[go: up one dir, main page]

Mishra et al., 2019 - Google Patents

Human activity recognition using deep neural network

Mishra et al., 2019

Document ID
11266358258920577633
Author
Mishra P
Dey S
Ghosh S
Seal D
Goswami S
Publication year
Publication venue
2019 International conference on data science and engineering (ICDSE)

External Links

Snippet

The smartphone has become quite ubiquitous and an indispensable part of our lives in the modern day. It has many sensors which capture several minute details pertaining to our activities. So, it is but inevitable that human desire creeps in to augment and improve one's …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • 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
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G06K9/627—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches based on distances between the pattern to be recognised and training or reference patterns
    • 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
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
    • 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
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/04—Architectures, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • 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
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6279—Classification techniques relating to the number of classes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/08—Learning methods
    • G06N3/082—Learning methods modifying the architecture, e.g. adding or deleting nodes or connections, pruning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/02—Knowledge representation
    • G06N5/022—Knowledge engineering, knowledge acquisition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/04—Inference methods or devices
    • 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
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F15/00—Digital computers in general; Data processing equipment in general
    • G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines

Similar Documents

Publication Publication Date Title
Sarkar et al. Human activity recognition from sensor data using spatial attention-aided CNN with genetic algorithm
Sansano et al. A study of deep neural networks for human activity recognition
Nilashi et al. Predicting parkinson’s disease progression: Evaluation of ensemble methods in machine learning
Hassanpour et al. A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals
Zeng et al. Convolutional neural networks for human activity recognition using mobile sensors
Kanjo et al. Deep learning analysis of mobile physiological, environmental and location sensor data for emotion detection
Alawneh et al. Enhancing human activity recognition using deep learning and time series augmented data
Kaya et al. Human activity recognition from multiple sensors data using deep CNNs
Zhu et al. Feature extraction for robust physical activity recognition
Farsi Application of ensemble RNN deep neural network to the fall detection through IoT environment
Sadiq et al. Exploiting feature selection and neural network techniques for identification of focal and nonfocal EEG signals in TQWT domain
Chetty et al. Body sensor networks for human activity recognition
Nguyen et al. Wearable sensor data based human activity recognition using machine learning: a new approach
Mishra et al. Human activity recognition using deep neural network
Ruiz-Suarez et al. Hidden Markov and semi-Markov models when and why are these models useful for classifying states in time series data?
Geravesh et al. Artificial neural networks for human activity recognition using sensor based dataset
Chetty et al. Intelligent human activity recognition scheme for eHealth applications
Khatiwada et al. Automated human activity recognition by colliding bodies optimization-based optimal feature selection with recurrent neural network
Slim et al. IoT System Based on parameter optimization of Deep Learning using Genetic Algorithm.
Liang et al. An automatic network structure search via channel pruning for accelerating human activity inference on mobile devices
Bhandari et al. A new design of epileptic seizure detection using hybrid heuristic-based weighted feature selection and ensemble learning
Bianchi et al. Learning representations for multivariate time series with missing data using Temporal Kernelized Autoencoders
Haroon et al. An ensemble classification and binomial cumulative based PCA for diagnosis of Parkinson’s disease and autism spectrum disorder
Devarakonda et al. Particle swarm optimization of convolutional neural networks for human activity prediction
Sadouk et al. Convolutional neural networks for human activity recognition in time and frequency-domain