Handwritten Digits Recognition From sEMG: Electrodes Location and Feature Selection

• Published in: IEEE access Vol. 11; pp. 58006 - 58015
• Main Authors: Tigrini, Andrea, Verdini, Federica, Scattolini, Mara, Barbarossa, Federico, Burattini, Laura, Morettini, Micaela, Fioretti, Sandro, Mengarelli, Alessandro
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Cognitive tasks; Digits; Discriminant analysis; Electrodes; Electromyography; Feature extraction; Forearm; Gesture recognition; Hand (anatomy); Handwriting; Handwriting recognition; Human computer interaction; human-machine interface; Man-machine interfaces; Myoelectric control; myoelectric pattern recognition; Myoelectricity; Pattern recognition; Rehabilitation; Support vector machines; Task analysis; Wrist
• DOI: 10.1109/ACCESS.2023.3279735
• ISSN: 2169-3536

Despite hand gesture recognition is a widely investigated field, the design of myoelectric architectures for detecting finer motor task, like the handwriting, is less studied. However, writing tasks involving cognitive loads represent an important aspect toward the generalization of myoelectric-based human-machine interfaces (HMI), and also for many rehabilitative tasks. In this study, the handwriting recognition of the ten digits was faced under the myoelectric control perspective, considering the probes setup and the feature extraction step. Time and frequency domain features were extracted from surface electromyography (sEMG) signals of 11 subjects who wrote the ten digits following a standardized template and 8 sEMG probes were equally distributed between forearm and wrist. Feature class separability was investigated and an aggregated feature set was built to train pattern recognition architectures, i.e. linear discriminant analysis (LDA) and quadratic support vector machine (QSVM). Also, four reduced probes setups were investigated. LDA and QSVM showed mean accuracy of about 97%, with all the forearm and wrist sEMG information. A significant reduction of performances was observed considering the wrist or the forearm only (<inline-formula> <tex-math notation="LaTeX">\leq 92 </tex-math></inline-formula>%) and when LDA and QSVM were trained with two electrodes information (<inline-formula> <tex-math notation="LaTeX">\leq 90 </tex-math></inline-formula>%). For the reliable classification performances in a motor task involving high cognitive demands, like the handwriting, it is required the use of probes fully covering forearm and wrist. Outcomes support the methodological transfer from myoelectric hand gesture to the handwriting recognition, which represents a key aspect in the development of new HMI for rehabilitation tasks.