Vonsevych et al., 2019 - Google Patents
Fingers movements control system based on artificial neural network modelVonsevych et al., 2019
View PDF- Document ID
- 3237095156871514083
- Author
- Vonsevych K
- Goethel M
- Mrozowski J
- Awrejcewicz J
- Bezuglyi M
- Publication year
- Publication venue
- Radioelectronics and Communications Systems
External Links
Snippet
Surface electromyographic (sEMG) signal is used in the various fields of applications where the need exists to measure the activity of body muscles, such as brain-computer interfaces, game industry, medical engineering, and other practical spheres. Even more, the use of …
- 238000003062 neural network model 0 title description 2
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
- A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
-
- 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
- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
- A61B5/6802—Sensor mounted on worn items
-
- 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
- A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radiowaves
- A61B5/053—Measuring electrical impedance or conductance of a portion of the body
-
- 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
- A61F2/68—Operating or control means
- A61F2/70—Operating or control means electrical
- A61F2002/704—Operating or control means electrical computer-controlled, e.g. robotic control
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Vonsevych et al. | Fingers movements control system based on artificial neural network model | |
Bi et al. | A review on EMG-based motor intention prediction of continuous human upper limb motion for human-robot collaboration | |
Kim et al. | A subject-transfer framework based on single-trial EMG analysis using convolutional neural networks | |
Blana et al. | Feasibility of using combined EMG and kinematic signals for prosthesis control: A simulation study using a virtual reality environment | |
Bunderson et al. | Quantification of feature space changes with experience during electromyogram pattern recognition control | |
Zecca et al. | Control of multifunctional prosthetic hands by processing the electromyographic signal | |
Das et al. | A review on the advancements in the field of upper limb prosthesis | |
Jafarzadeh et al. | Deep learning approach to control of prosthetic hands with electromyography signals | |
CN110675933B (en) | Finger mirror image rehabilitation training system | |
Amanpreet | Machine learning-based novel approach to classify the shoulder motion of upper limb amputees | |
Briouza et al. | A brief overview on machine learning in rehabilitation of the human arm via an exoskeleton robot | |
Wu et al. | Classification and simulation of process of linear change for grip force at different grip speeds by using supervised learning based on sEMG | |
Subasi et al. | sEMG signal classification using DWT and bagging for basic hand movements | |
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 | |
Wang et al. | Multi-finger myoelectric signals for controlling a virtual robotic prosthetic hand | |
Kakoty et al. | Electromyographic grasp recognition for a five fingered robotic hand | |
Ahmad | Moving approximate entropy and its application to the electromyographic control of an artificial hand | |
Ghanaei et al. | Taguchi Design of Experiments Application in Robust sEMG Based Force Estimation | |
Wolczowski et al. | Control of artificial hand via recognition of EMG signals | |
Gardner et al. | EMG based simultaneous wrist motion prediction using reinforcement learning | |
Vonsevych et al. | Features of lowchannel sEMG and FMG control systems for the biomechatronic solution of human fingers replacement | |
Joshua Samuel Raj et al. | Design of Human Adaptive Mechatronics Controller for Upper Limb Motion Intention Prediction. | |
Mathew et al. | Surface electromyogram based techniques for upper and lower extremity rehabilitation therapy-A comprehensive review | |
Villarejo et al. | Identification of low level sEMG signals for individual finger prosthesis |