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

Chowdhury, 2021 - Google Patents

Towards reducing labeling efforts in IoT-based machine learning systems: PhD forum abstract

Chowdhury, 2021

Document ID
4724394743632219689
Author
Chowdhury T
Publication year
Publication venue
Proceedings of the 20th International Conference on Information Processing in Sensor Networks (co-located with CPS-IoT Week 2021)

External Links

Snippet

The number of Internet-of-Things (IoT) and edge devices has exploded in recent years. Coupled with recent advances in learning methodologies, these can make the vision of smart building a reality and transform how people interact with their environment. Deep …
Continue reading at dl.acm.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
    • 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
    • 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
    • G06F3/03—Arrangements for converting the position or the displacement of a member into a coded form
    • 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
    • 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
    • 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
    • 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
    • G06K9/00362—Recognising human body or animal bodies, e.g. vehicle occupant, pedestrian; Recognising body parts, e.g. hand
    • 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
    • G06K9/00006—Acquiring or recognising fingerprints or palmprints
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis

Similar Documents

Publication Publication Date Title
Wang et al. Human activity recognition in a smart home environment with stacked denoising autoencoders
Jordao et al. Novel approaches to human activity recognition based on accelerometer data
US11429807B2 (en) Automated collection of machine learning training data
Becker et al. GestEar: combining audio and motion sensing for gesture recognition on smartwatches
Pei et al. MARS: Mixed virtual and real wearable sensors for human activity recognition with multidomain deep learning model
Pauwels et al. Sensor networks for ambient intelligence
EP4040320A1 (en) On-device activity recognition
Shinmoto Torres et al. Evaluation of wearable sensor tag data segmentation approaches for real time activity classification in elderly
US11989931B2 (en) Method and apparatus with object classification
CN113822350B (en) Method for identifying actions and estimating positions of equipment-free personnel based on multitask learning
Gjoreski et al. Head-ar: Human activity recognition with head-mounted imu using weighted ensemble learning
Sideridis et al. Gesturekeeper: Gesture recognition for controlling devices in iot environments
Khan et al. Gesthaar: An accelerometer-based gesture recognition method and its application in nui driven pervasive healthcare
Chowdhury Towards reducing labeling efforts in IoT-based machine learning systems: PhD forum abstract
Su et al. Multi-view fall detection based on spatio-temporal interest points
Chowdhury et al. Is general purpose sensing a pipe dream? a case study in ambient multi-sensing for human activity recognition
Sun et al. GeXSe (Generative Explanatory Sensor System): An Interpretable Deep Generative Model for Human Activity Recognition in Smart Spaces
Bagci Das et al. Human activity recognition based on multi‐instance learning
Bouchard et al. Regression analysis for gesture recognition using rfid technology
Zhou et al. Pre-classification based hidden Markov model for quick and accurate gesture recognition using a finger-worn device
Rubin Bose et al. Precise Recognition of Vision Based Multi-hand Signs Using Deep Single Stage Convolutional Neural Network
Li et al. Group walking recognition based on smartphone sensors
Budiarso et al. Optimizing LSTM with Grid Search and Regularization Techniques to Enhance Accuracy in Human Activity Recognition
Zhao et al. Feature Encoding by Location-Enhanced Word2Vec Embedding for Human Activity Recognition in Smart Homes
Sheishaa et al. A context-aware motion mode recognition system using embedded inertial sensors in portable smart devices