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

Ren et al., 2023 - Google Patents

Gait phase recognition of multi-mode locomotion based on multi-layer perceptron for the plantar pressure measurement system

Ren et al., 2023

Document ID
9812460714044320930
Author
Ren B
Liu J
Guan W
Ren P
Publication year
Publication venue
International Journal of Intelligent Robotics and Applications

External Links

Snippet

The gait phase recognition has some broad application prospects, such as lower limb exoskeleton (LLE). To accurately identify the gait phase in different locomotion modes according to gait patterns and corresponding gait characteristics, we define sets of gait …
Continue reading at link.springer.com (other versions)

Classifications

    • 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
    • A61B5/112—Gait analysis
    • 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
    • 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
    • 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
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/6802—Sensor mounted on worn items
    • 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
    • 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

Similar Documents

Publication Publication Date Title
Sethi et al. A comprehensive survey on gait analysis: History, parameters, approaches, pose estimation, and future work
Wang et al. Integral real-time locomotion mode recognition based on GA-CNN for lower limb exoskeleton
Seethapathi et al. Movement science needs different pose tracking algorithms
Deng et al. Advances in automation technologies for lower extremity neurorehabilitation: A review and future challenges
Attal et al. Automatic recognition of gait phases using a multiple-regression hidden Markov model
CN109953761B (en) Lower limb rehabilitation robot movement intention reasoning method
Chen et al. A novel gait pattern recognition method based on LSTM-CNN for lower limb exoskeleton
Khodabandelou et al. A fuzzy convolutional attention-based GRU network for human activity recognition
Wu et al. Gait phase classification for a lower limb exoskeleton system based on a graph convolutional network model
Wang et al. sEMG-based consecutive estimation of human lower limb movement by using multi-branch neural network
Kang et al. Subject-independent continuous locomotion mode classification for robotic hip exoskeleton applications
Chen et al. Kinematic analysis of human gait based on wearable sensor system for gait rehabilitation
Zeng et al. Research on a gait detection system and recognition algorithm for lower limb exoskeleton robot
Kim et al. Kinematic-based locomotion mode recognition for power augmentation exoskeleton
Prado et al. Prediction of gait cycle percentage using instrumented shoes with artificial neural networks
Zhang et al. Gait pattern identification and phase estimation in continuous multilocomotion mode based on inertial measurement units
Peng et al. Human walking pattern recognition based on KPCA and SVM with ground reflex pressure signal
Siu et al. Ankle torque estimation during locomotion from surface electromyography and accelerometry
CN115644858B (en) Wearable intelligent gait analyzer based on multi-sensor data fusion technology
Ren et al. Gait phase recognition of multi-mode locomotion based on multi-layer perceptron for the plantar pressure measurement system
Wang et al. Human gait prediction for lower limb rehabilitation exoskeleton using gated recurrent units
Soliman et al. Gait phase estimation of unsupervised outdoors walking using IMUs and a linear regression model
Guo et al. A real-time stable-control gait switching strategy for lower-limb rehabilitation exoskeleton
Bauman et al. Gait phase detection in walking and stairs using machine learning
Na et al. Deep domain adaptation, pseudo-labeling, and shallow network for accurate and fast gait prediction of unlabeled datasets