[go: up one dir, main page]

Orjuela-Cañón et al., 2017 - Google Patents

Deep neural network for EMG signal classification of wrist position: Preliminary results

Orjuela-Cañón et al., 2017

Document ID
13479499280192508223
Author
Orjuela-Cañón A
Ruíz-Olaya A
Forero L
Publication year
Publication venue
2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI)

External Links

Snippet

Physically impaired people may use Surface Electromyography (SEMG) signals to control rehabilitation and assistive devices. SEMG is the electrical manifestation of the neuromuscular activation associated with a contracting muscle. SEMG directly reflects the …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/18—Applying electric currents by contact electrodes
    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation, e.g. heart pace-makers
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7232—Signal processing specially adapted for physiological signals or for diagnostic purposes involving compression of the physiological signal, e.g. to extend the signal recording period
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/04—Detecting, measuring or recording bioelectric signals of the body of parts thereof
    • A61B5/0402—Electrocardiography, i.e. ECG
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/04—Detecting, measuring or recording bioelectric signals of the body of parts thereof
    • A61B5/0488—Electromyography
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/04—Detecting, measuring or recording bioelectric signals of the body of parts thereof
    • A61B5/0476—Electroencephalography
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1116—Determining posture transitions
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61F—FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, E.G. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS
    • A61F2/00—Filters implantable into blood vessels; Prostheses, i.e. artificial substitutes or replacements for parts of the body; Appliances for connecting them with the body; Devices providing patency to, or preventing collapsing of, tubular structures of the body, e.g. stents
    • A61F2/50—Prostheses not implantable in the body
    • A61F2/54—Artificial arms or hands or parts thereof
    • A61F2/58—Elbows; Wrists; Other joints; Hands

Similar Documents

Publication Publication Date Title
Orjuela-Cañón et al. Deep neural network for EMG signal classification of wrist position: Preliminary results
Wu et al. A CNN-SVM combined model for pattern recognition of knee motion using mechanomyography signals
Al-Angari et al. Distance and mutual information methods for EMG feature and channel subset selection for classification of hand movements
Ison et al. The role of muscle synergies in myoelectric control: trends and challenges for simultaneous multifunction control
Tang et al. Continuous estimation of human upper limb joint angles by using PSO-LSTM model
Lv et al. Hand gestures recognition from surface electromyogram signal based on self-organizing mapping and radial basis function network
Sosin et al. Continuous gesture recognition from sEMG sensor data with recurrent neural networks and adversarial domain adaptation
Li et al. Wireless sEMG-based identification in a virtual reality environment
Briouza et al. A brief overview on machine learning in rehabilitation of the human arm via an exoskeleton robot
Pancholi et al. A robust and accurate deep learning based pattern recognition framework for upper limb prosthesis using sEMG
Pancholi et al. DLPR: Deep learning-based enhanced pattern recognition frame-work for improved myoelectric prosthesis control
Byfield et al. Real-time classification of hand motions using electromyography collected from minimal electrodes for robotic control
Xiong et al. An user-independent gesture recognition method based on sEMG decomposition
Ke et al. Intersected EMG heatmaps and deep learning based gesture recognition
Ruiz-Olaya et al. EMG-based pattern recognition with kinematics information for hand gesture recognition
Song et al. Recognition of motion of human upper limb using semg in real time: Towards bilateral rehabilitation
Osborn et al. Monitoring at-home prosthesis control improvements through real-time data logging
Li et al. sEMG based joint angle estimation of lower limbs using LS-SVM
Wu et al. A CNN-SVM combined regression model for continuous knee angle estimation using mechanomyography signals
Abusedra et al. Lower limb exoskeleton control using EMG signal analysis
Karuna et al. Classification of hand movements via emg using machine learning methods for prosthesis
Pancholi et al. Novel time domain based upper-limb prosthesis control using incremental learning approach
Tresadern et al. Artificial neural network prediction using accelerometers to control upper limb FES during reaching and grasping following stroke
Mathew et al. Surface electromyogram based techniques for upper and lower extremity rehabilitation therapy-A comprehensive review
Nazari et al. Hand movements detection using emg signals for human-computer interface and convolution neural network