scholarly journals Affective computing in the context of music therapy: a systematic review

2021 ◽  
Vol 10 (15) ◽  
pp. e392101522844
Author(s):  
Maíra Araújo de Santana ◽  
Clarisse Lins de Lima ◽  
Arianne Sarmento Torcate ◽  
Flávio Secco Fonseca ◽  
Wellington Pinheiro dos Santos
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Music Therapy ◽  
Emotion Recognition ◽  
Affective Computing ◽  
Intelligent Computing ◽  
Important Approach ◽  
Review Protocol ◽  
Automatic Search ◽  
Recognition Of Emotions

Music therapy is an effective tool to slow down the progress of dementia since interaction with music may evoke emotions that stimulates brain areas responsible for memory. This therapy is most successful when therapists provide adequate and personalized stimuli for each patient. This personalization is often hard. Thus, Artificial Intelligence (AI) methods may help in this task. This paper brings a systematic review of the literature in the field of affective computing in the context of music therapy. We particularly aim to assess AI methods to perform automatic emotion recognition applied to Human-Machine Musical Interfaces (HMMI). To perform the review, we conducted an automatic search in five of the main scientific databases on the fields of intelligent computing, engineering, and medicine. We search all papers released from 2016 and 2020, whose metadata, title or abstract contains the terms defined in the search string. The systematic review protocol resulted in the inclusion of 144 works from the 290 publications returned from the search. Through this review of the state-of-the-art, it was possible to list the current challenges in the automatic recognition of emotions. It was also possible to realize the potential of automatic emotion recognition to build non-invasive assistive solutions based on human-machine musical interfaces, as well as the artificial intelligence techniques in use in emotion recognition from multimodality data. Thus, machine learning for recognition of emotions from different data sources can be an important approach to optimize the clinical goals to be achieved through music therapy.

scholarly journals Electrocardiogram-Based Emotion Recognition Systems and Their Applications in Healthcare—A Review

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5015
Author(s):  
Muhammad Anas Hasnul ◽  
Nor Azlina Ab. Ab.Aziz ◽  
Salem Alelyani ◽  
Mohamed Mohana ◽  
Azlan Abd. Abd. Aziz
Keyword(s):  
Artificial Intelligence ◽  
Emotion Recognition ◽  
Affective Computing ◽  
Review Paper ◽  
Recognition System ◽  
Healthcare Systems ◽  
Multimodal Approach ◽  
Health And Wellbeing ◽  
The Subject ◽  
Recognition Systems

Affective computing is a field of study that integrates human affects and emotions with artificial intelligence into systems or devices. A system or device with affective computing is beneficial for the mental health and wellbeing of individuals that are stressed, anguished, or depressed. Emotion recognition systems are an important technology that enables affective computing. Currently, there are a lot of ways to build an emotion recognition system using various techniques and algorithms. This review paper focuses on emotion recognition research that adopted electrocardiograms (ECGs) as a unimodal approach as well as part of a multimodal approach for emotion recognition systems. Critical observations of data collection, pre-processing, feature extraction, feature selection and dimensionality reduction, classification, and validation are conducted. This paper also highlights the architectures with accuracy of above 90%. The available ECG-inclusive affective databases are also reviewed, and a popularity analysis is presented. Additionally, the benefit of emotion recognition systems towards healthcare systems is also reviewed here. Based on the literature reviewed, a thorough discussion on the subject matter and future works is suggested and concluded. The findings presented here are beneficial for prospective researchers to look into the summary of previous works conducted in the field of ECG-based emotion recognition systems, and for identifying gaps in the area, as well as in developing and designing future applications of emotion recognition systems, especially in improving healthcare.

scholarly journals Artificial Intelligence Techniques for Distance Education: A Systematic Literature Review

TEM Journal ◽  
2021 ◽  
pp. 1621-1629
Author(s):  
Aayat Aljarrah ◽  
Mustafa Ababneh ◽  
Damla Karagozlu ◽  
Fezile Ozdamli
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Distance Education ◽  
Big Data ◽  
Distance Learning ◽  
Affective Computing ◽  
Learning System ◽  
Educational Systems ◽  
Artificial Intelligence Techniques ◽  
Educational Processes

