Sağbaş et al., 2020 - Google Patents
Stress detection via keyboard typing behaviors by using smartphone sensors and machine learning techniquesSağbaş 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 …
- 238000001514 detection method 0 title abstract description 36
Classifications
-
- 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
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- 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
- G06F17/20—Handling natural language data
- G06F17/27—Automatic analysis, e.g. parsing
-
- 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
- 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
- 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
- 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
- G06Q—DATA 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
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation, e.g. computer aided management of electronic mail or groupware; Time management, e.g. calendars, reminders, meetings or time accounting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
-
- 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 |
---|---|---|
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 |