Files and links (2)
Published VersionCC BY V4.0, Open Access
Journal article
Machine Learning and AI Technologies for Smart Wearables
Electronics , Vol.12(7), pp.1-21
2023
Abstract
The recent progress in computational, communications, and artificial intelligence (AI) technologies, and the widespread availability of smartphones together with the growing trends in multimedia data and edge computation devices have led to new models and paradigms for wearable devices. This paper presents a comprehensive survey and classification of smart wearables and research prototypes using machine learning and AI technologies. The paper aims to survey these new paradigms for machine learning and AI for wearables from various technological perspectives which have emerged, including: (1) smart wearables empowered by machine learning and AI; (2) data collection architectures and information processing models for AI smart wearables; and (3) applications for AI smart wearables. The review covers a wide range of enabling technologies for AI and machine learning for wearables and research prototypes. The main findings of the review are that there are significant technical challenges for AI smart wearables in networking and communication aspects such as issues for routing and communication overheads, information processing and computational aspects such as issues for computational complexity and storage, and algorithmic and application-dependent aspects such as training and inference. The paper concludes with some future directions in the smart wearable market and potential research.
Details
- Title
- Machine Learning and AI Technologies for Smart Wearables
- Authors
- Kah Phooi Seng (Corresponding Author) - Xi'an Jiaotong UniversityAng Li-Minn (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringPeter Eno (Author) - Federal University Oye EkitiAnthony Mmonyi (Author) - Afe Babalola University
- Publication details
- Electronics , Vol.12(7), pp.1-21
- Publisher
- MDPI AG
- Date published
- 2023
- DOI
- 10.3390/electronics12071509
- ISSN
- 2079-9292
- Copyright note
- © 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/).
- Organisation Unit
- School of Science, Technology and Engineering; Engage Research Lab
- Language
- English
- Record Identifier
- 99718256802621
- Output Type
- Journal article
Metrics
164 File views/ downloads
81 Record Views
InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Collaboration types
- Domestic collaboration
- International collaboration
- Web Of Science research areas
- Computer Science, Information Systems
- Engineering, Electrical & Electronic
- Physics, Applied
UN Sustainable Development Goals (SDGs)
This output has contributed to the advancement of the following goals:
Source: InCites