Enhancing the classification of hand movements through sEMG signal and non-iterative methods
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In movement classification through surface electromyography signal processing, the classification method must identify the user’s intention with satisfactory accuracy to promote an adequate biosignal interface. Traditionally, classical methods such as Support Vector Machines, Artificial Neural Networks, and Logistic Regression have been used to this end. Recently, Non-Iterative Methods based on Artificial Neural Networks have been revisited in the form of Random Vector Functional-Link Networks (RVFL) and its most recent derivation, the so-called Extreme Learning Machines (ELM). In this work, we evaluate the performance and potentialities of RVFL and ELM with Moore-Penrose (RVFL and ELM) and Ridge-Regression (R-ELM and R-RVFL) methods to classify 17 different upper-limb movements through surface electromyography (sEMG) signal processing. 341 different sets of tests involving sEMG channels and features were performed for each one of the 20 subjects (ten amputees and ten non-amputees) from NINAPro database. Overall, the NIM methods presented consistent advantages of accuracy rate and time processing when compared with most traditional classifiers. Once the best setup of inputs was defined, the R-ELM presented the best accuracy rate. While results up to 80% were already reported for NINAPro data using Deep Learning techniques which are blatantly costly on a computational perspective, there is no evaluation performed in embedded platforms using this database. Therefore, we conducted an embedded study case of the ELM method applied to a Raspberry Pi platform using: a) a timestamp segmentation and b) a sliding-window approach to emulate an online application of the technique. The first trial reached an average accuracy rate of 90.9% for the non-amputee and 63.1% for the amputee subjects. The second trial reached 77.2% of average accuracy for the non-amputee and 55.3% for the amputee subjects, pairing the results in literature, even with the limitations of an embedded platform.
KeywordssEMG Upper-limb Extreme learning machines Logistic regression Support vector machines Random vector functional-link
The authors would like to acknowledge the Brazilian Coordination for Improvement of Higher Level Personnel (CAPES) for the provision of the scholarship that made this work possible.
There is no funding source
Compliance with Ethical Standards
Conflict of interests statement
The authors disclose they do not have any relation with other people or organizations that could inappropriately influence (bias) their work, such as employers, stock ownership, paid expert testimony, patent applications/registrations, and grants or other funding.
This article does not contain any studies with human participants or animals performed by any of the authors.
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