Development of a New Low-Cost EMG Monitoring System for the Classification of Finger Movement

Seguna, Clive; Brockdorff, Adrian von; Scerri, Jeremy; Scicluna, Kris

doi:10.1109/ngcas.2018.8572217

Cited by 7 publications

(2 citation statements)

References 9 publications

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“…Similarly, the average recall ranges from 84.8% to 100% for DA, 81.8% to 100% for SVM, and 63.6% to 84.8% for kNN, depending on the specific gesture. Therefore, the classification of the five different fingers reaches accuracies above 90%, comparable with the most current literature [15][16][17][18] . In particular, in one of these works the authors acquired sEMG by means of an 8-channels MYO band, and classified five finger movements and rest periods using several classifiers, including kNN and SVM, reaching a maximal accuracy of 95.8% 18 .…”

Section: Resultssupporting

confidence: 86%

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

Rosati

Cisotto

Sili

et al. 2021

Preprint

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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.

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Section: Resultssupporting

confidence: 86%

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

Rosati

Cisotto

Sili

et al. 2021

Preprint

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“…The control signal part of the prosthetic hand consists of smoothening, threshold adjustment, logic and control output sections (Asghari et al, 2007;Çelik et al, 2016;Park et al, 2003;Seguna et al, 2018;Sharmila et al, 2016;Kobayashi et al, 2010). The last part, the prosthetic hand mechanism, can be a physical/mechanical hand (Asghari et al, 2007;Çelik et al, 2016;Cinal et al, 2016;Hussain et al, 2018;Park et al, 2003;Seguna et al, 2018) or a virtual hand (Akgün et al, 2013;Çelik et al, 2016;Sharmila et al, 2016;Kobayashi et al, 2010). The control of the virtual and prosthetic hand is performed using a microcontroller or computer and data acquisition card, according to the design approach.…”

Section: Introductionmentioning

confidence: 99%

Hardware Design of Low Cost Myoelectric Controlled Prosthetic Hand For Engineering Laboratory

ÇELİK

Tepe

Eminoğlu

2022

Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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This study presents a low cost, two-channel, on-off type, constant speed myo-electric controlled prosthetic hand project as an educational tool for Biomedical/Control/Mechatronic Engineering. Surface Electromyogram (sEMG) signals were recorded from the muscles of flexor carpi ulnaris and extensor digitorium on the forearm. Signal conditioning of these signals is performed in the analog stages of the system, before passing it on to a microcontroller for further filtering and implementation of control. The control logic is simplified to represent the muscles as being active or inactive, resulting in a very simple on/off control based on the two signals. The on/off control signals are used to drive a myo-electric controlled prosthetic hand-powered by a hobby RC servo motor. A printed circuit board has been designed for the analog stages of the system, and a simple Arduino Microcontroller is used for the digital stages. Other commercial off-the-shelf components were used to keep the cost of the hardware and the software components as low as possible. This project was used as a teaching aid for the final year undergraduate students to demonstrate the use of simple myo-electric signal processing and control techniques.

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The Reusable Electrode of EMG Sensor for Capturing The Calf Muscle Activities

Analia

Sulistyo

Iglesias

et al. 2020

2020 3rd International Conference on Applied Engineering (ICAE)

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Development of a New Low-Cost EMG Monitoring System for the Classification of Finger Movement

Cited by 7 publications

References 9 publications

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

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

Hardware Design of Low Cost Myoelectric Controlled Prosthetic Hand For Engineering Laboratory

The Reusable Electrode of EMG Sensor for Capturing The Calf Muscle Activities

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