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Proceedings Paper

Intelligent synthesis of neuromusculoskeletal signals using fuzzy expert critics
Author(s): Jack M. Winters
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Paper Abstract

The problem of synthesizing large amounts of sensor data into a meaningful form represents one of the key challenges in making effective use of smart sensors/actuators that are distributed throughout a structure. This paper develops an engineering approach for addressing this problem, focusing on how large sets of neuromusculoskeletal measurements can be synthesized with approximate reasoning by experts and trained human observation to help extract and prioritize the most salient diagnostic findings, given a reasonably large set of strategic performance tasks. A key objective is to create an environment for intimate human- computer interaction, that optimally uses the capabilities of each. The best of two key conceptual frameworks are synthesized: initial design via rule-based fuzzy expert critic modules, followed by a gradual transition toward fuzzy neuro-optimization and neuro- classification modules. It is suggested that this provides a more reasonable approach not only for interactive near-real-time medical diagnosis assisted by a 'smart' computer, but also for developing the types of robust adaptive critics needed for advanced studies of principles underlying neuromotor control and skill acquisition.

Paper Details

Date Published: 30 May 1996
PDF: 13 pages
Proc. SPIE 2718, Smart Structures and Materials 1996: Smart Sensing, Processing, and Instrumentation, (30 May 1996); doi: 10.1117/12.240884
Show Author Affiliations
Jack M. Winters, Catholic Univ. of America (United States)

Published in SPIE Proceedings Vol. 2718:
Smart Structures and Materials 1996: Smart Sensing, Processing, and Instrumentation
Kent A. Murphy; Dryver R. Huston, Editor(s)

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