Authors:
Sergey Lobov
1
;
Nadia Krilova
1
;
Innokentiy Kastalskiy
1
;
Victor Kazantsev
1
and
Valeri Makarov
2
Affiliations:
1
Lobachevsky State University of Nizhny Novgorod, Russian Federation
;
2
Lobachevsky State University of Nizhny Novgorod, Instituto de Matemática Interdisciplinar, Applied Mathematics Dept. and Universidad Complutense de Madrid, Russian Federation
Keyword(s):
Electromyography, Human-Computer Interface, Pattern Classification, Artificial Neural Networks.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Manipulation
;
EMG Signal Processing and Applications
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Rehabilitation
;
Neurocomputing
;
NeuroSensing and Diagnosis
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
;
Users’ Perception and Experience on Technologies
Abstract:
Surface electromyographic (sEMG) signals represent a superposition of the motor unit action potentials that
can be recorded by electrodes placed on the skin. Here we explore the use of an easy wearable sEMG
bracelet for a remote interaction with a computer by means of hand gestures. We propose a human-computer
interface that allows simulating “mouse” clicks by separate gestures and provides proportional
control with two degrees of freedom for flexible movement of a cursor on a computer screen. We use an
artificial neural network (ANN) for processing sEMG signals and gesture recognition both for mouse clicks
and gradual cursor movements. At the beginning the ANN goes through an optimized supervised learning
using either rigid or fuzzy class separation. In both cases the learning is fast enough and requires neither
special measurement devices nor specific knowledge from the end-user. Thus, the approach enables
building of low-budget user-friendly sEMG solutions. The interface
was tested on twelve healthy subjects.
All of them were able to control the cursor and simulate mouse clicks. The collected data show that at the
beginning users may have difficulties that are reduced with the experience and the cursor movement by
hand gestures becomes smoother, similar to manipulations by a computer mouse.
(More)