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

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 to control prosthetics and devices in real-life settings. The aim of our work was to develop 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 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 they were repeatedly performing 12 standard finger movements (6 extension and 6 flexion). 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.

Feature Stability and Setup Minimization for EEG-EMG-Enabled Monitoring Systems

“…In this paper, we focus on multi-modal systems that are able to simultaneously acquire electroencephalography (EEG) and electromyography (EMG) data during movement. Despite their higher explanatory value, these sensing modalities share a lower usability and a higher obtrusiveness: in fact, to capture significant patterns associated to motor recovery, a number of sensors are generally used to acquire brain signals from all over the scalp and from several muscles along the moving limb [15,16,17,18,19]. To ensure the sustainability of m-health systems based on electrophysiological measurements, one important challenge is to extract relevant pieces of EEG and EMG information to correlate with the subject's conditions and behavior.…”

“…On the other hand, commercial low-cost EEG and EMG devices are driven by comfort to place the electrodes in convenient places (e.g., the forehead for EEG), rather than in more meaningful but inconvenient locations (e.g., the centre of the scalp, or the hand). However, with the recent effort on wearables and new sensing technologies (e.g., flexible, printable and graphene-based electrodes [16,20,21,22]), new solutions are expected to trade-off comfort and proper motor control monitoring.…”

Feature stability and setup minimization for EEG-EMG-enabled monitoring systems

“…In this paper, we focus on multi-modal systems that are able to simultaneously acquire electroencephalography (EEG) and electromyography (EMG) data during movement. Despite their higher explanatory value, these sensing modalities share a lower usability and a higher obtrusiveness: in fact, to capture significant patterns associated to motor recovery, a number of sensors are generally used to acquire brain signals from all over the scalp and from several muscles along the moving limb [15][16][17][18][19].…”

“…On the other hand, commercial low-cost EEG and EMG devices are driven by comfort to place the electrodes in convenient places (e.g., the forehead for EEG), rather than in more meaningful but inconvenient locations (e.g., the center of the scalp, or the hand). However, with the recent effort on wearables and new sensing technologies (e.g., flexible, printable and graphene-based electrodes [16,[20][21][22]), new solutions are expected to trade-off comfort and proper motor control monitoring.…”