  Hand gesture recognition based on surface electromyographic (sEMG) signals is
a promising approach for developing 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 artefacts, 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. Recent studies address variability with strategies based on training
set composition, which improve inter-posture and inter-day generalization of
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
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 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 the best multi-day training, yielding 75.9%
inter-day test accuracy. All results are close to the baseline. Moreover, the
results of multi-day training 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.
