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

Sağbaş et al., 2020 - Google Patents

Stress detection via keyboard typing behaviors by using smartphone sensors and machine learning techniques

Sağbaş et al., 2020

View PDF
Document ID
9105714779262397618
Author
Sağbaş E
Korukoglu S
Balli S
Publication year
Publication venue
Journal of medical systems

External Links

Snippet

Stress is one of the biggest problems in modern society. It may not be possible for people to perceive if they are under high stress or not. It is important to detect stress early and unobtrusively. In this context, stress detection can be considered as a classification problem …
Continue reading at acikerisim.mu.edu.tr (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
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/02Knowledge representation
    • G06N5/022Knowledge engineering, knowledge acquisition
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/20Handling natural language data
    • G06F17/27Automatic analysis, e.g. parsing
    • 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
    • G06F3/015Input arrangements based on nervous system activity detection, e.g. brain waves [EEG] detection, electromyograms [EMG] detection, electrodermal response detection
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/04Inference methods or devices
    • 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
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/10Office automation, e.g. computer aided management of electronic mail or groupware; Time management, e.g. calendars, reminders, meetings or time accounting
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00Computer systems based on specific mathematical models
    • G06N7/005Probabilistic networks
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F15/00Digital computers in general; Data processing equipment in general
    • G06F15/18Digital 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
Sağbaş et al. Stress detection via keyboard typing behaviors by using smartphone sensors and machine learning techniques
Lee et al. Towards unobtrusive emotion recognition for affective social communication
Carneiro et al. Multimodal behavioral analysis for non-invasive stress detection
Suni Lopez et al. Towards real-time automatic stress detection for office workplaces
Knowles et al. Uncertainty in current and future health wearables
Kim et al. Prediction for retrospection: Integrating algorithmic stress prediction into personal informatics systems for college students’ mental health
Soto et al. Observing and predicting knowledge worker stress, focus and awakeness in the wild
Gonçalves et al. Assessing users’ emotion at interaction time: a multimodal approach with multiple sensors
Zhang et al. Multi-modal interactive fusion method for detecting teenagers’ psychological stress
Vildjiounaite et al. Unsupervised stress detection algorithm and experiments with real life data
Sanchez et al. Towards job stress recognition based on behavior and physiological features
Kunc et al. Real-life validation of emotion detection system with wearables
Sağbaş et al. Real-time stress detection from smartphone sensor data using genetic algorithm-based feature subset optimization and k-nearest neighbor algorithm
Hadhri et al. A voting ensemble classifier for stress detection
Lim et al. Continuous stress monitoring under varied demands using unobtrusive devices
Cernian et al. Mood detector-on using machine learning to identify moods and emotions
Magdin et al. The possibilities of classification of emotional states based on user behavioral characteristics
Yang et al. Wearable Structured Mental-Sensing-Graph Measurement
Zhang et al. A survey on mobile affective computing
US20230088373A1 (en) Progressive individual assessments using collected inputs
Jacob et al. Affect sensing from smartphones through touch and motion contexts
Alibasa et al. Predicting mood from digital footprints using frequent sequential context patterns features
Carneiro et al. Context acquisition in auditory emotional recognition studies
Le-Quang et al. Wemotion: A system to detect emotion using wristbands and smartphones
Kächele et al. The influence of annotation, corpus design, and evaluation on the outcome of automatic classification of human emotions