Enhancing Prosthetic Musculotendinous Proprioception utilizing Multidisciplinary Artificially Intelligent Learning Approach

Authors:
John N. Carbone, Electrical and Computer Engineering Department, Southern Methodist University
Ryan A. Carbone, Biological Sciences, Baylor University
Dr. James A. Crowder, Systems Fellow, Colorado Engineering Inc.

Abstract:
Historically, research shows analysis, characterization, and classification of complex heterogeneous non-linear systems and interactions have been difficult to accurately understand and effectively model. Advanced biophysical and biomechanical prosthesis research shows that development of patient specific physiologically meaningful musculotendinous proprioception would generate a marked impact on reflex control, fine volitional motor control, and overall user experience.

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Enhancing Prosthetic Musculotendinous Proprioception utilizing Multidisciplinary Artificially Intelligent Learning Approach (3 downloads)

 

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