AI-Based Visual Early Warning System
Facial expressions are a universally recognised means of conveying internal emotional states across diverse human cultural and ethnic groups. Recent advances in understanding people’s emotions expressed through verbal and non-verbal communication are particularly noteworthy in the clinical context f...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-08-01
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| Series: | Informatics |
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| Online Access: | https://www.mdpi.com/2227-9709/11/3/59 |
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| author | Zeena Al-Tekreeti Jeronimo Moreno-Cuesta Maria Isabel Madrigal Garcia Marcos A. Rodrigues |
| author_facet | Zeena Al-Tekreeti Jeronimo Moreno-Cuesta Maria Isabel Madrigal Garcia Marcos A. Rodrigues |
| author_sort | Zeena Al-Tekreeti |
| collection | DOAJ |
| description | Facial expressions are a universally recognised means of conveying internal emotional states across diverse human cultural and ethnic groups. Recent advances in understanding people’s emotions expressed through verbal and non-verbal communication are particularly noteworthy in the clinical context for the assessment of patients’ health and well-being. Facial expression recognition (FER) plays an important and vital role in health care, providing communication with a patient’s feelings and allowing the assessment and monitoring of mental and physical health conditions. This paper shows that automatic machine learning methods can predict health deterioration accurately and robustly, independent of human subjective assessment. The prior work of this paper is to discover the early signs of deteriorating health that align with the principles of preventive reactions, improving health outcomes and human survival, and promoting overall health and well-being. Therefore, methods are developed to create a facial database mimicking the underlying muscular structure of the face, whose Action Unit motions can then be transferred to human face images, thus displaying animated expressions of interest. Then, building and developing an automatic system based on convolution neural networks (CNN) and long short-term memory (LSTM) to recognise patterns of facial expressions with a focus on patients at risk of deterioration in hospital wards. This research presents state-of-the-art results on generating and modelling synthetic database and automated deterioration prediction through FEs with 99.89% accuracy. The main contributions to knowledge from this paper can be summarized as (1) the generation of visual datasets mimicking real-life samples of facial expressions indicating health deterioration, (2) improvement of the understanding and communication with patients at risk of deterioration through facial expression analysis, and (3) development of a state-of-the-art model to recognize such facial expressions using a ConvLSTM model. |
| format | Article |
| id | doaj-art-42d265393a2140c683b9ee04a57e7c0b |
| institution | OA Journals |
| issn | 2227-9709 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Informatics |
| spelling | doaj-art-42d265393a2140c683b9ee04a57e7c0b2025-08-20T01:55:33ZengMDPI AGInformatics2227-97092024-08-011135910.3390/informatics11030059AI-Based Visual Early Warning SystemZeena Al-Tekreeti0Jeronimo Moreno-Cuesta1Maria Isabel Madrigal Garcia2Marcos A. Rodrigues3Industry and Innovation Research Institute, Sheffield Hallam University, Sheffield S1 1WB, UKDepartment of Intensive Care, North Middlesex University Hospital, London N18 1QX, UKDepartment of Intensive Care, North Middlesex University Hospital, London N18 1QX, UKIndustry and Innovation Research Institute, Sheffield Hallam University, Sheffield S1 1WB, UKFacial expressions are a universally recognised means of conveying internal emotional states across diverse human cultural and ethnic groups. Recent advances in understanding people’s emotions expressed through verbal and non-verbal communication are particularly noteworthy in the clinical context for the assessment of patients’ health and well-being. Facial expression recognition (FER) plays an important and vital role in health care, providing communication with a patient’s feelings and allowing the assessment and monitoring of mental and physical health conditions. This paper shows that automatic machine learning methods can predict health deterioration accurately and robustly, independent of human subjective assessment. The prior work of this paper is to discover the early signs of deteriorating health that align with the principles of preventive reactions, improving health outcomes and human survival, and promoting overall health and well-being. Therefore, methods are developed to create a facial database mimicking the underlying muscular structure of the face, whose Action Unit motions can then be transferred to human face images, thus displaying animated expressions of interest. Then, building and developing an automatic system based on convolution neural networks (CNN) and long short-term memory (LSTM) to recognise patterns of facial expressions with a focus on patients at risk of deterioration in hospital wards. This research presents state-of-the-art results on generating and modelling synthetic database and automated deterioration prediction through FEs with 99.89% accuracy. The main contributions to knowledge from this paper can be summarized as (1) the generation of visual datasets mimicking real-life samples of facial expressions indicating health deterioration, (2) improvement of the understanding and communication with patients at risk of deterioration through facial expression analysis, and (3) development of a state-of-the-art model to recognize such facial expressions using a ConvLSTM model.https://www.mdpi.com/2227-9709/11/3/59facial expression (FE)facial expression recognition (FER)automatic facial expression recognition (AFER)machine learning (ML)deep learning (DL)convolution neural networks (CNN) |
| spellingShingle | Zeena Al-Tekreeti Jeronimo Moreno-Cuesta Maria Isabel Madrigal Garcia Marcos A. Rodrigues AI-Based Visual Early Warning System Informatics facial expression (FE) facial expression recognition (FER) automatic facial expression recognition (AFER) machine learning (ML) deep learning (DL) convolution neural networks (CNN) |
| title | AI-Based Visual Early Warning System |
| title_full | AI-Based Visual Early Warning System |
| title_fullStr | AI-Based Visual Early Warning System |
| title_full_unstemmed | AI-Based Visual Early Warning System |
| title_short | AI-Based Visual Early Warning System |
| title_sort | ai based visual early warning system |
| topic | facial expression (FE) facial expression recognition (FER) automatic facial expression recognition (AFER) machine learning (ML) deep learning (DL) convolution neural networks (CNN) |
| url | https://www.mdpi.com/2227-9709/11/3/59 |
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