Nothing Special   »   [go: up one dir, main page]

Xu et al., 2018 - Google Patents

Elders' fall detection based on biomechanical features using depth camera

Xu et al., 2018

View PDF
Document ID
6797414219678421469
Author
Xu T
Zhou Y
Publication year
Publication venue
International journal of wavelets, multiresolution and information processing

External Links

Snippet

An accidental fall poses a serious threat to the health of the elderly. With the advances of technology, an increased number of surveillance systems have been installed in the elderly home to help medical staffs find the elderly at risk. Based on the study of human …
Continue reading at zhouyunlab.github.io (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
    • G06K9/00221Acquiring 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
    • G06K9/00362Recognising human body or animal bodies, e.g. vehicle occupant, pedestrian; Recognising body parts, e.g. hand
    • G06K9/00369Recognition of whole body, e.g. static pedestrian or occupant recognition
    • 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
    • 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
    • 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
    • A61B5/1116Determining posture transitions
    • 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
    • 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
    • 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
    • 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
    • A61B5/1112Global tracking of patients, e.g. by using GPS
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/20Analysis of motion

Similar Documents

Publication Publication Date Title
Xu et al. Elders’ fall detection based on biomechanical features using depth camera
Gabel et al. Full body gait analysis with Kinect
Li et al. Pre-impact fall detection based on a modified zero moment point criterion using data from Kinect sensors
Xiong et al. S3D-CNN: skeleton-based 3D consecutive-low-pooling neural network for fall detection
Lin et al. Fall detection system with artificial intelligence-based edge computing
Yao et al. A big bang–big crunch type-2 fuzzy logic system for machine-vision-based event detection and summarization in real-world ambient-assisted living
Wang et al. Swimming stroke phase segmentation based on wearable motion capture technique
Mastorakis et al. Fall detection without people: A simulation approach tackling video data scarcity
Reddy et al. Human activity recognition from kinect captured data using stick model
Ma et al. Human motion gesture recognition based on computer vision
Alazrai et al. Fall detection for elderly using anatomical-plane-based representation
Liu et al. Automatic fall risk detection based on imbalanced data
Ren et al. Multivariate analysis of joint motion data by Kinect: application to Parkinson’s disease
Zhen et al. Hybrid Deep‐Learning Framework Based on Gaussian Fusion of Multiple Spatiotemporal Networks for Walking Gait Phase Recognition
Alrazzak et al. A survey on human activity recognition using accelerometer sensor
Yuan et al. Adaptive recognition of motion posture in sports video based on evolution equation
Cheng et al. Periodic physical activity information segmentation, counting and recognition from video
Kapoor et al. Light-weight seated posture guidance system with machine learning and computer vision
Tang et al. Synthetic IMU datasets and protocols can simplify fall detection experiments and optimize sensor configuration
Dindo et al. Hankelet-based action classification for motor intention recognition
Oumaima et al. Vision-based fall detection and prevention for the elderly people: A review & ongoing research
Qian et al. Combining deep learning and model-based method using Bayesian Inference for walking speed estimation
Nouisser et al. Deep learning and kinect skeleton-based approach for fall prediction of elderly physically disabled
Chen et al. A Novel CNN-BiLSTM Ensemble Model With Attention Mechanism for Sit-to-Stand Phase Identification Using Wearable Inertial Sensors
Mastorakis Human fall detection methodologies: from machine learning using acted data to fall modelling using myoskeletal simulation