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5.3.3 A Method for Analyzing Fundamental Kinesiological Motions of Human Body by Applying Interpretive Structural Modeling (ISM)
Author(s) -
Kayo Munehiro Michael,
Ohkami Yoshiaki
Publication year - 2009
Publication title -
incose international symposium
Language(s) - English
Resource type - Journals
ISSN - 2334-5837
DOI - 10.1002/j.2334-5837.2009.tb00987.x
Subject(s) - dimension (graph theory) , set (abstract data type) , motion (physics) , computer science , frame (networking) , rigid body , human body , homogeneous , motion capture , diagram , artificial intelligence , mathematics , physics , classical mechanics , pure mathematics , telecommunications , database , programming language , combinatorics
Summary The objective of this paper is to analyze interconnectedness of kinesiological motions by applying a well‐known systems modeling approach called the Interpretive Structural Modeling (ISM). This approach is applied to the previously proposed Somatic Balance Restoration Therapy (SBRT), developed by one of the authors and offers a safe way in correcting imbalances and distortions that occur in the human body. The human body is a complex system composed of over 200 bones and 630 muscles that comprise the musculoskeletal system, which works not only as the frame for the human body, but as the engine of human kinetic motion as well. As a practitioner, one of the authors developed a visually identifiable diagnosis system through many years of accumulated therapy data. Although the authors attempted to find a more methodical approach to justify the SBRT by inventing the unique “Motion Diagram”, a more systematic approach was required for a more efficient and homogeneous treatment. In this paper, the fundamental patterns produced by the SBRT are mapped into an n‐square matrix of dimension 70, based on the Fundamental Body Motions and analyzed by the ISM. This will be followed by graphical representations and classification of the body motions into several categories based on the degree of interaction and activeness. The results has revealed a priority of the fundamental motion patterns which helps find the most effective motions to be used for identifying imbalanced or distorted parts from the larger dimension set.