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IoT-Enabled WBAN and Machine Learning for Speech Emotion Recognition in Patients
Internet of things (IoT)-enabled wireless body area network (WBAN) is an emerging technology that combines medical devices, wireless devices, and non-medical devices for healthcare management applications. Speech emotion recognition (SER) is an active research field in the healthcare domain and mach...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10056097/ https://www.ncbi.nlm.nih.gov/pubmed/36991659 http://dx.doi.org/10.3390/s23062948 |
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author | Olatinwo, Damilola D. Abu-Mahfouz, Adnan Hancke, Gerhard Myburgh, Hermanus |
author_facet | Olatinwo, Damilola D. Abu-Mahfouz, Adnan Hancke, Gerhard Myburgh, Hermanus |
author_sort | Olatinwo, Damilola D. |
collection | PubMed |
description | Internet of things (IoT)-enabled wireless body area network (WBAN) is an emerging technology that combines medical devices, wireless devices, and non-medical devices for healthcare management applications. Speech emotion recognition (SER) is an active research field in the healthcare domain and machine learning. It is a technique that can be used to automatically identify speakers’ emotions from their speech. However, the SER system, especially in the healthcare domain, is confronted with a few challenges. For example, low prediction accuracy, high computational complexity, delay in real-time prediction, and how to identify appropriate features from speech. Motivated by these research gaps, we proposed an emotion-aware IoT-enabled WBAN system within the healthcare framework where data processing and long-range data transmissions are performed by an edge AI system for real-time prediction of patients’ speech emotions as well as to capture the changes in emotions before and after treatment. Additionally, we investigated the effectiveness of different machine learning and deep learning algorithms in terms of performance classification, feature extraction methods, and normalization methods. We developed a hybrid deep learning model, i.e., convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM), and a regularized CNN model. We combined the models with different optimization strategies and regularization techniques to improve the prediction accuracy, reduce generalization error, and reduce the computational complexity of the neural networks in terms of their computational time, power, and space. Different experiments were performed to check the efficiency and effectiveness of the proposed machine learning and deep learning algorithms. The proposed models are compared with a related existing model for evaluation and validation using standard performance metrics such as prediction accuracy, precision, recall, F1 score, confusion matrix, and the differences between the actual and predicted values. The experimental results proved that one of the proposed models outperformed the existing model with an accuracy of about 98%. |
format | Online Article Text |
id | pubmed-10056097 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100560972023-03-30 IoT-Enabled WBAN and Machine Learning for Speech Emotion Recognition in Patients Olatinwo, Damilola D. Abu-Mahfouz, Adnan Hancke, Gerhard Myburgh, Hermanus Sensors (Basel) Article Internet of things (IoT)-enabled wireless body area network (WBAN) is an emerging technology that combines medical devices, wireless devices, and non-medical devices for healthcare management applications. Speech emotion recognition (SER) is an active research field in the healthcare domain and machine learning. It is a technique that can be used to automatically identify speakers’ emotions from their speech. However, the SER system, especially in the healthcare domain, is confronted with a few challenges. For example, low prediction accuracy, high computational complexity, delay in real-time prediction, and how to identify appropriate features from speech. Motivated by these research gaps, we proposed an emotion-aware IoT-enabled WBAN system within the healthcare framework where data processing and long-range data transmissions are performed by an edge AI system for real-time prediction of patients’ speech emotions as well as to capture the changes in emotions before and after treatment. Additionally, we investigated the effectiveness of different machine learning and deep learning algorithms in terms of performance classification, feature extraction methods, and normalization methods. We developed a hybrid deep learning model, i.e., convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM), and a regularized CNN model. We combined the models with different optimization strategies and regularization techniques to improve the prediction accuracy, reduce generalization error, and reduce the computational complexity of the neural networks in terms of their computational time, power, and space. Different experiments were performed to check the efficiency and effectiveness of the proposed machine learning and deep learning algorithms. The proposed models are compared with a related existing model for evaluation and validation using standard performance metrics such as prediction accuracy, precision, recall, F1 score, confusion matrix, and the differences between the actual and predicted values. The experimental results proved that one of the proposed models outperformed the existing model with an accuracy of about 98%. MDPI 2023-03-08 /pmc/articles/PMC10056097/ /pubmed/36991659 http://dx.doi.org/10.3390/s23062948 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Olatinwo, Damilola D. Abu-Mahfouz, Adnan Hancke, Gerhard Myburgh, Hermanus IoT-Enabled WBAN and Machine Learning for Speech Emotion Recognition in Patients |
title | IoT-Enabled WBAN and Machine Learning for Speech Emotion Recognition in Patients |
title_full | IoT-Enabled WBAN and Machine Learning for Speech Emotion Recognition in Patients |
title_fullStr | IoT-Enabled WBAN and Machine Learning for Speech Emotion Recognition in Patients |
title_full_unstemmed | IoT-Enabled WBAN and Machine Learning for Speech Emotion Recognition in Patients |
title_short | IoT-Enabled WBAN and Machine Learning for Speech Emotion Recognition in Patients |
title_sort | iot-enabled wban and machine learning for speech emotion recognition in patients |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10056097/ https://www.ncbi.nlm.nih.gov/pubmed/36991659 http://dx.doi.org/10.3390/s23062948 |
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