Romanian Journal of Information Science and Technology (ROMJIST)

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ROMJIST is a publication of Romanian Academy,
Section for Information Science and Technology

Editor – in – Chief:

Academician Dan Dascalu

Secretariate (office):
Adriana Apostol
Adress for correspondence: romjist@nano-link.net (after 1st of January, 2019)

Editing of the printed version: Mihaela Marian (Publishing House of the Romanian Academy, Bucharest)

Technical editor
of the on-line version:
Lucian Milea (University POLITEHNICA of Bucharest)

Sponsors:
• National Institute for R & D
in Microtechnologies
(IMT Bucharest), www.imt.ro
• Association for Generic
and Industrial Technologies (ASTEGI), www.astegi.ro

ROMJIST Volume 20, No. 2, 2017, pp. 115-123, Paper no. 554/2017
 

Petru Lucian MILEA, Adrian BARBILIAN, Marius MOGA, Mark Edward POGĂRĂȘTEANU, Ana Maria OPROIU, Victor LAZO, Ioan Cristian STOICA
A new Method for Myoelectric Signal Acquisition: Preparing the Patients to Efficiently Use an Artificial Arm

ABSTRACT: This paper presents a new methodology in amputation surgery that is dedicated to patients who intend to use a myoelectric forearm prosthesis. The control signals for a myoelectric prosthesis are the surface EMG signals. In the case of an amputation stump, these signals are weak and difficult to use for effective control, due to the amputation methodology and the lack of activity after amputation. The proposed method aims the osteomyoplastic suturing of the muscles in circumferentially offset positions in order to obtain clear myoelectric signals, non-overlapping and with less correlated information. Following the surgery, a specific training is used, based on a biofeedback device, in order to help the patient to gradually obtain better control of stump’s muscles. The proposed methodology was evaluated experimentally at CMH, in a stump retouch surgery (on a patient who lost his hand almost 20 years ago), allowing comparison of the signals before surgery with ones after surgery and also with ones obtained after completing the post-surgery training. While going through these stages, a constant improvement of amplitude and independence of EMG signals collected from the stump was found. In the end, the patient was able to control an experimental model of artificial hand, by five different EMG signals, collected from stump’s muscles. The results appear to validate, in this case, the working hypotheses used as the base for the surgical method and biofeedback training.

KEYWORDS: amputation, CONM, artificial hand, prosthesis, myoelectric control

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