Authors:
Simone Benatti
1
;
Elisabetta Farella
2
;
Emanuele Gruppioni
3
and
Luca Benini
4
Affiliations:
1
University of Bologna, Italy
;
2
University of Bologna and Fondazione Bruno Kessler, Italy
;
3
INAIL, Italy
;
4
University of Bologna and ETHZ, Italy
Keyword(s):
EMG, Patter Recognition, Multisession, Active Prosthesis.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Detection and Identification
;
Devices
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Human-Computer Interaction
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Real-Time Systems
;
Sensor Networks
;
Soft Computing
;
Wearable Sensors and Systems
Abstract:
Control of active hand prostheses is an open challenge. In fact, the advances in mechatronics made available
prosthetic hands with multiple active degrees of freedom; however the predominant control strategies are
still not natural for the user, enabling only few gestures, thus not exploiting the prosthesis potential. Pattern
recognition and machine learning techniques can be of great help when applied to surface electromyography
signals to offer a natural control based on the contraction of muscles corresponding to the real movements.
The implementation of such approach for an active prosthetic system offers many challenges related to the
reliability of data collected to train the classification algorithm. This paper focuses on these problems and
propose an implementation suitable for an embedded system.