Machine Learning for Healthcare Wearable Devices: The Big Picture
Open accessAbstract
Using artificial intelligence and machine learning techniques in healthcare applications has been actively researched over the last few years. It holds promising opportunities as it is used to track human activities and vital signs using wearable devices and assist in diseases’ diagnosis, and it can play a great role in elderly care and patient’s health monitoring and diagnostics. With the great technological advances in medical sensors and miniaturization of electronic chips in the recent five years, more applications are being researched and developed for wearable devices. Despite the remarkable growth of using smart watches and other wearable devices, a few of these massive research efforts for machine learning applications have found their way to market. In this study, a review of the different areas of the recent machine learning research for healthcare wearable devices is presented. Different challenges facing machine learning applications on wearable devices are discussed. Potential solutions from the literature are presented, and areas open for improvement and further research are highlighted.
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Many models for neural speech enhancement were developed using RNN [98].
“Many models were developed through usage of RNN [98], the authors in [99] built on these models, but using TinyML.”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [98]
Section 2.2 discusses machine-learning models for wearable-device tasks, and the Discussion mentions audio monitoring, but neither addresses neural speech enhancement with RNNs. Searches for “neural speech enhancement,” “RNN,” and “recurrent neural network audio noise suppression speech” found no passage supporting the attributed claim. (LLM, conf=0.73)
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References 179 · Cited by 321
Research works printed but not linked: 12
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- Authors
- Sabry, F., Eltaras, T., Labda, W., Alzoubi, K., & Malluhi, Q.
- Affiliations
- Qatar University (QA)
- Community College of Qatar (QA)
- Corresponding author
- Sabry, Farida
- Venue
- Journal of Healthcare Engineering
- Publisher
- Wiley
- Published
- 2022-04-18
- Volume
- 2022
- Pages
- 1-25
- ISSN
- 2040-2295 (ISSN-L)
- 2040-2309
- PMC
- PMC9038375
- OpenAlex
- W4224242327
- Cited by
- 321
- References
- 179
- Citations by year
- 2026: 65 · 2025: 99 · 2024: 82 · 2023: 64 · 2022: 11
- FWCI
- 5.45
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of Healthcare Engineering (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PORTO@Iris (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Qatar University QSpace (Qatar University) (submittedVersion)
- Funders
- Qatar National Research Fund (ECRA 01-006-1-001)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- machine learning in healthcare, activity monitoring, vital signs monitoring, disease diagnosis, elderly care, patient health monitoring, smartwatches, biomedical sensors, resource-constrained devices
- MeSH terms
- Machine Learning, Wearable Electronic Devices, Aged, Artificial Intelligence, Delivery of Health Care, Health Facilities, Humans
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