Convergence of Artificial Intelligence and Internet of Things in Smart Healthcare: A Case Study of Voice Pathology Detection
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The integration of artificial intelligence (AI) and the Internet of Things (IoT) has tremendous prospects in smart healthcare. The advancement of AI in the form of deep learning brought a revolution in automatic classification and detection systems. In addition, next-generation wireless communications such as 5G networking brought speed and the seamless transmission of data. With the convergence of these elements, the smart healthcare sector is currently booming. Particularly during the post-COVID-19 pandemic, the necessity of smart healthcare has come to light more than before. A significant number of people suffer from voice pathology. This pathology can be easily cured if detected early. In this study, a voice pathology detection system within a smart healthcare framework is proposed. The inputs are obtained by the IoT, namely microphones and electroglottography (EGG) devices to capture voice and EGG signals, respectively. Spectrograms are obtained from these signals and fed into a pretrained convolutional neural network (CNN). The features extracted from the CNN are fused and processed using a bi-directional long short-term memory network. The proposed system is evaluated using a publicly available database, called the Saarbruecken voice database. The experimental results show that bimodal input performs better than a single input. An accuracy of 95.65% is obtained for the proposed system.
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In the world of healthcare, the word artificial intelligence plays a very important and critical function in interpreting the human thinking process through the computer employing many efficient AI-based algorithms to make the difficult examination easy for healthcare professionals.
“In the world of healthcare, the word artificial intelligence plays a very important and critical function in interpreting the human thinking process through the computer employing many efficient AI-based algorithms to make the difficult examination easy for healthcare professionals [167].”
From Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues (2023) · cites this paper as [167]
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- GHULAM MUHAMMAD (Senior Member, IEEE) received the B.S. degree in computer sci- ence and engineering from the Bangladesh Uni- versity of Engineering and Technology, in 1997, and the M.S. and Ph.D. degrees in electronic and information engineering from the Toyohashi University of Technology, Japan, in 2003 and 2006, respectively. He is currently a Professor with the Department of Computer Engineering, College of Computer and Information Sciences, King Saud University (KSU), Riyadh, Saudi Arabia. He is a principal investigator and a co-principal investigator in many research projects. He has authored or coauthored more than 250 publications, including for IEEE/ACM/Springer/Elsevier journals, and flagship conference papers, and he holds two U.S. patents. His research interests include signal processing, machine learning, the IoT, medical signal and image analysis, AI, and biometrics. He was a recipient of the Japan Society for Promotion and Science (JSPS) Fellowship from the Ministry of Education, Culture, Sports, Science, and Technology, Japan. He received the Best Faculty Award of the Computer Engineering Department, KSU, from 2014 to 2015. He has supervised more than 15 Ph.D. and master's theses. MUSAED ALHUSSEIN (Member, IEEE) was born in Riyadh, Saudi Arabia. He received the B.S. degree in computer engineering from King Saud University, Riyadh, in 1988, and the M.S. and Ph.D. degrees in computer science and engineer- ing from the University of South Florida, Tampa, Florida, in 1992 and 1997, respectively. Since 1997, he has been a faculty of the Computer Engi- neering Department, College of Computer and Information Science, King Saud University, where he is currently the Founder and the Director of the Embedded Computing and Signal Processing Research Laboratory. His research interests include computer architecture and signal processing, and in particular on VLSI testing and verification, embedded and pervasive computing, cyber-physical systems, mobile cloud computing, big data, e-healthcare, and body area networks.
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- Authors
- Muhammad, G., & Alhussein, M.
- Affiliations
- King Saud University (SA)
- Venue
- IEEE Access
- Published
- 2021
- Volume
- 9
- Pages
- 89198-89209
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3174711822
- Cited by
- 70
- References
- 57
- Citations by year
- 2026: 9 · 2025: 10 · 2024: 21 · 2023: 16 · 2022: 13 · 2021: 1
- FWCI
- 8.05
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Deanship of Scientific Research at King Saud University, Riyadh, Saudi Arabia (RG-1436-016)
- Topics
- Health Sciences › Medicine › Physiology › Voice and Speech Disorders
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- voice pathology detection, smart healthcare, Internet of Things, electroglottography, spectrogram, convolutional neural networks, bidirectional LSTM, multimodal fusion, Saarbruecken Voice Database, 5G, deep learning
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