Enhancing Prosthetic Musculotendinous Proprioception Utilizing Multidisciplinary Artificially Intelligent Learning Approach

  • Dr. James Crowder, CAES APD
  • John N. Carbone, Computer Science and Engineering Dept., Southern Methodist University
  • Ryan A. Carbone, Biological Sciences, Baylor University

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. Recent advances in Artificial Intelligence are benefitting disciplines struggling with learning from rapid increasing data volume, velocity, and complexity. Research shows complexity reducing axiomatic design benefitting medical devices, but surprisingly not prosthetics. Therefore, we propose a multidisciplinary approach to potentially enhance prosthetic proprioception by combining AI adaptive learning, axiomatic design complexity reduction techniques applied to real-time classification of high volume prosthetic usage characteristics.

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