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 Speech Recognition of Isolated Words using a New Speech Database in Sylheti

2019 ◽  
Vol 8 (3) ◽  
pp. 6259-6268
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Speech Recognition ◽  
Recognition System ◽  
Speech Recognition System ◽  
Natural Languages ◽  
Speech Database ◽  
Recognition Systems ◽  
Neural Network Classifiers ◽  
Engineering Psychology

With the advancements in the field of artificial intelligence, speech recognition based applications are becoming more and more popular in the recent years. Researchers working in many areas including linguistics, engineering, psychology, etc. have been trying to address various aspects relating to speech recognition in different natural languages around the globe. Although many interactive speech applications in "well-resourced" major languages are being developed, uses of these applications are still limited due to language barrier. Hence, researchers have also been concentrating to design speech recognition system in various under-resourced languages. Sylheti is one of such under-resourced languages primarily spoken in the Sylhet division of Bangladesh and also spoken in the southern part of Assam, India. This paper has two contributions: i) it presents a new speech database of isolated words for the Sylheti language, and ii) it presents speech recognition systems for the Sylheti language to recognize isolated Sylheti words by applying two variants of neural network classifiers. The performances of these recognition systems are evaluated with the proposed database and the observations are presented.

scholarly journals Modification of the Pre-Processing Stage of a Traffi Sign Recognition System Taking into Account the Characteristics of the Subject Area

Vestnik NSUEM ◽  
2020 ◽  
pp. 235-249
Author(s):  
S. Yu. Pchelintsev
Keyword(s):  
Processing Speed ◽  
Recognition System ◽  
Subject Area ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Road Signs ◽  
Sign Recognition ◽  
The Subject ◽  
Recognition Systems ◽  
High Level

Traffic sign recognition systems require a high level of responsiveness and accuracy with limited use of computing resources. The process of image pre-processing precedes the process of directly recognizing images, therefore, the recognition results depend on its effectiveness. When conducting pre-processing, it is important to take into account the features of the subject area, within which recognition is performed. The article discusses the process of pre-processing and preparing images in the context of creating a system for recognizing road signs. The main problems that arise during the operation of such a system are identified. Their solutions are proposed. Own combination of these solutions allowed us to create a new system for recognizing road signs, which gives a gain in processing speed by cutting off images of no interest before entering the classifier, and also taking into account the peculiarities of operation in an urban environment – more difficult conditions compared with recognition of road signs on tracks or on artificially created training grounds.

Smart MM

2019 ◽  
pp. 225-251
Author(s):  
Meenakshi Tripathi ◽  
Saatvik Shah ◽  
Prashant Bahal ◽  
Harsh Sharma ◽  
Ritika Gupta
Keyword(s):  
Artificial Intelligence ◽  
Emotion Recognition ◽  
Real Time ◽  
Management System ◽  
Recommendation System ◽  
Information Gain ◽  
Recognition System ◽  
Media Management ◽  
User Friendly ◽  
Made In

Rapid advancements have been made in the field of artificial intelligence in recent years. This has resulted in its adoption in various technologies from medicine to search engines. Existing media management systems have however not yet fully leveraged the power of artificial intelligence (AI) to give users enhanced information apart from basic media metadata. This chapter proposes a smart movie management system which works majorly offline and uses AI to deliver optimum information to the users on four vital tasks. These tasks are multilevel phrase level review polarity, plot and review keywords, a content-based recommendation system, and an emotion recognition system. The complete system works in near-real time with a user-friendly presentation to maximize a user's information gain.

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 Extracting Information on Affective Computing Research from Data Analysis of Known Digital Platforms: Research into Emotional Artificial Intelligence

Digital ◽  
2021 ◽  
Vol 1 (3) ◽  
pp. 162-172
Author(s):  
Nafissa Yusupova ◽  
Diana Bogdanova ◽  
Nadejda Komendantova ◽  
Hossein Hassani
Keyword(s):  
Artificial Intelligence ◽  
Affective Computing ◽  
Scientific Publication ◽  
Subject Area ◽  
Research Trends ◽  
Digital Platforms ◽  
Area Of Interest ◽  
Structured Information ◽  
The Subject ◽  
Citation Databases

The topic of affective computing has been growing rapidly in recent times. In the last five years, the volume of publications in this field has tripled. The question arises which research trends are most in demand today. This can only be judged by analysing the publications that present the results of research. Since researchers have access to the entire global scientific publication space, the task of analysing big data arises. This leads to the problem of identifying the most significant results in the subject area of interest. This paper presents some results of the analysis of semi-structured information from scientific citation databases on the subject of “affective computing”.

scholarly journals Development and Progress in Sensors and Technologies for Human Emotion Recognition

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5554 ◽  
Author(s):  
Shantanu Pal ◽  
Subhas Mukhopadhyay ◽  
Nagender Suryadevara
Keyword(s):  
Emotion Recognition ◽  
Future Internet ◽  
Humanoid Robots ◽  
Recognition System ◽  
Future Research ◽  
Human Beings ◽  
Human Emotion ◽  
Online Platforms ◽  
Learning Techniques ◽  
Recognition Systems

With the advancement of human-computer interaction, robotics, and especially humanoid robots, there is an increasing trend for human-to-human communications over online platforms (e.g., zoom). This has become more significant in recent years due to the Covid-19 pandemic situation. The increased use of online platforms for communication signifies the need to build efficient and more interactive human emotion recognition systems. In a human emotion recognition system, the physiological signals of human beings are collected, analyzed, and processed with the help of dedicated learning techniques and algorithms. With the proliferation of emerging technologies, e.g., the Internet of Things (IoT), future Internet, and artificial intelligence, there is a high demand for building scalable, robust, efficient, and trustworthy human recognition systems. In this paper, we present the development and progress in sensors and technologies to detect human emotions. We review the state-of-the-art sensors used for human emotion recognition and different types of activity monitoring. We present the design challenges and provide practical references of such human emotion recognition systems in the real world. Finally, we discuss the current trends in applications and explore the future research directions to address issues, e.g., scalability, security, trust, privacy, transparency, and decentralization.

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.

A Rule-Based Opto-Electronic Detection and Target Recognition System

2015 ◽  
Vol 734 ◽  
pp. 416-421 ◽  
Author(s):  
Jian Dan Zhong ◽  
Qin Zhang Wu ◽  
Zhen Ming Peng ◽  
Jin Zhang ◽  
Guang Le Yao
Keyword(s):  
Artificial Intelligence ◽  
General Model ◽  
Learning Algorithm ◽  
Target Recognition ◽  
Recognition System ◽  
Data Set ◽  
Rule Based ◽  
Standard Data ◽  
Electronic Detection ◽  
Recognition Systems

Opto-electronic detection and target recognition systems are widely used in the detection, monitoring, identification and other fields. In order to improve the flexibility and accuracy of this kind of system, we involved artificial intelligence technology into this area. As one of the most successful technology of artificial intelligence, expert systems (rule based systems) are widely used in industrial and intelligent control and other fields. This paper presents a general model of the rule based opto-electronic detection and object recognition systems. The model relies on the expert system tool—CLIPS which supports inference engine for reasoning. And a learning algorithm is used to generate the inference rules. In order to make the generated rules are easy to understand, decision tree algorithm was selected to apply in this general model. Finally, the model is applied to a vehicle identification test, a benchmark standard data-set from UCI machine learning repository was selected for this experiment. The experimental results show that the system has higher accuracy. Furthermore, this system is flexible for other target recognition as well, when the rules of relevant targets were added to this system.

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