Kumar et al., 2024 - Google Patents
Decoding stress with computer vision-based approach using audio signals for psychological event identification during COVID-19Kumar et al., 2024
View PDF- Document ID
- 7472090107561042760
- Author
- Kumar A
- Godse S
- Kolekar S
- Saini D
- Pandita D
- Tiwari P
- Publication year
- Publication venue
- Journal of Electrical Systems
External Links
Snippet
Interpreting psychological events can be costly and quite complex. It is simple to translate such experiences into a person's spoken and nonverbal cues. The suggested model investigates a computer vision-based method for using an individual's audio signal to …
- 230000005236 sound signal 0 title abstract description 13
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
- 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
- G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
-
- 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
-
- 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
- G06K9/62—Methods or arrangements for recognition using electronic means
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L17/00—Speaker identification or verification
- G10L17/26—Recognition of special voice characteristics, e.g. for use in lie detectors; Recognition of animal voices
-
- 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
- G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
- G06Q10/063—Operations research or analysis
- G06Q10/0639—Performance analysis
- G06Q10/06398—Performance of employee with respect to a job function
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00
- G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00 specially adapted for particular use
- G10L25/51—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00 specially adapted for particular use for comparison or discrimination
- G10L25/66—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00 specially adapted for particular use for comparison or discrimination for extracting parameters related to health condition
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/08—Speech classification or search
-
- 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
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zhu et al. | A review of key technologies for emotion analysis using multimodal information | |
| US12230369B2 (en) | Systems and methods for mental health assessment | |
| US11120895B2 (en) | Systems and methods for mental health assessment | |
| Thati et al. | A novel multi-modal depression detection approach based on mobile crowd sensing and task-based mechanisms | |
| Victor et al. | Detecting depression using a framework combining deep multimodal neural networks with a purpose-built automated evaluation. | |
| López-de-Ipiña et al. | Feature selection for spontaneous speech analysis to aid in Alzheimer's disease diagnosis: A fractal dimension approach | |
| JP2022553749A (en) | Acoustic and Natural Language Processing Models for Velocity-Based Screening and Behavioral Health Monitoring | |
| Cummins et al. | Artificial intelligence to aid the detection of mood disorders | |
| Feng | Toward knowledge-driven speech-based models of depression: Leveraging spectrotemporal variations in speech vowels | |
| Kumar et al. | Identification of psychological stress from speech signal using deep learning algorithm | |
| Hasan et al. | Empathy detection from text, audiovisual, audio or physiological signals: A systematic review of task formulations and machine learning methods | |
| Flores et al. | Depression screening using deep learning on follow-up questions in clinical interviews | |
| Du et al. | Game: Generalized deep learning model towards multimodal data integration for early screening of adolescent mental disorders | |
| Gaikwad et al. | Speech recognition-based prediction for mental health and depression: a review | |
| Sandulescu et al. | Mobile app for stress monitoring using voice features | |
| Kumar et al. | Decoding stress with computer vision-based approach using audio signals for psychological event identification during COVID-19 | |
| Farah et al. | Mdd: A unified multimodal deep learning approach for depression diagnosis based on text and audio speech | |
| Tippannavar et al. | Advances and Challenges in Human Emotion Recognition Systems: A Comprehensive Review | |
| Sharma et al. | Machine and deep learning approaches for heart disease risk assessment | |
| Kugapriya et al. | UNWIND–a mobile application that provides emotional support for working women | |
| Santhiya et al. | Advancing Stress Detection through Deep Learning in Human-Machine Interactions with Speech Signals Analysis | |
| Lu et al. | Multimodal Classroom Climate Recognition: Dataset, Method, Application | |
| Wijaya et al. | Stress detection through wearable sensors: a convolutional neural network-based approach using heart rate and step data. | |
| Nimisha et al. | Real Time Speech Emotion Recognition Using LSTM and Raspberry Pi | |
| Krishna et al. | Tackling Depression Detection With Deep Learning: A Hybrid Model |