The Mouck Method for Gait Analysis & Path Deviation Study

Part V - Applications and Acknowledgements

1. Human Tracking
2. Clinical Gait Analysis
   1. 2D Classification System
   2. Time Dependant Values
   3. Application to Real Data
   4. Correction Mechanisms
   5. First Real Application
   6. Various Points
3. Quadrupeds
4. Acknowledgements

A. Human Tracking

When a person walks in a desert-like scene, the lack of visual direction cues makes it more likely that natural left/right differences in walking pattern will cause the overall direction of travel to show regular, minor changes. The path often shows as a large arc.

Since a person wandering even a short distance can lead to relatively large direction changes, the analysis of how people walk while wandering could develop into a much more significant factor in the human tracking arena, now that direction changes over a single step are better understood.

Every possible 2D path characteristic can be recreated using the Step Model, so it allows for the creation of correlation tables to relate changes in one or more parameters to variations in field available measurements, like step length (line), or distance and direction from a standard point or line(s), etc.

This method will facilitate the creation of a system to predict wander paths for lost people (path deviation), given a few standard physical measurements and a current sample footfall pattern. Since a person walking is a vector system, it's perfect for a computer program.

The study of limb dominance, and its effect on walking pattern, would be a large part of this application. But, how parameters change with external stresses, such as fatigue or carrying something heavy, would be the most substantial area, as well as correction mechanisms (see below).

Co-operation with 3D gait analysts should provide much of the data required to investigate path deviation, and there could be a lot done with what’s already on file.

Unfortunately, the 4 points of gait (and foot-line), at the proper time point of heel-contact, are not measurable from a real footprint pattern, since they’re in the air. But, that doesn’t mean the system can’t be applied. At least now we know the correct body segments and joints which are involved in distance and direction when walking, and have an idea of their individual variations.

And, the direction changes over a step may be large enough were estimated positions of heel-point pelvic joints are adequate. Since the parameters are fundamentally related due to the physical connection of the 4 points of gait, muscular and/or skeletal limitations may make it possible to limit the possibilities for each step, perhaps with error estimations.

This method is important to human tracking for 2 main reasons. It allows the production of realistic footfall plots, in order to study distance and direction relationships between footfalls which couldn’t be studied without it, and it shows how the different body segments and joints contribute, via the parameters, to the observed footfall pattern.

This means that tables can be created which show how each parameter affects the position of the footprint, so variations for each can be correlated in order to produce a “most likely” Step Model for each step. A part of this is lab study of how people walk, in order define limits of motion and show how each parameter is related to the others.

The relationship of pelvic stretch and straddle-line doesn’t have to be studied, since they’re the sides of a right triangle with the pelvis-line as hypotenuse, but all others have to be determined. The step-out-line and rear-leg-line, for eg., have a relationship through ground contact and the pelvis-line. How one changes wrt the other should provide greater insight into basic walking controls, and help determine what simplifications can be applied, for analysis.

Application to SAR should involve 2 main areas, one of which requires real data input.

The first area involves 2 computer programs and a database. The initial program would be for creating Step Models from specific input, taking relevant measurements on the Model, using the Model (with any others) to create footfall plots, taking relevant measurements on the plot, showing the acquired data via figures and graphs, and tabulating the data in a standard format.

The stage 2 program would use the previous for the base, but be enhanced to facilitate SAR in the field (see Foreword, Application to SAR - One Plausible Scenario). This would require the input of physical data and it’s use to estimate Step Models, the plotting of wander paths, the evaluation of terrain if such data is available (popular areas could have a detailed topographical map), the calculation and display of potential error path regions, allow input of field determined factors like rockiness or moisture, create logs of input and all analyses, etc.

The world database would be to tabulate real data.

The second main area of application is the study of normal walking patterns, as well as changes in the parameters due to specific stresses (such as wearing a heavy backpack) and limb dominance. This would require the definition of a standard method to determine limb dominance (which there currently isn’t, I believe), as well as the analysis of parameter data. This requires technical data input, such as 3D marker tracks.

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