DocumentCode
1135364
Title
Characterization of EMG Patterns From Proximal Arm Muscles During Object- and Orientation-Specific Grasps
Author
Martelloni, Chiara ; Carpaneto, Jacopo ; Micera, Silvestro
Author_Institution
Adv. Robot. Technol. & Syst. (ARTS) Lab., Scuola Superiore Sant´´Anna, Pisa, Italy
Volume
56
Issue
10
fYear
2009
Firstpage
2529
Lastpage
2536
Abstract
Reach-to-grasp tasks are composed of several actions that are more and more considered as simultaneously controlled by the central nervous system in a feedforward manner (at least for well-known activities). If this hypothesis is correct, during prehension tasks, the activity of proximal muscles (and not only of the distal ones used to control finger movements) is modulated according to the kind of object to be grasped and its position. This means that different objects could be identified by processing the electromyographic (EMG) signals recorded from proximal muscles. In this paper, specific experiments have been carried out to support this hypothesis in able-bodied subjects. The results achieved seem to confirm this possibility by showing that the activation of proximal muscles can be statistically different for different grip types. This finding supports the hypothesis that proximal and distal muscles are simultaneously controlled during reaching and grasping. Moreover, this kind of information could allow the development of an EMG-based control strategy based on the natural muscular activities selected by the central nervous system.
Keywords
electromyography; feedforward; medical control systems; medical signal processing; neurophysiology; statistical analysis; EMG pattern characterization; EMG signal processing; EMG-based control strategy; central nervous system; electromyography; feedforward manner; orientation-specific grasps; proximal arm muscles; statistical analysis; Central nervous system; Centralized control; Control systems; Electromyography; Fingers; Muscles; Permission; Robotics and automation; Robots; Signal processing; Subspace constraints; Biorobotics; electromyographic (EMG) signals; hand prosthesis; reach-to-grasp; upper arm; Adult; Analysis of Variance; Arm; Electromyography; Female; Hand Strength; Humans; Male; Muscle, Skeletal; Signal Processing, Computer-Assisted;
fLanguage
English
Journal_Title
Biomedical Engineering, IEEE Transactions on
Publisher
ieee
ISSN
0018-9294
Type
jour
DOI
10.1109/TBME.2009.2026470
Filename
5165084
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