Miao et al., 2024 - Google Patents

Applying hybrid deep learning models to assess upper limb rehabilitation

Miao et al., 2024

View PDF
Document ID
4211218097752714929
Author
Miao S
Liu Z
Wang D
Shen X
Shen N
Publication year
Publication venue
IEEE Access

External Links

Snippet

Upper limb rehabilitation training is an effective method to restore and improve the upper limb motor function of stroke patients, which enables them to engage in daily life activities independently. However, traditional rehabilitation training methods have limitations such as …
Continue reading at ieeexplore.ieee.org (PDF) (other versions)

Classifications

    • 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
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
    • 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
    • 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
    • G06K9/00221Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
    • G06K9/00268Feature extraction; Face representation
    • G06K9/00281Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
    • 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
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30196Human being; Person
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/20Analysis of motion
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10024Color image
    • 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
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • 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
    • G06K9/00362Recognising human body or animal bodies, e.g. vehicle occupant, pedestrian; Recognising body parts, e.g. hand
    • 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

Similar Documents

Publication Publication Date Title
Liao et al. A deep learning framework for assessing physical rehabilitation exercises
Ashraf et al. A novel telerehabilitation system for physical exercise monitoring in elderly healthcare
Ma et al. Human motion gesture recognition based on computer vision
Miao et al. Applying hybrid deep learning models to assess upper limb rehabilitation
Sheu et al. Improvement of human pose estimation and processing with the intensive feature consistency network
Fu et al. Human pose estimation and action recognition for fitness movements
Cao et al. A novel combination model of convolutional neural network and long short-term memory network for upper limb evaluation using kinect-based system
Saini ismartyog: A real time yoga pose recognition and correction feedback model using deep learning for smart healthcare
Ren et al. Multivariate analysis of joint motion data by Kinect: application to Parkinson’s disease
Solongontuya et al. Novel side pose classification model of stretching gestures using three-layer LSTM
Dutta et al. A hand gesture-operated system for rehabilitation using an end-to-end detection framework
McColl et al. Human body pose interpretation and classification for social human-robot interaction
Hu et al. Effective evaluation of HGcnMLP method for markerless 3D pose estimation of musculoskeletal diseases patients based on smartphone monocular video
Shu et al. The research and implementation of human posture recognition algorithm via OpenPose
Abdelrazik et al. Efficient deep learning algorithm for egyptian sign language recognition
Zhu et al. Walking representation and simulation based on multi-source image fusion and multi-agent reinforcement learning for gait rehabilitation
Hou et al. Interpretable two-stage action quality assessment via 3D human pose estimation and dynamic feature alignment
Jaleel et al. Body motion detection and tracking using a Kinect sensor
Ye et al. Low-cost Geometry-based Eye Gaze Detection using Facial Landmarks Generated through Deep Learning
Klein et al. Assessing the reliability of AI-based angle detection for shoulder and elbow rehabilitation
Zeng et al. Research on the integration of computer human-computer interaction technology and multimedia medical big data interaction system
Gao et al. Healthcare System from Multisensor Collaboration and Human Action Recognition.
Zhang et al. Robust activity recognition based on human skeleton for video surveillance
Hundall et al. Computer vision and abnormal patient gait assessment a comparison of machine learning models
Hu et al. Acrnet: Attention cube regression network for multi-view real-time 3d human pose estimation in telemedicine