[go: up one dir, main page]

Zhou et al., 2019 - Google Patents

Deep learning based gesture recognition and its application in interactive control of intelligent wheelchair

Zhou et al., 2019

Document ID
5074444898822580054
Author
Zhou X
Wang F
Wang J
Wang Y
Yan J
Zhou G
Publication year
Publication venue
International Conference on Intelligent Robotics and Applications

External Links

Snippet

With the development of robotics technology, new human-robot interaction technology has gradually received more and more attention. Bioelectric-based gesture recognition, which is to be studied in this article, has become a frontier subject of new human-robot interaction …
Continue reading at link.springer.com (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F3/00Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/03Arrangements for converting the position or the displacement of a member into a coded form
    • G06F3/033Pointing devices displaced or positioned by the user, e.g. mice, trackballs, pens or joysticks; Accessories therefor
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F3/00Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/011Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
    • G06F3/014Hand-worn input/output arrangements, e.g. data gloves
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F3/00Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/011Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
    • G06F3/015Input arrangements based on nervous system activity detection, e.g. brain waves [EEG] detection, electromyograms [EMG] detection, electrodermal response detection
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F3/00Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/017Gesture based interaction, e.g. based on a set of recognized hand gestures
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00335Recognising movements or behaviour, e.g. recognition of gestures, dynamic facial expressions; Lip-reading
    • G06K9/00355Recognition of hand or arm movements, e.g. recognition of deaf sign language
    • 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
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F2203/00Indexing scheme relating to G06F3/00 - G06F3/048
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions

Similar Documents

Publication Publication Date Title
Yang et al. Dynamic gesture recognition using surface EMG signals based on multi-stream residual network
Qi et al. An adaptive reinforcement learning-based multimodal data fusion framework for human–robot confrontation gaming
Geethanjali Myoelectric control of prosthetic hands: state-of-the-art review
Su et al. Depth vision guided hand gesture recognition using electromyographic signals
CN113423341B (en) Method and apparatus for automatic calibration of wearable electrode sensor systems
Zhou et al. A novel muscle-computer interface for hand gesture recognition using depth vision
Chowdhury et al. Muscle computer interface: A review
Fahim et al. A visual analytic in deep learning approach to eye movement for human-machine interaction based on inertia measurement
Hasan Rukaiya Khatun Moury, Nazimul Haque. Coordination between Visualization and Execution of Movements
Xue et al. Human in-hand motion recognition based on multi-modal perception information fusion
Li et al. EMG-based HCI using CNN-LSTM neural network for dynamic hand gestures recognition
Noh et al. A decade of progress in human motion recognition: A comprehensive survey from 2010 to 2020
Sadarangani et al. A preliminary investigation on the utility of temporal features of Force Myography in the two-class problem of grasp vs. no-grasp in the presence of upper-extremity movements
Barfi et al. Improving robotic hand control via adaptive Fuzzy-PI controller using classification of EMG signals
Shin et al. EMG and IMU based real-time HCI using dynamic hand gestures for a multiple-DoF robot arm
Li et al. Touch gesture recognition using spatiotemporal fusion features
Jo et al. Real-time hand gesture classification using crnn with scale average wavelet transform
Xiong et al. A novel HCI based on EMG and IMU
Naser et al. sEMG-Based hand gestures classification using a semi-supervised multi-layer neural networks with Autoencoder
Ge et al. Gesture recognition and master–slave control of a manipulator based on sEMG and convolutional neural network–gated recurrent unit
Prasad et al. A wireless dynamic gesture user interface for HCI using hand data glove
Zhou et al. Deep learning based gesture recognition and its application in interactive control of intelligent wheelchair
Trifonov et al. Human–Machine Interface of Rehabilitation Exoskeletons with Redundant Electromyographic Channels
Yan et al. EEG-based recognition of hand movement and its parameter
James et al. Realtime hand landmark tracking to aid development of a prosthetic arm for reach and grasp motions