Activities per year
Abstract
Duchenne Muscular Dystrophy (DMD) is a progressive muscular disease. Active hand orthoses can greatly improve the quality of life of people with DMD. Surface Electromyography (sEMG) is commonly used for the control of active devices. The interfacing between the human and the sensor is regularly done by an adhesive skin interface (sticker). This can cause discomfort, especially during daily use. For forearm sEMG measurements, a sleeve design can solve this problem.
The design presented here aims to make sEMG more comfortable, yet functional for daily use. In order to achieve that, we designed a simple, low-cost sEMG sleeve using a commercial ankle brace. 6 equidistant cuts around the circumference of the sleeve were made in order for the sEMG sensors (Delsys Trigno, Delsys Inc.) to be placed. Those are held in place by a number of 3D printed plastic casings mounted with plastic snap buttons. The buttons are used to make the attachment of the casings fast and easy.
A preliminary evaluation of the sleeve has been carried out with 6 healthy subjects, using a library of 6 and 9 gestures and a simple artificial neural network (ANN) classifier. The performance was evaluated, in terms of classification time, training time and accuracy (offline) and selection time, completion time, completion rate and accuracy (online). The results show that the performance of the sleeve is not significantly different than the adhesive skin interface.
We conclude, that the EMG-sleeve is a better alternative than the current methods for sensor skin interfacing, while having similar performance during a classification task.
The design presented here aims to make sEMG more comfortable, yet functional for daily use. In order to achieve that, we designed a simple, low-cost sEMG sleeve using a commercial ankle brace. 6 equidistant cuts around the circumference of the sleeve were made in order for the sEMG sensors (Delsys Trigno, Delsys Inc.) to be placed. Those are held in place by a number of 3D printed plastic casings mounted with plastic snap buttons. The buttons are used to make the attachment of the casings fast and easy.
A preliminary evaluation of the sleeve has been carried out with 6 healthy subjects, using a library of 6 and 9 gestures and a simple artificial neural network (ANN) classifier. The performance was evaluated, in terms of classification time, training time and accuracy (offline) and selection time, completion time, completion rate and accuracy (online). The results show that the performance of the sleeve is not significantly different than the adhesive skin interface.
We conclude, that the EMG-sleeve is a better alternative than the current methods for sensor skin interfacing, while having similar performance during a classification task.
Original language | English |
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Publication status | Published - 27 Jan 2017 |
Event | 6th Dutch Bio-Medical Engineering Conference 2017 - Hotel Zuiderduin, Egmond aan Zee, Netherlands Duration: 26 Jan 2017 → 27 Jan 2017 Conference number: 6 http://www.bme2017.nl |
Conference
Conference | 6th Dutch Bio-Medical Engineering Conference 2017 |
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Abbreviated title | BME 2017 |
Country/Territory | Netherlands |
City | Egmond aan Zee |
Period | 26/01/17 → 27/01/17 |
Internet address |
Fingerprint
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6th Dutch Bio-Medical Engineering Conference 2017
Konstantinos Nizamis (Participant)
26 Jan 2017 → 27 Jan 2017Activity: Participating in or organising an event › Participating in a conference, workshop, ...
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Design and Validation of a Wireless EMG-Sleeve for Gesture Classification
Arno Stienen (Examiner), Konstantinos Nizamis (Examiner) & Andries de Boer (Examiner)
11 Jul 2016Activity: Examination
Research output
- 1 PhD Thesis - Research UT, graduation UT
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Hand Neuro-Motor Characterization and Motor Intention Decoding in Duchenne Muscular Dystrophy
Nizamis, K., 28 May 2019, 1st ed. Enschede: University of Twente. 230 p.Research output: Thesis › PhD Thesis - Research UT, graduation UT
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