Deprecated: The each() function is deprecated. This message will be suppressed on further calls in /home/zhenxiangba/zhenxiangba.com/public_html/phproxy-improved-master/index.php on line 456
Avilés-Carrillo et al., 2026 - Google Patents
[go: Go Back, main page]

Avilés-Carrillo et al., 2026 - Google Patents

A biomechanical hand model to quantify finger joint kinematics using a 3d motion capture system

Avilés-Carrillo et al., 2026

View PDF
Document ID
3390432487977752310
Author
Avilés-Carrillo V
Molinari R
De Villa G
Elias L
Publication year
Publication venue
BioRxiv

External Links

Snippet

The kinematics of rhythmic, speed-modulated finger and grasp-like movements were analyzed using a reduced biomechanical model of the hand and a marker-based optical motion-capture system. Twenty-one healthy participants performed eight hand motor tasks …
Continue reading at www.biorxiv.org (PDF) (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
    • G06F3/041Digitisers, e.g. for touch screens or touch pads, characterized by the transducing means
    • 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
    • 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
    • G06F19/3437Medical simulation or modelling, e.g. simulating the evolution of medical disorders
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/103Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1121Determining geometric values, e.g. centre of rotation or angular range of movement
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/04Detecting, measuring or recording bioelectric signals of the body of parts thereof
    • A61B5/0402Electrocardiography, i.e. ECG
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235Details of waveform analysis

Similar Documents

Publication Publication Date Title
Pizzolato et al. Comparison of six electromyography acquisition setups on hand movement classification tasks
Caramiaux et al. Understanding gesture expressivity through muscle sensing
Hwang et al. Real-time robustness evaluation of regression based myoelectric control against arm position change and donning/doffing
Salter et al. emg2pose: A large and diverse benchmark for surface electromyographic hand pose estimation
Averta et al. Unvealing the principal modes of human upper limb movements through functional analysis
Chowdhury et al. Muscle computer interface: A review
US11596339B2 (en) Determining intended user movement to control an external device
Buongiorno et al. A survey on deep learning in electromyographic signal analysis
Nowak et al. The LET procedure for prosthetic myocontrol: towards multi-DOF control using single-DOF activations
Bailly et al. Real-time and dynamically consistent estimation of muscle forces using a moving horizon emg-marker tracking algorithm—application to upper limb biomechanics
Wang et al. Ultrasonography and electromyography based hand motion intention recognition for a trans-radial amputee: A case study
Yang et al. Emgbench: Benchmarking out-of-distribution generalization and adaptation for electromyography
Karrenbach et al. Deep learning and session-specific rapid recalibration for dynamic hand gesture recognition from EMG
Sîmpetru et al. Sensing the full dynamics of the human hand with a neural interface and deep learning
Gordienko et al. Effect of natural and synthetic noise data augmentation on physical action classification by brain–computer interface and deep learning
He Ultrasound-based human machine interfaces for hand gesture recognition: a scoping review and future direction
Zhu et al. Application and improvement of MFCC in gesture recognition with surface electromyography
Avilés-Carrillo et al. A biomechanical hand model to quantify finger joint kinematics using a 3d motion capture system
Pallarès-López et al. A multimodal biomechanics dataset with synchronized kinematics and internal tissue motions during reaching
Bao Design and Implementation of Gesture Music Composition System based on RealSense and Recurrent Neural Network.
Wilson et al. Gesture recognition through classification of acoustic muscle sensing for prosthetic control
Wang et al. MicroXercise: A Micro-Level Comparative and Explainable System for Remote Physical Therapy
Andreas et al. A database of multimodal myography and hand kinematics during realistic daily life activities
Allouch et al. Proposition, identification, and experimental evaluation of an inverse dynamic neuromusculoskeletal model for the human finger
Chihi et al. Modeling simple and complex handwriting based on EMG signals