dc.contributor.author |
Olatinwo, DD
|
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dc.contributor.author |
Hancke, G
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|
dc.contributor.author |
Myburgh, H
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dc.contributor.author |
Abu-Mahfouz, Adnan MI
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dc.date.accessioned |
2023-10-26T09:57:33Z |
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dc.date.available |
2023-10-26T09:57:33Z |
|
dc.date.issued |
2023-03 |
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dc.identifier.citation |
Olatinwo, D., Hancke, G., Myburgh, H. & Abu-Mahfouz, A.M. 2023. IoT-enabled WBAN and machine learning for speech emotion recognition in patients. <i>Sensors, 23(6).</i> http://hdl.handle.net/10204/13178 |
en_ZA |
dc.identifier.issn |
1424-8220 |
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dc.identifier.uri |
https://doi.org/10.3390/s23062948
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dc.identifier.uri |
http://hdl.handle.net/10204/13178
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|
dc.description.abstract |
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%. |
en_US |
dc.format |
Fulltext |
en_US |
dc.language.iso |
en |
en_US |
dc.relation.uri |
https://www.mdpi.com/1424-8220/23/6/2948 |
en_US |
dc.source |
Sensors, 23(6) |
en_US |
dc.subject |
IoT WBAN |
en_US |
dc.subject |
Machine learning |
en_US |
dc.subject |
Deep learning |
en_US |
dc.subject |
Edge AI |
en_US |
dc.subject |
Speech emotion |
en_US |
dc.subject |
Convolutional neural network |
en_US |
dc.subject |
CNN |
en_US |
dc.subject |
BiLSTM |
en_US |
dc.subject |
Standard scaler |
en_US |
dc.subject |
Min–max scaler |
en_US |
dc.subject |
Robust scaler |
en_US |
dc.subject |
Data augmentation |
en_US |
dc.subject |
Spectrograms |
en_US |
dc.subject |
Regularization techniques |
en_US |
dc.subject |
Mel spectrogram |
en_US |
dc.title |
IoT-enabled WBAN and machine learning for speech emotion recognition in patients |
en_US |
dc.type |
Article |
en_US |
dc.description.pages |
23 |
en_US |
dc.description.note |
Copyright: © 2023 by the authors. 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/ |
en_US |
dc.description.cluster |
Next Generation Enterprises & Institutions |
en_US |
dc.description.impactarea |
EDT4IR Management |
en_US |
dc.identifier.apacitation |
Olatinwo, D., Hancke, G., Myburgh, H., & Abu-Mahfouz, A. M. (2023). IoT-enabled WBAN and machine learning for speech emotion recognition in patients. <i>Sensors, 23(6)</i>, http://hdl.handle.net/10204/13178 |
en_ZA |
dc.identifier.chicagocitation |
Olatinwo, DD, G Hancke, H Myburgh, and Adnan MI Abu-Mahfouz "IoT-enabled WBAN and machine learning for speech emotion recognition in patients." <i>Sensors, 23(6)</i> (2023) http://hdl.handle.net/10204/13178 |
en_ZA |
dc.identifier.vancouvercitation |
Olatinwo D, Hancke G, Myburgh H, Abu-Mahfouz AM. IoT-enabled WBAN and machine learning for speech emotion recognition in patients. Sensors, 23(6). 2023; http://hdl.handle.net/10204/13178. |
en_ZA |
dc.identifier.ris |
TY - Article
AU - Olatinwo, DD
AU - Hancke, G
AU - Myburgh, H
AU - Abu-Mahfouz, Adnan MI
AB - 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%.
DA - 2023-03
DB - ResearchSpace
DP - CSIR
J1 - Sensors, 23(6)
KW - IoT WBAN
KW - Machine learning
KW - Deep learning
KW - Edge AI
KW - Speech emotion
KW - Convolutional neural network
KW - CNN
KW - BiLSTM
KW - Standard scaler
KW - Min–max scaler
KW - Robust scaler
KW - Data augmentation
KW - Spectrograms
KW - Regularization techniques
KW - Mel spectrogram
LK - https://researchspace.csir.co.za
PY - 2023
SM - 1424-8220
T1 - IoT-enabled WBAN and machine learning for speech emotion recognition in patients
TI - IoT-enabled WBAN and machine learning for speech emotion recognition in patients
UR - http://hdl.handle.net/10204/13178
ER -
|
en_ZA |
dc.identifier.worklist |
27079 |
en_US |