In the current era, education, like other fields, relies heavily on big data. Moreover, artificial intelligence, including affective computing, is one of the most essential and popular technologies adopted by educational institutions to process and analyze big data. In this systematic review, many previous research types related to improving educational systems using artificial intelligence techniques were studied, such as: deep learning, machine learning, and affective computing. This systematic review aims to identify the gaps in students' emotional understanding in distance education systems. The world has recently witnessed the spread of educational processes for distance learning, especially in the university and the enormous open online courses (MOOCs). Besides, the COVID-19 pandemic has been involved in changing all educational processes to a distance learning system. The results indicated that these systems recorded a high success rate. However, the teacher does not fully understand the student’s emotional state during the educational session. It also lacks monitoring or monitoring during the electronic exams, which are electronic exams. So, it is a widespread problem in distance learning.

scholarly journals An Affective and Cognitive Toy to Support Mood Disorders

Informatics ◽  
2020 ◽  
Vol 7 (4) ◽  
pp. 48
Author(s):  
Esperanza Johnson ◽  
Iván González ◽  
Tania Mondéjar ◽  
Luis Cabañero-Gómez ◽  
Jesús Fontecha ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Emotion Recognition ◽  
Mood Disorders ◽  
Emotional Regulation ◽  
Affective Computing ◽  
Raspberry Pi ◽  
Future Work ◽  
Therapy Sessions

Affective computing is a branch of artificial intelligence that aims at processing and interpreting emotions. In this study, we implemented sensors/actuators into a stuffed toy mammoth, which allows the toy to have an affective and cognitive basis to its communication. The goal is for therapists to use this as a tool during their therapy sessions that work with patients with mood disorders. The toy detects emotion and provides a dialogue that would guide a session aimed at working with emotional regulation and perception. These technical capabilities are possible by employing IBM Watson’s services, implemented into a Raspberry Pi Zero. In this paper, we delve into its evaluation with neurotypical adolescents, a panel of experts, and other professionals. The evaluation aims were to perform a technical and application validation for use in therapy sessions. The results of the evaluations are generally positive, with an 87% accuracy for emotion recognition, and an average usability score of 77.5 for experts (n = 5), and 64.35 for professionals (n = 23). We add to that information some of the issues encountered, its effects on applicability, and future work to be done.

scholarly journals Emotion recognition techniques using physiological signals and video games –Systematic review–

2017 ◽  
Vol 26 (46) ◽  
Author(s):  
Mauro Callejas-Cuervo ◽  
Laura Alejandra Martínez-Tejada ◽  
Andrea Catherine Alarcón-Aldana
Keyword(s):  
Systematic Review ◽  
Video Games ◽  
Emotion Recognition ◽  
Emotional Reactions ◽  
Physiological Signals ◽  
Multimedia Tools ◽  
Physiological Signal ◽  
Important Approach ◽  
The Individual ◽  
Education Psychology

Emotion recognition systems from physiological signals are innovative techniques that allow studying the behavior and reaction of an individual when exposed to information that may evoke emotional reactions through multimedia tools, for example, video games. This type of approach is used to identify the behavior of an individual in different fields, such as medicine, education, psychology, etc., in order to assess the effect that the content has on the individual that is interacting with it. This article shows a systematic review of articles that report studies on emotion recognition with physiological signals and video games, between January 2010 and April 2016. We searched in eight databases, and found 15 articles that met the selection criteria. With this systematic review, we found that the use of video games as emotion stimulation tools has become an innovative field of study, due to their potential to involve stories and multimedia tools that can interact directly with the person in fields like rehabilitation. We detected clear examples where video games and physiological signal measurement became an important approach in rehabilitation processes, for example, in Posttraumatic Stress Disorder (PTSD) treatments.

Sleep and cognition in schizophrenia spectrum disorders: A systematic review protocol.

2020 ◽  
Author(s):  
Sean Carruthers ◽  
Gemma Brunetti ◽  
Susan Rossell
Keyword(s):  
Systematic Review ◽  
Mental Illnesses ◽  
Schizophrenia Spectrum Disorders ◽  
Systematic Review Protocol ◽  
Schizophrenia Spectrum Disorder ◽  
Schizophrenia Spectrum ◽  
Review Protocol ◽  
Spectrum Disorders ◽  
Sleep And Cognition ◽  
The Relationship

Schizophrenia spectrum disorders are chronic and debilitating mental illnesses characterised by both cognitive impairments and sleep deficits. In this systematic review protocol, we outline an approach to examine the available literature investigating the relationship between sleep and cognition in individuals with schizophrenia spectrum disorder.

Application of Artificial Intelligence in Forecasting: A Systematic Review

2019 ◽  
Author(s):  
Albert Annor Antwi ◽  
Ayman Abdulsalam Mohamed Al-Dherasi
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review
Sign in / Sign up

Export Citation Format

Share Document