Miao et al., 2024 - Google Patents
Applying hybrid deep learning models to assess upper limb rehabilitationMiao 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 …
- 210000001364 upper extremity 0 title abstract description 53
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6247—Extracting 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/34—Computer-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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F3/00—Input 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
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
- G06K9/00268—Feature extraction; Face representation
- G06K9/00281—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00335—Recognising movements or behaviour, e.g. recognition of gestures, dynamic facial expressions; Lip-reading
- G06K9/00355—Recognition of hand or arm movements, e.g. recognition of deaf sign language
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30196—Human being; Person
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00362—Recognising human body or animal bodies, e.g. vehicle occupant, pedestrian; Recognising body parts, e.g. hand
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer 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 |