In this paper, a fatigue recognition system, based on a Machine Learning (ML) algorithm is presented. A wearable device is used to acquire the sEMG signals on a subject performing complex tasks, using tools, and components. Different features are utilized in order to train the ML classifier, namely: amplitude features, frequency features, and used tools and components. In order to verify the effectiveness of the proposed system, various features have been chosen to train the classifier, i.e., an ensemble bagging decision tree, and a preliminary experimental assessment is presented, where the F1-score is calculated. The results show that through the use of all the proposed features and with an optimization phase of the classifier, it is possible to reach an F1-score of 77.7 %.

Measurement System for Operator 5.0: a Learning Fatigue Recognition based on sEMG Signals / De Vito, Luca; Picariello, Enrico; Picariello, Francesco; Tudosa, Ioan; Sbaragli, Andrea; Papini, Gastone Pietro Rosati; Pilati, Francesco. - (2023), pp. -6. (Intervento presentato al convegno MeMeA 2023 tenutosi a Jeju, Republic of Korea nel 14th-16th June 2023) [10.1109/MeMeA57477.2023.10171933].

Measurement System for Operator 5.0: a Learning Fatigue Recognition based on sEMG Signals

Sbaragli, Andrea;Papini, Gastone Pietro Rosati
Penultimo
;
Pilati, Francesco
Ultimo
2023-01-01

Abstract

In this paper, a fatigue recognition system, based on a Machine Learning (ML) algorithm is presented. A wearable device is used to acquire the sEMG signals on a subject performing complex tasks, using tools, and components. Different features are utilized in order to train the ML classifier, namely: amplitude features, frequency features, and used tools and components. In order to verify the effectiveness of the proposed system, various features have been chosen to train the classifier, i.e., an ensemble bagging decision tree, and a preliminary experimental assessment is presented, where the F1-score is calculated. The results show that through the use of all the proposed features and with an optimization phase of the classifier, it is possible to reach an F1-score of 77.7 %.
2023
2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings
Piscataway, NJ, USA
IEEE
978-1-6654-9384-0
De Vito, Luca; Picariello, Enrico; Picariello, Francesco; Tudosa, Ioan; Sbaragli, Andrea; Papini, Gastone Pietro Rosati; Pilati, Francesco
Measurement System for Operator 5.0: a Learning Fatigue Recognition based on sEMG Signals / De Vito, Luca; Picariello, Enrico; Picariello, Francesco; Tudosa, Ioan; Sbaragli, Andrea; Papini, Gastone Pietro Rosati; Pilati, Francesco. - (2023), pp. -6. (Intervento presentato al convegno MeMeA 2023 tenutosi a Jeju, Republic of Korea nel 14th-16th June 2023) [10.1109/MeMeA57477.2023.10171933].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/399141
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