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  • 标题:Machine Learning approach towards real time assessment of hand-arm vibration risk
  • 本地全文:下载
  • 作者:G. Aiello ; A. Certa ; Islam Abusohyon
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2021
  • 卷号:54
  • 期号:1
  • 页码:1187-1192
  • DOI:10.1016/j.ifacol.2021.08.140
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractIn industry 4,0, the establishment of an interconnected environment where human operators cooperate with the machines offers the opportunity for substantially improving the ergonomics and safety conditions of the workplace. This topic is discussed in the paper referring to the vibration risk, which is a well-known cause of work-related pathologies. A wearable device has been developed to collect vibration data and to segment the signals obtained in time windows. A machine learning classifier is then proposed to recognize the worker’s activity and to evaluate the exposure to vibration risks. The experimental results demonstrate the feasibility and effectiveness of the methodology proposed.
  • 关键词:KeywordsAccelerometersArtificial intelligenceactivity recognitionvibration risk
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