Emotion Monitoring System Based on Sensors and Data Analysis II
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 229
Special Issue Editor
Interests: automatic control and robotics; modeling and identification; estimation; artificial intelligence; evolutionary computations; computational intelligence; cognitive systems
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Special Issue Information
Dear Colleagues,
The automatic monitoring of emotions is an issue that must be solved to successfully perform various tasks, from a more natural human–computer interaction to assessing the effectiveness of a patient's medical therapy. The most advanced solutions are based on a fusion of many data sources, including audiovisual measurements and text and medical parameters. These data are then processed by advanced data analysis algorithms, from statistics and mathematical modeling to machine learning and artificial neural networks.
The great complexity of the problem of recognizing emotions, which in some conditions is difficult even for people, requires a large data stream, which, in subsequent stages, is processed into features with an increasingly high level of abstraction, ending with a rather-difficult-to-define concept of emotion. This is in line with deep learning, in which raw data in subsequent stages result in a higher level of features.
For this reason, deep neural networks are considered one of the most promising methods of data analysis in emotion monitoring systems (EMS). Deep neural networks, if applicable at all, are, however, only part of a larger process that includes the following steps:
- Selecting the right set of sensors and methods for measuring and combining data (raw data are created at this stage);
- Preliminary processing (selection of features) so that the original large data stream can be limited to the appropriate size and meaning (at this stage, statistical analysis and machine learning methods are used, supported by expert knowledge);
- Building a system to analyze the processed data stream to generate output variables describing emotions.
The second stage is extremely important because it is part of data mining and iterative learning based on data; it enables the integration of expert knowledge but does not yet generate a solution in the form of the so-called black box (ultimately) directly processing raw data.
The proposed topic of EMS based on sensors and data analysis (SDA) also includes its specific applications in the form of human reaction validation (HRV) subprojects, whose main goal is to develop a data-based tool that will allow estimating or forecasting the broadly understood emotional state of humans. In the simplest case, a database model based on the data lake provided can be developed, describing the effect of certain stimuli on people. On the other hand, one can try to develop a cybernetic human psychological model based on the same data and adapt to the needs of various projects.
We are pleased to invite you to submit your papers to this Special Issue of Sensors, "Emotion Monitoring System Based on Sensors and Data Analysis". This Special Issue aims to discuss the latest research progress in the above described field of emotion monitoring. We encourage submissions of conceptual and empirical papers focused on this subject. Different types of approaches related to this field are welcome.
Prof. Dr. Zdzislaw Kowalczuk
Guest Editor
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Keywords
- sensor networks
- data analysis
- data fusion
- human–computer interactions
- diagnosis and medical treatment
- statistical analysis
- mathematical modeling
- cybernetic models
- artificial and deep neural networks
- machine learning
- expert knowledge
- monitoring, recognizing, estimating, and forecasting of emotions
- agent and robotic applications
- emotion monitoring used in autonomous systems
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