UM  > Faculty of Science and Technology  > DEPARTMENT OF ELECTROMECHANICAL ENGINEERING
Residential Collegefalse
Status已發表Published
Recent advances in wearable electromechanical sensors—Moving towards machine learning-assisted wearable sensing systems
Nian Dai1; Iek Man Lei1; Zhaoyang Li1; Yi Li3; Peng Fang2; Junwen Zhong1
2022-12
Source PublicationNano Energy
ISSN2211-2855
Volume105Pages:108041
Abstract

With the assistance of powerful machine learning algorithms, data collecting and processing efficiency of wearable electromechanical sensors are highly improved. Meanwhile, the functions and applications of these intelligent sensing systems are widely enhanced and expanded. In this review, wearable electromechanical sensors with various working mechanisms and their typical usage for monitoring human physiological signals are outlined. The recent advances of machine learning-assisted wearable electromechanical sensing systems in specific applications of tactile perception, gesture/gait recognition, and health care are then summarized and discussed. Finally, current existing limitations and future perspectives are discussed. The progress of intelligent wearable electromechanical sensing systems will promote the development in the domains of human-machine interface (HMI), soft robotics, metaverse, etc.

DOIhttps://doi.org/10.1016/j.nanoen.2022.108041
URLView the original
Indexed BySCIE
Fulltext Access
Citation statistics
Document TypeJournal article
CollectionDEPARTMENT OF ELECTROMECHANICAL ENGINEERING
Corresponding AuthorJunwen Zhong
Affiliation1.Department of Electromechanical Engineering and Centre for Artificial Intelligence and Robotics, University of Macau, 999078, Macau, China
2.CAS Key Laboratory of Human-Machine Intelligent-Synergy Systems, Shenzhen Institutes of Advanced Technology & Shenzhen Engineering Laboratory of Neral Rehabilitation Technology, Shenzhen 518055, PR China
3.Department of Sociology, University of Macau, 999078, Macau, China
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Nian Dai,Iek Man Lei,Zhaoyang Li,et al. Recent advances in wearable electromechanical sensors—Moving towards machine learning-assisted wearable sensing systems[J]. Nano Energy,2022,105:108041.
APA Nian Dai,Iek Man Lei,Zhaoyang Li,Yi Li,Peng Fang,&Junwen Zhong.(2022).Recent advances in wearable electromechanical sensors—Moving towards machine learning-assisted wearable sensing systems.Nano Energy,105,108041.
MLA Nian Dai,et al."Recent advances in wearable electromechanical sensors—Moving towards machine learning-assisted wearable sensing systems".Nano Energy 105(2022):108041.
Files in This Item: Download All
File Name/Size Publications Version Access License
1-s2.0-S221128552201(21663KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Nian Dai]'s Articles
[Iek Man Lei]'s Articles
[Zhaoyang Li]'s Articles
Baidu academic
Similar articles in Baidu academic
[Nian Dai]'s Articles
[Iek Man Lei]'s Articles
[Zhaoyang Li]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Nian Dai]'s Articles
[Iek Man Lei]'s Articles
[Zhaoyang Li]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: 1-s2.0-S2211285522011193-main.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.