Simultaneous sEMG Classification of Hand/Wrist Gestures and Forces - Leone, Francesca; Gentile, Cosimo; Ciancio, Anna Lisa; Gruppioni, Emanuele; Davalli, Angelo; Sacchetti, Rinaldo; Guglielmelli, Eugenio; Zollo, Loredana (Frontiers in Neurorobotics, 2019)
Surface electromyography (sEMG) signals represent a promising approach for decoding the motor intention of amputees to control a multifunctional prosthetic hand in a non-invasive way. Several approaches based on proportional amplitude methods or simple thresholds on sEMG signals have been proposed to control a single degree of freedom at time, without the possibility of increasing the number of controllable multiple DoFs in a natural manner. Myoelectric control based on PR techniques have been introduced to add multiple DoFs by keeping low the number of electrodes and allowing the discrimination of different muscular patterns for each class of motion. However, the use of PR algorithms to simultaneously decode both gestures and forces has never been studied deeply. This paper introduces a hierarchical classification approach with the aim to \textcolor{blue}{assess} the desired hand/wrist gestures, as well as the desired force levels to exert during grasping tasks. A Finite State Machine was introduced to manage and coordinate three classifiers based on the Non-Linear Logistic Regression algorithm. The classification architecture was evaluated across $31$ healthy subjects. The “hand/wrist gestures classifier”, introduced for the discrimination of $7$ hand/wrist gestures, presented a mean classification accuracy of $98.78 %$, while the “Spherical and Tip force classifier", created for the identification of three force levels, reached an average accuracy of $98.80 %$ and $96.09 %$, respectively. \textcolor{red}{These results were confirmed by Linear Discriminant Analysis (LDA) with time domain features extraction, considered as ground truth for the final validation of the performed analysis. A Wilcoxon Signed-Rank test was carried out for the statistical analysis of comparison between NLR and LDA and statistical significance was considered at $p<0.05$. The comparative analysis reports not statistically significant differences in terms of F1Score performance between NLR and LDA. Thus, this study reveals that the use of non linear classification algorithm, as NLR, is as much suitable as the benchmark LDA classifier for implementing an EMG pattern recognition system, able both to decode hand/wrist gestures and to associate different performed force levels to grasping actions.}