[go: up one dir, main page]

Alam et al., 2014 - Google Patents

GeSmart: A gestural activity recognition model for predicting behavioral health

Alam et al., 2014

View PDF
Document ID
5975293026230485261
Author
Alam M
Roy N
Publication year
Publication venue
2014 International Conference on Smart Computing

External Links

Snippet

To promote independent living for elderly population activity recognition based approaches have been investigated deeply to infer the activities of daily living (ADLs) and instrumental activities of daily living (I-ADLs). Deriving and integrating the gestural activities (such as …
Continue reading at www.researchgate.net (PDF) (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
    • G06K9/00335—Recognising movements or behaviour, e.g. recognition of gestures, dynamic facial expressions; Lip-reading
    • 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
    • G06F3/015—Input arrangements based on nervous system activity detection, e.g. brain waves [EEG] detection, electromyograms [EMG] detection, electrodermal response detection
    • 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
    • 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

Similar Documents

Publication Publication Date Title
US10706329B2 (en) Methods for explainability of deep-learning models
US11009952B2 (en) Interface for electroencephalogram for computer control
Min et al. Exploring audio and kinetic sensing on earable devices
Zhou et al. Tackling mental health by integrating unobtrusive multimodal sensing
Hashmi et al. Motion reveal emotions: Identifying emotions from human walk using chest mounted smartphone
Myroniv et al. Analyzing user emotions via physiology signals
US10952680B2 (en) Electroencephalogram bioamplifier
CN113764099A (en) Psychological state analysis method, device, equipment and medium based on artificial intelligence
Alam et al. A smart segmentation technique towards improved infrequent non-speech gestural activity recognition model
Bahador et al. Deep learning–based multimodal data fusion: Case study in food intake episodes detection using wearable sensors
EP4058967A1 (en) System and method for collecting behavioural data to assist interpersonal interaction
Gu et al. Wearable social sensing: Content-based processing methodology and implementation
Alam et al. GeSmart: A gestural activity recognition model for predicting behavioral health
US20220409134A1 (en) Using an In-Ear Microphone Within an Earphone as a Fitness and Health Tracker
Hasan et al. Human activity recognition using smartphone sensors with context filtering
Turaev et al. Review and analysis of patients’ body language from an artificial intelligence perspective
Kumar et al. Neuro-phone: An assistive framework to operate Smartphone using EEG signals
Aggelides et al. A gesture recognition approach to classifying allergic rhinitis gestures using wrist-worn devices: a multidisciplinary case study
Vi et al. Efficient real-time devices based on accelerometer using machine learning for har on low-performance microcontrollers
Setiawan et al. Fine-grained emotion recognition: fusion of physiological signals and facial expressions on spontaneous emotion corpus
Qu et al. Indoor multiperson detection and recognition through footsteps: A deep learning approach with acoustic signal analysis
Hossain et al. Sleep well: a sound sleep monitoring framework for community scaling
Yang et al. Wearable structured mental-sensing-graph measurement
Bi et al. Measuring children’s eating behavior with a wearable device
Giannakopoulos et al. Daily activity recognition based on meta-classification of low-level audio events