Olatinwo, DDHancke, GMyburgh, HAbu-Mahfouz, Adnan MI2023-10-262023-10-262023-03Olatinwo, 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/10204/131781424-8220https://doi.org/10.3390/s23062948http://hdl.handle.net/10204/13178Internet 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%.FulltextenIoT WBANMachine learningDeep learningEdge AISpeech emotionConvolutional neural networkCNNBiLSTMStandard scalerMin–max scalerRobust scalerData augmentationSpectrogramsRegularization techniquesMel spectrogramArticleOlatinwo, 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/10204/13178Olatinwo, 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/10204/13178Olatinwo 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.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 -27079