[go: up one dir, main page]

Hristov et al., 2021 - Google Patents

Detection of individual finger flexions using two-channel electromyography

Hristov et al., 2021

View PDF
Document ID
11608035119188672235
Author
Hristov B
Nadzinski G
Publication year
Publication venue
ETAI 2021

External Links

Snippet

Due to technological advances in biomedical engineering, electronics, 3D printing and artificial intelligence, there has been a significant increase in the feasibility of producing accurate, fast and fully functional prosthetics. This paper discusses the possibility of …
Continue reading at www.researchgate.net (PDF) (other versions)

Classifications

    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235Details of waveform analysis
    • A61B5/7264Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/06Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • G06N3/0635Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means using analogue means
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/04Architectures, e.g. interconnection topology
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/08Learning methods

Similar Documents

Publication Publication Date Title
Krasoulis et al. Multi-grip classification-based prosthesis control with two EMG-IMU sensors
Betthauser et al. Stable responsive EMG sequence prediction and adaptive reinforcement with temporal convolutional networks
Farina et al. The extraction of neural information from the surface EMG for the control of upper-limb prostheses: emerging avenues and challenges
Benatti et al. Analysis of robust implementation of an EMG pattern recognition based control
Pancholi et al. Advanced energy kernel-based feature extraction scheme for improved EMG-PR-based prosthesis control against force variation
Portnova-Fahreeva et al. Linear and non-linear dimensionality-reduction techniques on full hand kinematics
Al-Qaness et al. TCNN-KAN: Optimized CNN by Kolmogorov-Arnold network and pruning techniques for sEMG gesture recognition
Raurale et al. Emg acquisition and hand pose classification for bionic hands from randomly-placed sensors
Moin et al. Analysis of contraction effort level in emg-based gesture recognition using hyperdimensional computing
Akmal et al. SVM-based real-time classification of prosthetic fingers using myo armband-acquired electromyography data
Thomik et al. Real-time movement prediction for improved control of neuroprosthetic devices
Cene et al. Using the sEMG signal representativity improvement towards upper-limb movement classification reliability
Nieuwoudt et al. Investigation of real-time control of finger movements utilizing surface EMG signals
Wei et al. Classification of human hand movements using surface EMG for myoelectric control
Anam et al. Random forest-based simultaneous and proportional myoelectric control system for finger movements
Hristov et al. Classification of individual and combined finger flexions using machine learning approaches
Heydarzadeh et al. Emg spectral analysis for prosthetic finger control
Hristov et al. Detection of individual finger flexions using two-channel electromyography
CN111923048A (en) Electromyographic signal classification and exoskeleton robot control method and device
Machado et al. Recurrent neural network as estimator for a virtual sEMG channel
Fu et al. Identification of finger movements from forearm surface EMG using an augmented probabilistic neural network
Aishwarya et al. Feature extraction for EMG based prostheses control
Cene et al. Upper-limb movement classification through logistic regression sEMG signal processing
Xiloyannis et al. Dynamic forward prediction for prosthetic hand control by integration of EMG, MMG and kinematic signals
Guerrero-Méndez et al. Decoding semg under non-ideal conditions toward robust muscle-machine interface control