The use of surface electromyography (sEMG) is rapidly spreading, from robotic prostheses and muscle computer interfaces to rehabilitation devices controlled by residual muscular activities. In this context, sEMG-based gesture recognition plays an enabling role in controlling prosthetics and devices in real-life settings. Our work aimed at developing a low-cost, print-and-play platform to acquire and analyse sEMG signals that can be arranged in a fully customized way, depending on the application and the users’ needs. We produced 8-channel sEMG matrices to measure the muscular activity of the forearm using innovative nanoparticle-based inks to print the sensors embedded into each matrix using a commercial inkjet printer. Then, we acquired the multi-channel sEMG data from 12 participants while repeatedly performing twelve standard finger movements (six extensions and six flexions). Our results showed that inkjet printing-based sEMG signals ensured significant similarity values across repetitions in every participant, a large enough difference between movements (dissimilarity index above 0.2), and an overall classification accuracy of 93–95% for flexion and extension, respectively.

Rosati, G., Cisotto, G., Sili, D., Compagnucci, L., De Giorgi, C., Pavone, E., et al. (2021). Inkjet-printed fully customizable and low-cost electrodes matrix for gesture recognition. SCIENTIFIC REPORTS, 11(1) [10.1038/s41598-021-94526-5].

Inkjet-printed fully customizable and low-cost electrodes matrix for gesture recognition

Cisotto G.
Co-primo
;
2021

Abstract

The use of surface electromyography (sEMG) is rapidly spreading, from robotic prostheses and muscle computer interfaces to rehabilitation devices controlled by residual muscular activities. In this context, sEMG-based gesture recognition plays an enabling role in controlling prosthetics and devices in real-life settings. Our work aimed at developing a low-cost, print-and-play platform to acquire and analyse sEMG signals that can be arranged in a fully customized way, depending on the application and the users’ needs. We produced 8-channel sEMG matrices to measure the muscular activity of the forearm using innovative nanoparticle-based inks to print the sensors embedded into each matrix using a commercial inkjet printer. Then, we acquired the multi-channel sEMG data from 12 participants while repeatedly performing twelve standard finger movements (six extensions and six flexions). Our results showed that inkjet printing-based sEMG signals ensured significant similarity values across repetitions in every participant, a large enough difference between movements (dissimilarity index above 0.2), and an overall classification accuracy of 93–95% for flexion and extension, respectively.
Articolo in rivista - Articolo scientifico
Surface; Biosensor; Signals;
English
Rosati, G., Cisotto, G., Sili, D., Compagnucci, L., De Giorgi, C., Pavone, E., et al. (2021). Inkjet-printed fully customizable and low-cost electrodes matrix for gesture recognition. SCIENTIFIC REPORTS, 11(1) [10.1038/s41598-021-94526-5].
Rosati, G; Cisotto, G; Sili, D; Compagnucci, L; De Giorgi, C; Pavone, E; Paccagnella, A; Betti, V
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/366719
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