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 L.;Picariello E.;Picariello F.;Tudosa I.;
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 %.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
