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Bhatt P, Sethi A, Tasgaonkar V, Shroff J, Pendharkar I, Desai A, Sinha P, Deshpande A, Joshi G, Rahate A, Jain P, Walambe R, Kotecha K, Jain NK. Machine learning for cognitive behavioral analysis: datasets, methods, paradigms, and research directions. Brain Inform 2023; 10:18. [PMID: 37524933 PMCID: PMC10390406 DOI: 10.1186/s40708-023-00196-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 06/06/2023] [Indexed: 08/02/2023] Open
Abstract
Human behaviour reflects cognitive abilities. Human cognition is fundamentally linked to the different experiences or characteristics of consciousness/emotions, such as joy, grief, anger, etc., which assists in effective communication with others. Detection and differentiation between thoughts, feelings, and behaviours are paramount in learning to control our emotions and respond more effectively in stressful circumstances. The ability to perceive, analyse, process, interpret, remember, and retrieve information while making judgments to respond correctly is referred to as Cognitive Behavior. After making a significant mark in emotion analysis, deception detection is one of the key areas to connect human behaviour, mainly in the forensic domain. Detection of lies, deception, malicious intent, abnormal behaviour, emotions, stress, etc., have significant roles in advanced stages of behavioral science. Artificial Intelligence and Machine learning (AI/ML) has helped a great deal in pattern recognition, data extraction and analysis, and interpretations. The goal of using AI and ML in behavioral sciences is to infer human behaviour, mainly for mental health or forensic investigations. The presented work provides an extensive review of the research on cognitive behaviour analysis. A parametric study is presented based on different physical characteristics, emotional behaviours, data collection sensing mechanisms, unimodal and multimodal datasets, modelling AI/ML methods, challenges, and future research directions.
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Affiliation(s)
- Priya Bhatt
- Symbiosis Institute of Technology, Symbiosis International Deemed University, Pune, India
| | - Amanrose Sethi
- Symbiosis Institute of Technology, Symbiosis International Deemed University, Pune, India
| | - Vaibhav Tasgaonkar
- Symbiosis Institute of Technology, Symbiosis International Deemed University, Pune, India
| | - Jugal Shroff
- Symbiosis Institute of Technology, Symbiosis International Deemed University, Pune, India
| | - Isha Pendharkar
- Symbiosis Institute of Technology, Symbiosis International Deemed University, Pune, India
| | - Aditya Desai
- Symbiosis Institute of Technology, Symbiosis International Deemed University, Pune, India
| | - Pratyush Sinha
- Symbiosis Institute of Technology, Symbiosis International Deemed University, Pune, India
| | - Aditya Deshpande
- Symbiosis Institute of Technology, Symbiosis International Deemed University, Pune, India
| | - Gargi Joshi
- Symbiosis Institute of Technology, Symbiosis International Deemed University, Pune, India
| | - Anil Rahate
- Symbiosis Institute of Technology, Symbiosis International Deemed University, Pune, India
| | - Priyanka Jain
- Centre for Development of Advanced Computing (C-DAC), Delhi, India
| | - Rahee Walambe
- Symbiosis Institute of Technology, Symbiosis International Deemed University, Pune, India.
- Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International Deemed University, Pune, India.
| | - Ketan Kotecha
- Symbiosis Institute of Technology, Symbiosis International Deemed University, Pune, India.
- Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International Deemed University, Pune, India.
- UCSI University, Kuala Lumpur, Malaysia.
| | - N K Jain
- Centre for Development of Advanced Computing (C-DAC), Delhi, India
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