[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Ardestani et al., 2014 - Google Patents

A neural network approach for determining gait modifications to reduce the contact force in knee joint implant

Ardestani et al., 2014

View PDF
Document ID
14034835734749478782
Author
Ardestani M
Chen Z
Wang L
Lian Q
Liu Y
He J
Li D
Jin Z
Publication year
Publication venue
Medical engineering & physics

External Links

Snippet

There is a growing interest in non-surgical gait rehabilitation treatments to reduce the loading in the knee joint. In particular, synergetic kinematic changes required for joint offloading should be determined individually for each subject. Previous studies for gait …
Continue reading at eprints.whiterose.ac.uk (PDF) (other versions)

Classifications

    • 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
    • G06F19/3437—Medical simulation or modelling, e.g. simulating the evolution of medical disorders
    • 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
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1121—Determining geometric values, e.g. centre of rotation or angular range of movement
    • 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
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1116—Determining posture transitions
    • 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
    • A61B5/45—For evaluating or diagnosing the musculoskeletal system or teeth
    • A61B5/4528—Joints
    • 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
    • G06F19/345—Medical expert systems, neural networks or other automated diagnosis
    • 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
    • G06F19/3481—Computer-assisted prescription or delivery of treatment by physical action, e.g. surgery or physical exercise
    • 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
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/1036—Measuring load distribution, e.g. podologic studies
    • A61B5/1038—Measuring plantar pressure during gait

Similar Documents

Publication Publication Date Title
Ardestani et al. Feed forward artificial neural network to predict contact force at medial knee joint: Application to gait modification
Ardestani et al. Human lower extremity joint moment prediction: A wavelet neural network approach
Saxby et al. Tibiofemoral contact forces during walking, running and sidestepping
Falisse et al. Physics-based simulations to predict the differential effects of motor control and musculoskeletal deficits on gait dysfunction in cerebral palsy: a retrospective case study
Pizzolato et al. CEINMS: A toolbox to investigate the influence of different neural control solutions on the prediction of muscle excitation and joint moments during dynamic motor tasks
Fregly et al. Grand challenge competition to predict in vivo knee loads
Shao et al. An EMG-driven model to estimate muscle forces and joint moments in stroke patients
Burton II et al. Machine learning for rapid estimation of lower extremity muscle and joint loading during activities of daily living
Nieuwenhuys et al. Literature review and comparison of two statistical methods to evaluate the effect of botulinum toxin treatment on gait in children with cerebral palsy
Ardestani et al. A neural network approach for determining gait modifications to reduce the contact force in knee joint implant
Ardestani et al. Gait modification and optimization using neural network–genetic algorithm approach: application to knee rehabilitation
Ardestani et al. A real-time topography of maximum contact pressure distribution at medial tibiofemoral knee implant during gait: application to knee rehabilitation
Ardestani et al. Sensitivity analysis of human lower extremity joint moments due to changes in joint kinematics
Kainz et al. ESB Clinical Biomechanics Award 2020: Pelvis and hip movement strategies discriminate typical and pathological femoral growth–Insights gained from a multi-scale mechanobiological modelling framework
Ogaya et al. Muscle contributions to knee extension in the early stance phase in patients with knee osteoarthritis
Morrison et al. Joint range of motion entropy changes in response to load carriage in military personnel
Willson et al. Full body musculoskeletal model for simulations of gait in persons with transtibial amputation
Jang et al. Acute vibration feedback during gait reduces mechanical ankle joint loading in chronic ankle instability patients
Zych et al. Forward and backward walking share the same motor modules and locomotor adaptation strategies
Zou et al. Prediction on the medial knee contact force in patients with knee valgus using transfer learning approaches: Application to rehabilitation gaits
Perrone et al. Hip joint contact forces are lower in people with femoroacetabular impingement syndrome during squat tasks
Son et al. A hybrid static optimisation method to estimate muscle forces during muscle co-activation
Ravera et al. Individual muscle force–energy rate is altered during crouch gait: A neuro-musculoskeletal evaluation
Cornish et al. Hip contact forces can be predicted with a neural network using only synthesised key points and electromyography in people with hip osteoarthritis
Kumar et al. A comprehensive analysis of lower extremity based gait cycle disorders and muscle analysis