Smart Health Solution Integrating IoT and Cloud: A Case Study of Voice Pathology Monitoring
Abstract
The integration of the IoT and cloud technology is very important to have a better solution for an uninterrupted, secured, seamless, and ubiquitous framework. The complementary nature of the IoT and the could in terms of storage, processing, accessibility, security, service sharing, and components makes the convergence suitable for many applications. The advancement of mobile technologies adds a degree of flexibility to this solution. The health industry is one of the venues that can benefit from IoT–Cloud technology, because of the scarcity of specialized doctors and the physical movement restrictions of patients, among other factors. In this article, as a case study, we discuss the feasibility of and propose a solution for voice pathology monitoring of people using IoT–cloud. More specifically, a voice pathology detection system is proposed inside the monitoring framework using a local binary pattern on a Mel-spectrum representation of the voice signal, and an extreme learning machine classifier to detect the pathology. The proposed monitoring framework can achieve high accuracy of detection, and it is easy to use.
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The e-healthcare system may give general support on a larger scale, such as management assistance, healthcare service delivery, and so on, as well as specialized help, such as citizen health data.
“The e-healthcare system may give general support on a larger scale, such as management assistance, healthcare service delivery, and so on, as well as specialized help, such as citizen health data [135].”
From Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues (2023) · cites this paper as [135]
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- Authors
- Muhammad, G., Rahman, S. M. M., Alelaiwi, A., & Alamri, A.
- Affiliations
- King Saud University (SA)
- Venue
- IEEE Communications Magazine
- Published
- 2017-01
- Volume
- 55
- Issue
- 1
- Pages
- 69-73
- ISSN
- 0163-6804 (ISSN-L)
- OpenAlex
- W2578907517
- Cited by
- 244
- References
- 12
- Citations by year
- 2026: 5 · 2025: 10 · 2024: 15 · 2023: 26 · 2022: 39 · 2021: 35 · 2020: 38 · 2019: 28 · 2018: 33 · 2017: 15
- FWCI
- 58.04
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Communications Magazine (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Health Sciences › Medicine › Physiology › Voice and Speech Disorders
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- voice pathology detection, IoT-cloud integration, remote patient monitoring, local binary pattern, Mel spectrum, extreme learning machine, voice signal analysis, mobile health
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