posted on 2021-05-06, 10:45authored byAlessandro Simeone, Alessandra Caggiano, Lev Boun, Rebecca GrantRebecca Grant
This paper presents an intelligent cloud-based platform for workers healthcare monitoring and risk prevention in potentially hazardous manufacturing contexts. The platform is structured according to sequential modules dedicated to data acquisition, processing and decision-making support. Several sensors and data sources, including smart wearables, machine tool embedded sensors and environmental sensors, are employed for data collection, comprising information on offline clinical background, operational and environmental data. The cloud data processing module is responsible for extracting relevant features from the acquired data in order to feed a machine learning-based decision-making support system. The latter provides a classification of workers’ health status so that a prompt intervention can be performed in particularly challenging scenarios.
Funding
Research Start-up Fund Subsidized Project of Shantou University, China, (No. NFT17004)
CLOUD MODE “CLOUD Manufacturing for On-Demand manufacturing sErvices” of the University of Naples Federico II, Italy (000011-ALTRI_ DR_3450_2016_RICERCA_ATENEO-CAGGIANO)
History
School
Mechanical, Electrical and Manufacturing Engineering
Published in
Procedia CIRP
Volume
99
Pages
50 - 56
Source
14th CIRP Conference on Intelligent Computation in Manufacturing Engineering, 15-17 July 2020
This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/