scholarly journals Artificial intelligence for affective computing: an emotion recognition case study

2020 ◽  
pp. 29-44
Keyword(s):  
Artificial Intelligence ◽  
Emotion Recognition ◽  
Affective Computing

Research on Affective Recognition with Physiological Signals Based on IAGA and wkNN Classification

2010 ◽  
Vol 143-144 ◽  
pp. 677-681
Author(s):  
Hai Ning Wang ◽  
Shou Qian Sun ◽  
Ting Shu ◽  
Jian Feng Wu
Keyword(s):  
Emotion Recognition ◽  
Affective Computing ◽  
Feature Reduction ◽  
Physiological Signals ◽  
Experimental Simulation ◽  
Adaptive Genetic Algorithm ◽  
Knn Classifier ◽  
Important Field ◽  
Low Efficiency

The ability to understand human emotions is desirable for the computer in many applications recently. Recording and recognizing physiological signals of emotion has become an increasingly important field of research in affective computing and human computer interaction. For the problem of feature redundancy of physiological signals-based emotion recognition and low efficiency of traditional feature reduction algorithms on great sample data, this paper proposed an improved adaptive genetic algorithm (IAGA) to solve the problem of emotion feature selection, and then presented a weighted kNN classifier (wkNN) to classify features by making full use of emotion sample information. We demonstrated a case study of emotion recognition application and verified the algorithm's validity by the analysis of experimental simulation data and the comparison of several recognition methods.

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 Graph Representation Integrating Signals for Emotion Recognition and Analysis

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4035
Author(s):  
Teresa Zawadzka ◽  
Tomasz Wierciński ◽  
Grzegorz Meller ◽  
Mateusz Rock ◽  
Robert Zwierzycki ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Affective Computing ◽  
Graph Model ◽  
Data Retrieval ◽  
Graph Representation ◽  
Multiple Sources ◽  
Vast Number ◽  
Video Clips ◽  
Query Patterns

Data reusability is an important feature of current research, just in every field of science. Modern research in Affective Computing, often rely on datasets containing experiments-originated data such as biosignals, video clips, or images. Moreover, conducting experiments with a vast number of participants to build datasets for Affective Computing research is time-consuming and expensive. Therefore, it is extremely important to provide solutions allowing one to (re)use data from a variety of sources, which usually demands data integration. This paper presents the Graph Representation Integrating Signals for Emotion Recognition and Analysis (GRISERA) framework, which provides a persistent model for storing integrated signals and methods for its creation. To the best of our knowledge, this is the first approach in Affective Computing field that addresses the problem of integrating data from multiple experiments, storing it in a consistent way, and providing query patterns for data retrieval. The proposed framework is based on the standardized graph model, which is known to be highly suitable for signal processing purposes. The validation proved that data from the well-known AMIGOS dataset can be stored in the GRISERA framework and later retrieved for training deep learning models. Furthermore, the second case study proved that it is possible to integrate signals from multiple sources (AMIGOS, ASCERTAIN, and DEAP) into GRISERA and retrieve them for further statistical analysis.

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 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.

Case Study: Forecast Performance of Potential Zone Using Artificial Intelligence AI in Deltaic Mature Field

2016 ◽  
Author(s):  
Pradhipta Seno Respati ◽  
Cesti Ardan ◽  
Muchammad R. Alfaqih
Keyword(s):  
Artificial Intelligence ◽  
Potential Zone ◽  
Forecast Performance ◽  
Mature Field

scholarly journals Managing Artificial Intelligence on Public Transportation (Case Study Jakarta City, Indonesia)

2021 ◽  
Vol 717 (1) ◽  
pp. 012021
Author(s):  
Keisha Dinya Solihati ◽  
Dian Indriyani
Keyword(s):  
Artificial Intelligence ◽  
Public Transportation

Monthly inflow forecasting utilizing advanced artificial intelligence methods: a case study of Haditha Dam in Iraq

2021 ◽  
Author(s):  
Mohammed Falah Allawi ◽  
Intesar Razaq Hussain ◽  
Majid Ibrahim Salman ◽  
Ahmed El-Shafie
Keyword(s):  
Artificial Intelligence ◽  
Artificial Intelligence Methods ◽  
Inflow Forecasting

scholarly journals UNDERSTANDING SPECTRUM BIAS IN ALGORITHMS DERIVED BY ARTIFICIAL INTELLIGENCE A CASE STUDY IN DETECTING AORTIC STENOSIS USING ELECTROCARDIOGRAMS

2021 ◽  
Vol 77 (18) ◽  
pp. 3241
Author(s):  
Andrew S. Tseng ◽  
Michal Shelly-Cohen ◽  
Zachi Itzhak Attia ◽  
Peter Noseworthy ◽  
Paul Friedman ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Aortic Stenosis
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