sEMG-based hand gesture recognition with deep learning

Zanghieri, Marcello (2019) sEMG-based hand gesture recognition with deep learning. [Laurea magistrale], Università di Bologna, Corso di Studio in Fisica [LM-DM270]
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Hand gesture recognition based on surface electromyographic (sEMG) signals is a promising approach for the development of Human-Machine Interfaces (HMIs) with a natural control, such as intuitive robot interfaces or poly-articulated prostheses. However, real-world applications are limited by reliability problems due to motion artifacts, postural and temporal variability, and sensor re-positioning. This master thesis is the first application of deep learning on the Unibo-INAIL dataset, the first public sEMG dataset exploring the variability between subjects, sessions and arm postures, by collecting data over 8 sessions of each of 7 able-bodied subjects executing 6 hand gestures in 4 arm postures. In the most recent studies, the variability is addressed with training strategies based on training set composition, which improve inter-posture and inter-day generalization of classical (i.e. non-deep) machine learning classifiers, among which the RBF-kernel SVM yields the highest accuracy. The deep architecture realized in this work is a 1d-CNN implemented in Pytorch, inspired by a 2d-CNN reported to perform well on other public benchmark databases. On this 1d-CNN, various training strategies based on training set composition were implemented and tested. Multi-session training proves to yield higher inter-session validation accuracies than single-session training. Two-posture training proves to be the best postural training (proving the benefit of training on more than one posture), and yields 81.2% inter-posture test accuracy. Five-day training proves to be the best multi-day training, and yields 75.9% inter-day test accuracy. All results are close to the baseline. Moreover, the results of multi-day trainings highlight the phenomenon of user adaptation, indicating that training should also prioritize recent data. Though not better than the baseline, the achieved classification accuracies rightfully place the 1d-CNN among the candidates for further research.

Tipologia del documento
Tesi di laurea (Laurea magistrale)
Autore della tesi
Zanghieri, Marcello
Relatore della tesi
Correlatore della tesi
Corso di studio
Curriculum E: Fisica applicata
Ordinamento Cds
Parole chiave
Electromyography,EMG,Surface electromyography,sEMG,Deep Learning,DL,Convolutional Neural Networks,CNN,Unibo-INAIL,Unibo-INAIL dataset,Machine Learning,ML,Python,PyThorch,Pattern Recognition,PR,Hand gesture recognition,Gesture recognition,Prosthetics,Biomedical signals,Automatic differentiation,Activation Potential,AP,Motor Unit Activation Potential,MUAP,Human-Machine Interface,HMI,Human-Computer Interface,HCI
Data di discussione della Tesi
22 Marzo 2019

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