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
Link To Document :
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