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

Gumaei et al., 2019 - Google Patents

A hybrid deep learning model for human activity recognition using multimodal body sensing data

Gumaei et al., 2019

View PDF
Document ID
16522241317512271314
Author
Gumaei A
Hassan M
Alelaiwi A
Alsalman H
Publication year
Publication venue
IEEE Access

External Links

Snippet

Human activity recognition from multimodal body sensor data has proven to be an effective approach for the care of elderly or physically impaired people in a smart healthcare environment. However, traditional machine learning techniques are mostly focused on a …
Continue reading at ieeexplore.ieee.org (PDF) (other versions)

Classifications

    • 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
    • G06F19/345Medical expert systems, neural networks or other automated diagnosis
    • 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
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6268Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • 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
    • G06N3/06Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • G06N3/0635Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means using analogue means
    • 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
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • 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
    • 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
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • 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

Similar Documents

Publication Publication Date Title
Gumaei et al. A hybrid deep learning model for human activity recognition using multimodal body sensing data
Ihianle et al. A deep learning approach for human activities recognition from multimodal sensing devices
Uddin et al. A body sensor data fusion and deep recurrent neural network-based behavior recognition approach for robust healthcare
Choi et al. EmbraceNet: A robust deep learning architecture for multimodal classification
Nguyen et al. Trends in human activity recognition with focus on machine learning and power requirements
Sansano et al. A study of deep neural networks for human activity recognition
Barut et al. Multitask LSTM model for human activity recognition and intensity estimation using wearable sensor data
Mekruksavanich et al. Sport-Related Activity Recognition from Wearable Sensors Using Bidirectional GRU Network.
Praveen et al. Cross attentional audio-visual fusion for dimensional emotion recognition
Abdel-Salam et al. Human activity recognition using wearable sensors: review, challenges, evaluation benchmark
Kumar et al. Human activity recognition (har) using deep learning: Review, methodologies, progress and future research directions
Mekruksavanich et al. Hybrid convolution neural network with channel attention mechanism for sensor-based human activity recognition
García et al. Towards effective detection of elderly falls with CNN-LSTM neural networks
San et al. Deep learning for human activity recognition
Guerra et al. Automatic pose recognition for monitoring dangerous situations in Ambient-Assisted Living
Zheng et al. Meta-learning meets the Internet of Things: Graph prototypical models for sensor-based human activity recognition
Wu et al. Robust fall detection in video surveillance based on weakly supervised learning
Gil-Martín et al. Time analysis in human activity recognition
Liu et al. Deep-learning-based signal enhancement of low-resolution accelerometer for fall detection systems
Zhao et al. Attention‐based sensor fusion for emotion recognition from human motion by combining convolutional neural network and weighted kernel support vector machine and using inertial measurement unit signals
Hristov Real-time abnormal human activity detection using 1DCNN-LSTM for 3D skeleton data
Mogan et al. Advances in vision-based gait recognition: From handcrafted to deep learning
ALISAWI et al. Real-Time Emotion Recognition Using Deep Learning Methods: Systematic Review
Zhu et al. Human activity recognition based on a modified capsule network
Xu et al. An enhanced human activity recognition algorithm with positional attention