George Coghill, Serge Gracovetsky and the Spinal Engine theory. For nearly 40 years, from the beginning of the 20th century until his death in , biologist. Buy The Spinal Engine by Serge Gracovetsky (Paperback) online at Lulu. Visit the Lulu Marketplace for product details, ratings, and reviews. Dr. Serge Gracovetsky offers with his “Spinal Engine” theory a compelling model that presents the holistic and integrated function of the whole.
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This is a revised gdacovetsky october of the original book I published in It has been reformatted in the standard 8. It is catalogued under the ISBN The interested reader can watch:.
The Critter Walk – Mathematical Analysis. Application To A Vertebrate Critter. The Critter With Short Stumps. The Problem Of Spinal Modelling. Limitations Of Mathematical Modelling. What Is A Model? The Posterior Ligamentous System. Construction Of The Model. The Need For Lordosis. Description Of The Control System. Selecting The Best Lordosis.
The Teaching Of Lifting. The Muscle Relaxation Phenomenon. The Spine As A Megamuscle. The Instantaneous Centre Of Rotation. The Centre Of Rotation.
The Range Of Trunk Motion. The Internal Abdominal Pressure. Modelling Of The Gdacovetsky Joint. Coupled Motion – The Functional Context. Classical Theories Of Locomotion. Combining Two Basic Building Blocks.
Combining Three Basic Building Blocks. General Approach To The Problem. Characterization Of The Axial Torque.
The Average And The Individual. The Thurston And Harris Experiment. Implications Of The Working Hypothesis. Comparison Of Locomotory Styles. Part 1 — Kinematics. The Baseline For Comparison. Comparison Of Kinematic Data. Application Of The Theory. The Determination Of Safe Loads.
George Coghill, Serge Gracovetsky and the Spinal Engine theory | Alexander Technique Running
The First Task – Anatomical Details. The Third Task – Mathematical Details.
The Fourth Task – System Details. Characteristic Curve Motion Algorithm.
Outline Of Optimization Algorithm. Example Of Clinical Use. Components Of The Intervertebral Joint.
thespinalengine – gracovetsky
To understand is to change, to go beyond oneself. This reading did not change me. Search for a Method: This is the age of the machine. Everywhere we look, living things are being described in terms of robotics. The bewildered physician is taken through differential equations, all kinds of Laplace and Fourier transforms, rational and irrational operators, and the like. At the end of the discussion, our perplexed man of medicine finds it difficult to contain his frustration and resentment towards such a mathematical cavalry.
Alas, slinal more cases than not, the problem solver loses sight of the initial problem. The study of the human spine did not escape this predicament: Somehow an idea will survive any kind of experimental annihilation if it is attractive enough. When we ask ourselves why this is so, we find there are no easy and clear-cut answers. The act of describing our environment is as old as mankind itself.
We use our senses to perform measurements and, by so doing, take the first step in the process of understanding. The early Babylonians were famous for their ability to track the position of the stars.
Mathematics became the tool of choice in their attempt to order the resulting gracovetskh.
In Search of Excellence – Body Physics – The Spinal Engine
Its sppinal was demonstrated by both Newton and Kepler, who reduced the vast number of available astronomical observations to a few simple relationships. This set of relationships is a model of celestial mechanics. Strict mathematical computations can be compared with the behavior of the real system and will ruthlessly expose any weakness inherent in the assumptions of the model. Conversely, such a model may also reveal any number of unforeseen mechanisms. The French mathematician Urban Leverrier assumed the existence of another planet and, using the model, went on to calculate its position.
His research led directly to the discovery of Neptune.
George Coghill, Serge Gracovetsky and the Spinal Engine theory
It should be noted that Leverrier chose to believe in the accuracy of the model, as it was consistent with the precision of the instruments of his time. The theory of relativity eventually led to the development of a better model. The best that can be achieved is the creation of a model that explains the data available at a particular point in time.
Some models survive for a long time whereas others do not. Regardless, a model is no substitute for common sense. Rather, enngine is a tool to be used to express hypotheses and rigorously evaluate their consequences. That is not to say that models are absolutely necessary. Galileo did not require a model in gracovtsky to realize that the earth was rotating. To be a success, a surgical procedure need not be determined by the solution of some fantastic equation.
Nevertheless, as humanity becomes more refined, the limitations and costs associated with a purely experimental approach become more pronounced. For instance, it is known that Neptune orbits the sun every years, even though the planet has not been observed for that long, simply because the model dngine up the acquisition of knowledge.
This is the reason for modeling biological systems. With experimental methods not always providing data sufficient to solve the many problems in medicine and biology, the theorist can join forces with the experimentalist. The added dimension of the theoretical modeling approach can rngine lead to a further understanding of a given problem and also potentially provide solutions not otherwise attainable. However, no one today would dare describe an entire animal in mathematical terms. System theory is a collection of overlapping areas such as control theory, information theory, automation theory and communication theory.
Although most of the basic concepts were formulated as early as the nineteenth century, it was the Second World War that provided the impetus necessary to express these concepts formally and apply them to the destruction of millions of human beings. In addition, the need to coordinate world-scale military operations propelled communication theory to new heights.
It was the race for the moon that triggered the massive research effort needed to ensure the proper guidance of spacecraft during orbit transfer. Flight conditions were almost ideal for the application of the precise laws of celestial mechanics, and so the control problem was readily expressible in mathematical form. Moreover, with the development of digital computers, it has been possible to model and calculate the trajectory of these space vehicles with extraordinary precision.
These ideal mathematical conditions provided fertile ground for the frantic development of system theory. The first lunar landing went far to convince many of its worth. The application of these new ideas to the medical profession was eased to a large extent by the parallels between engineering and biological control systems.
Control theory was used to analyze the physiology of respiration, thermoregulation and ionic balance. Entire subsystems were considered in the description of behavior and development.
For a while, success seemed to be just around the corner. Not everyone shared eengine enthusiasm. Such criticism is hard to swallow although some of it is, in fact, deserved. It could be further argued that modeling, as practiced, might not be the best approach. One possible reason might be that mathematical models are greatly dependent upon fast digital computers, whereas the human brain is notoriously slow at computation. This revelation is obvious whenever we attempt to model the reasoning process used to perform a diagnosis.
The representation of knowledge is best achieved with the heuristic rules we ggracovetsky follow.