Myoelectric Arm Using Artificial Neural Networks to Reduce Cognitive Load of the User

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21260%2F17%3A00233584" target="_blank" >RIV/68407700:21260/17:00233584 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21460/17:00233584

Result on the web

<a href="http://link.springer.com/article/10.1007%2Fs00521-015-2074-x" target="_blank" >http://link.springer.com/article/10.1007%2Fs00521-015-2074-x</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00521-015-2074-x" target="_blank" >10.1007/s00521-015-2074-x</a>

Result language

angličtina

Original language name

Myoelectric Arm Using Artificial Neural Networks to Reduce Cognitive Load of the User

Original language description

Today’s multiple degree-of-freedom myoelectric prosthesis relies only on direct control by the processed electromyographic signal. However, it is difficult for the wearer to learn unnatural muscle contractions in order to wield more than three DoFs of the arm. This makes it almost impossible to use more complex prostheses with a larger number of actuators. Methods based on sensor–actuator loop and artificial intelligence may reduce cognitive load of the user by removing low level control, and an intelligent control system would make it needless to micromanage every action. For this purpose, sensor system for body segments motion capture was developed, as well as sensor system for prosthetic limb’s environment motion capture. Neural networks were designed to process data from the sensor systems. For the identification of the knee angle, orientation trackers were used. Neural network predictor of arm positions predicts the shoulder angle using the information about movement of the lower limb. In the case of the periodic/cyclic movements of the legs, such as walking, the control unit uses typical movement patterns of the healthy upper limb. Ultrasonic range sensors are used to create 3D map of objects in the environment around the arm. Neural network predictor of object positions predicts collisions. If the potential collisions are identified, the control unit stops arm movement. The new methods were verified by MATLAB and are designed as a part of assistive technology for disabled people and are to be understood as an original contribution to the investigation of new prosthesis control units and international debate on the design of new myoelectric prostheses.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20201 - Electrical and electronic engineering

Project

<a href="/en/project/VG20102015002" target="_blank" >VG20102015002: Flexiguard</a><br>

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Neural Computing and Applications

ISSN

0941-0643

e-ISSN

1433-3058

Volume of the periodical

28

Issue of the periodical within the volume

2

Country of publishing house

GB - UNITED KINGDOM

Number of pages

9

Pages from-to

419-427

UT code for WoS article

000393051200017

EID of the result in the Scopus database

2-s2.0-84945557035