Medical intelligence using PPG signals and hybrid learning at the edge to detect fatigue in physical activities
The educational environment plays a vital role in the development of students who participate in athletic pursuits both in terms of their physical health and their ability to detect fatigue. As a result of recent advancements in deep learning and biosensors benefitting from edge computing resources,...
Published in: | SCIENTIFIC REPORTS |
---|---|
Main Authors: | Liu, Ping; Song, Yazhou; Yang, Xuan; Li, Dejuan; Khosravi, M. |
Format: | Article |
Language: | English |
Published: |
NATURE PORTFOLIO
2024
|
Subjects: | |
Online Access: | https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001270360800032 |
Similar Items
-
Medical intelligence using PPG signals and hybrid learning at the edge to detect fatigue in physical activities
by: Liu P.; Song Y.; Yang X.; Li D.; Khosravi M.
Published: (2024) -
Acoustic emission signal for fatigue crack classification on reinforced concrete beam
by: Md Nor N.; Ibrahim A.; Muhamad Bunnori N.; Mohd Saman H.
Published: (2013) -
Numerical Analysis of Single Edge Notched Tension Specimen with Fatigue Crack Parameter of Conventional Specimen Using Linear Elastic Fracture Mechanics
by: Busari Y.O.; Abdullah S.; Manurung Y.H.P.; Shuaib-Babata Y.L.
Published: (2021) -
Fatigue damage monitoring using un-supervised clustering method of acoustic emission signal on SAE 1045 steel
by: Mohammad M.; Tajuddin A.; Abdullah S.; Jamaluddin N.; Murat B.I.S.
Published: (2016) -
Multi-level parallel scheduling of dependent-tasks using graph-partitioning and hybrid approaches over edge-cloud
by: Kaur M.; Kadam S.; Hannoon N.
Published: (2022)