Comparison of speed-accuracy tradeoff between linear and nonlinear filtering algorithms for myocontrol

Nonlinear Bayesian filtering of surface electromyography (EMG) can provide a stable output signal with little delay and the ability to change rapidly, making it a potential control input for prosthetic or communication devices. We hypothesized that myocontrol follows Fitts' Law, and that Bayesi...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of neurophysiology Jg. 119; H. 6; S. 2030
Hauptverfasser: Borish, Cassie N, Feinman, Adam, Bertucco, Matteo, Ramsy, Natalie G, Sanger, Terence D
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States 01.06.2018
Schlagworte:
Adult
Algorithms
Bayes Theorem
Electromyography - methods
Female
Humans
Male
Muscle, Skeletal - physiology
Fitts’ Law
surface electromyography
filtering
myocontrol
Bayesian
ISSN:1522-1598, 1522-1598
Online-Zugang:Weitere Angaben
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Nonlinear Bayesian filtering of surface electromyography (EMG) can provide a stable output signal with little delay and the ability to change rapidly, making it a potential control input for prosthetic or communication devices. We hypothesized that myocontrol follows Fitts' Law, and that Bayesian filtered EMG would improve movement times and success rates when compared with linearly filtered EMG. We tested the two filters using a Fitts' Law speed-accuracy paradigm in a one-muscle myocontrol task with EMG captured from the dominant first dorsal interosseous muscle. Cursor position in one dimension was proportional to EMG. Six indices of difficulty were tested, varying the target size and distance. We examined two performance measures: movement time (MT) and success rate. The filter had a significant effect on both MT and success. MT followed Fitts' Law and the speed-accuracy relationship exhibited a significantly higher channel capacity when using the Bayesian filter. Subjects seemed to be less cautious using the Bayesian filter due to its lower error rate and smoother control. These findings suggest that Bayesian filtering may be a useful component for myoelectrically controlled prosthetics or communication devices. NEW & NOTEWORTHY Whereas previous work has focused on assessing the Bayesian algorithm as a signal processing algorithm for EMG, this study assesses the use of the Bayesian algorithm for online EMG control. In other words, the subjects see the output of the filter and can adapt their own behavior to use the filter optimally as a tool. This study compares how subjects adapt EMG behavior using the Bayesian algorithm vs. a linear algorithm.
Bibliographie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1522-1598
1522-1598
DOI:10.1152/jn.00188.2017