Worker safety in agriculture 4.0: A new approach for mapping operator’s vibration risk through Machine Learning activity recognition
Computers and Electronics in Agriculture, ISSN: 0168-1699, Vol: 193, Page: 106637
2022
- 23Citations
- 108Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
While being a fundamental driver of competitiveness in agroindustry, technological innovation has also introduced new critical elements related, for example, to the sustainability of the production processes as well as to the safety of workers. In such regard, the advent of the 4th industrial revolution (Agriculture 4.0) based on digitalization, is an unprecedented opportunity of rethinking the role of innovation in a new human-centric perspective. In particular, the establishment of an interconnected work environment and the augmentation of the operator’s physical, sensorial, and cognitive capabilities, are two technologies which can be effectively employed for substantially improving the ergonomics and safety conditions on the workplace. This paper approaches such topic referring to the vibration risk, which is a well-known cause of work-related pathologies, and proposes an original methodology for mapping the risk exposure of the operators to the activities performed. A miniaturized wearable device is employed to collect vibration data, and the signals obtained are segmented in time windows and processed in order to extract the significant features. Finally, a machine learning classifier has been developed to recognize the worker’s activity and to evaluate the related exposure to vibration risks. To validate the methodology proposed, an experimental analysis in real operating conditions has been finally carried out by monitoring the activities performed by a team of workers during harvesting operations. The results obtained demonstrate the feasibility and the effectiveness of the methodology proposed.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0168169921006542; http://dx.doi.org/10.1016/j.compag.2021.106637; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85123206989&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0168169921006542; https://dx.doi.org/10.1016/j.compag.2021.106637
Elsevier BV
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