary
A concept of Myoelectric Digital Twin - highly realistic and fast computational model tailored for the training of deep learning algorithms that 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.
- Type
- article
- Published
- 2023-03-23
- Cited by
- 52
- References
- 58
- Access
- Open access
- OpenAlex
- https://openalex.org/W36959193
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:257669123
Keywords
Political science
References
- Relation between size of neurons and their susceptibility to discharge.
- Fast generation model of high density surface EMG signals in a cylindrical conductor volume
- Automated Solution of Differential Equations by the Finite Element Method: The FEniCS Book
- Intra- and extracellular potential fields of active nerve and muscle fibres. A physico-mathematical analysis of different models.
- Models of recruitment and rate coding organization in motor-unit pools.
- Theory of current source-density analysis and determination of conductivity tensor for anuran cerebellum.
- Evaluation of muscle force classification using shape analysis of the sEMG probability density function: a simulation study
- The active fiber in a volume conductor.
- Precise and fast calculation of the motor unit potentials detected by a point and rectangular plate electrode.
- Detection of motor unit action potentials with surface electrodes: influence of electrode size and spacing
- Influence of motor unit synchronization on amplitude characteristics of surface and intramuscularly recorded EMG signals
- The adjoint method for general EEG and MEG sensor-based lead field equations
- Finite limb dimensions and finite muscle length in a model for the generation of electromyographic signals.
- Muscle fiber action potential changes and surface EMG: A simulation study.
- Considerations of quasi-stationarity in electrophysiological systems.
- Advances in surface electromyographic signal simulation with analytical and numerical descriptions of the volume conductor
- Limitations of the surface electromyography technique for estimating motor unit synchronization
- The Extraction of Neural Information from the Surface EMG for the Control of Upper-Limb Prostheses: Emerging Avenues and Challenges
- Influence of motor unit properties on the size of the simulated evoked surface EMG potential
- Solution Methods of Electrical Field Problems in Physiology
Cited by
- I-Spin live: An open-source software based on blind-source separation for real-time decoding of motor unit activity in humans
- Motor unit placement in a realistic muscle cross section model: performance of a new algorithm and effects of muscle architecture on surface EMG power spectral components
- I-Spin live, an open-source software based on blind-source separation for real-time decoding of motor unit activity in humans
- Digital twin and cross-scale mechanical interaction for fabric rubber composites considering model uncertainties
- Metrology in sEMG and movement analysis: the need for training new figures in clinical rehabilitation
- OpenDiHu: An efficient and scalable framework for biophysical simulations of the neuromuscular system
- Adaptive EMG decomposition in dynamic conditions based on online learning metrics with tunable hyperparameters
- NeuroMotion: Open-source platform with neuromechanical and deep network modules to generate surface EMG signals during voluntary movement
- A review of in-situ measurement and simulation technologies for ceramic sintering: towards a digital twin sintering system
- Advances in Machine Learning for Wearable Sensors
- A Dual‐Mode, Scalable, Machine‐Learning‐Enhanced Wearable Sensing System for Synergetic Muscular Activity Monitoring
- Definitions and Characteristics of Patient Digital Twins Being Developed for Clinical Use: Scoping Review
- Peripheral neural interfaces for reading high-frequency brain signals
- Minimally Invasive Motor Function Rehabilitation Through Digital Twin Technology: A Review
- A U-Net based partial convolutional time-domain separation model to identify motor units from surface electromyographic signals in real time.
- High-density electromyography for effective gesture-based control of physically assistive mobile manipulators
- Revolutionizing biological digital twins: Integrating internet of bio-nano things, convolutional neural networks, and federated learning
- Unlocking the full potential of high‐density surface EMG: novel non‐invasive high‐yield motor unit decomposition
- Transforming Healthcare: Intelligent Wearable Sensors Empowered by Smart Materials and Artificial Intelligence
- Representation of Human arm Dynamic Intents With an Electrical Impedance Tomography (EIT)-Driven Musculoskeletal Model for Human–Robot Interaction
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