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.
Worker safety in agriculture 4.0: A new approach for mapping operator's vibration risk through Machine Learning activity recognition
Venticinque M
2022
Abstract
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.File | Dimensione | Formato | |
---|---|---|---|
prod_468516-doc_188991.pdf
solo utenti autorizzati
Descrizione: Worker safety in agriculture 4.0: A new approach for mapping operator's vibration risk through Machine Learning activity recognition
Tipologia:
Versione Editoriale (PDF)
Licenza:
NON PUBBLICO - Accesso privato/ristretto
Dimensione
3.08 MB
Formato
Adobe PDF
|
3.08 MB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.