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

Hung et al., 2014 - Google Patents

Activity recognition with sensors on mobile devices

Hung et al., 2014

Document ID
15461629076815997187
Author
Hung W
Shen F
Wu Y
Hor M
Tang C
Publication year
Publication venue
2014 International Conference on Machine Learning and Cybernetics

External Links

Snippet

Recently, Activity Recognition (AR) has become a popular research topic and gained attention in the study field because of the increasing availability of sensors in consumer products, such as GPS sensors, vision sensors, audio sensors, light sensors, temperature …
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
    • G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
    • G06K9/00268—Feature extraction; Face representation
    • G06K9/00281—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
    • 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
    • G06K9/00369—Recognition of whole body, e.g. static pedestrian or occupant recognition
    • 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
    • G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
    • G06K9/00288—Classification, e.g. identification
    • 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
    • G06K9/00335—Recognising movements or behaviour, e.g. recognition of gestures, dynamic facial expressions; Lip-reading
    • 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
    • G06F3/00—Input 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
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F3/00—Input 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
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/017—Gesture based interaction, e.g. based on a set of recognized hand gestures
    • 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
    • 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
    • G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • G06K9/00771—Recognising scenes under surveillance, e.g. with Markovian modelling of scene activity

Similar Documents

Publication Publication Date Title
Hung et al. Activity recognition with sensors on mobile devices
Yen et al. Human daily activity recognition performed using wearable inertial sensors combined with deep learning algorithms
Shakya et al. Comparative study of machine learning and deep learning architecture for human activity recognition using accelerometer data
Gupta et al. A survey on human activity recognition and classification
Wang et al. Detecting user activities with the accelerometer on android smartphones
Nahar et al. A machine learning based fall detection for elderly people with neurodegenerative disorders
Morales et al. Human activity recognition by smartphones regardless of device orientation
Büber et al. Discriminative time-domain features for activity recognition on a mobile phone
Yuan et al. Smartphone-based activity recognition using hybrid classifier
Birku et al. Survey on fall detection systems
Minh et al. Evaluation of smartphone and smartwatch accelerometer data in activity classification
Hong et al. A low-cost real-time IoT human activity recognition system based on wearable sensor and the supervised learning algorithms
Harasimowicz et al. Accelerometer-based human activity recognition and the impact of the sample size
Mekruksavanich et al. Wearable fall detection based on motion signals using hybrid deep residual neural network
Samiei-Zonouz et al. Smartphone-centric human posture monitoring system
Khandnor et al. A survey of activity recognition process using inertial sensors and smartphone sensors
Ghobadi et al. Foot-mounted inertial measurement unit for activity classification
Alvee et al. Application of machine learning classifiers for predicting human activity
Samà et al. Gait identification by means of box approximation geometry of reconstructed attractors in latent space
Prasertsung et al. A classification of accelerometer data to differentiate pedestrian state
Eskaf et al. Aggregated activity recognition using smart devices
Awan et al. Human activity recognition in WSN: a comparative study
Miyamoto et al. Human activity recognition system including smartphone position
Dehkordi et al. Optimal feature set for smartphone-based activity recognition
Jitpattanakul Wearable fall detection based on motion signals using hybrid deep residual neural network