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Comparison of speed-accuracy tradeoff between linear and nonlinear filtering algorithms for myocontrol.
Borish, Cassie N; Feinman, Adam; Bertucco, Matteo; Ramsy, Natalie G; Sanger, Terence D.
Afiliación
  • Borish CN; Department of Biomedical Engineering, University of Southern California , Los Angeles, California.
  • Feinman A; Department of Biomedical Engineering, University of Southern California , Los Angeles, California.
  • Bertucco M; Department of Biomedical Engineering, University of Southern California , Los Angeles, California.
  • Ramsy NG; Department of Biomedical Engineering, University of Southern California , Los Angeles, California.
  • Sanger TD; Department of Biomedical Engineering, University of Southern California , Los Angeles, California.
J Neurophysiol ; 119(6): 2030-2035, 2018 06 01.
Article en En | MEDLINE | ID: mdl-29384451
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Electromiografía Tipo de estudio: Prognostic_studies Límite: Adult / Female / Humans / Male Idioma: En Revista: J Neurophysiol Año: 2018 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Electromiografía Tipo de estudio: Prognostic_studies Límite: Adult / Female / Humans / Male Idioma: En Revista: J Neurophysiol Año: 2018 Tipo del documento: Article Pais de publicación: Estados Unidos