The basics of performance analysis

Performance, or gait, analysis is one way of looking at the e-Patterns derived from Adaptive FIT. Because these patterns are direct representations of a person's stride characteristics; they can be analyzed on different levels to produce pertinent data concerning their gait. A First Person Network provides two distinct developments in gait analysis: 1) data is drawn from the internal physiological movements of a stride as opposed to current observational methods, and 2) the true ability to conduct gait analysis outside of the lab setting, allowing for a higher level of robustness.

Gait analysis is centered on the quantification and interpretation of animal/human locomotion. In human movement, it is the study of the positions, angles, velocities, and accelerations of body segments and joints during motion. Study of the gait can reflect on a variety of compensations for underlying pathologies (diseases), their causes and prescriptions. The practice of analysis allows these diagnoses to be made, as well as permitting future developments in rehabilitation engineering. Aside from clinical applications, gait analysis is widely used in professional sports training, referred to as performance analysis, to optimize and improve the athletic performance. Coupled to this are the many other areas in the general health and orthopedic fields.

Our first improvement on the current method is in the straightforward, yet concentrated, data that can now be generated. How we derive this higher level of data is due, in part, to the clean nature of what is generated. Instead of basing the calculations on anatomical landmarks, we now have data that is taken directly from a foot's movements within the shoe, right down to the micro level (no sensor of any kind being used). A pattern recognition system will then analyze this data, and ultimately provide a higher level of performance analysis, one that highlights the intrinsic characteristics of the person's gait. The ultimate goal in designing such a system is to achieve the best possible performance, regardless of the task. For optimal performance, the system must use and apply a judicious number of cues and combine them in meaningful and useful ways. Different 'classifiers' transform the data generated in order to make them comparable. Some commonly used transformations include linear, logarithmic, logistical and exponential. These are only a few of the ways that the data arrived at can be understood and ultimately applied. The intent with analysis is to achieve the cleanest form of data, zeroing in on specific patterns, and discarding the multiple, and sometimes confusing, variables that are often associated with a gait cycle being looked-at -- due to the filming of it. With the use of Adaptive FIT, we will have that opportunity to work with properties unique to an individual, borrowing them from the situation and place the person/patient/client is operating in. At play or at work, on the field or in the gym -- wherever the shoes perform there is data created that can be useful.

What works in one environment ...
Different models of gait analysis will produce different outcomes, or applications. There is a growing interest in the analytical methods for producing the characteristics of a stride, but the tools available are few: the pedometer (step counter), the accelerometer (a transducer which converts mechanical motion into an electrical signal that is proportional to the acceleration value of the motion) and the lab equipment. In the lab, of course, there are force platforms and camera work, with markers placed on the client/patient, but none of this is practical when the person is understood-best in real situations, whether playing on the court or out in the field.

The inability to study the gait outside of the lab is a major compromise that plagues the field. To date, it has not been possible to truly monitor a person's gait without a well-planned walkway. Commonly, there are several (five or more) video cameras that are placed around it, each of them linked to a computer. And of course the client is kept aware of the force platform they will have to hit. Now, with a First Person Network, a person's gait can be analyzed independent of the outside environment. Currently, the client/patient has markers applied to various anatomical landmarks, using gyros too if required, which enables the computer to calculate trajectories in three dimensions. When fed to programs, models of a graphic nature are returned, which will, it is hoped, show an underlying meaning to the motion of bones. The aim for it all is a breakdown of movements in each of the joints. Operations like these are not needed where Adaptive FIT is used. When the information on an individual's stride is gained, the parameters have been vastly simplified. Even the podiatrist in a small office can have a superior level of data by analyzing the movements of their patient or client -- once connected to their footware. When the subject is in footware and the wireless capability has been activated the footware is then capable of informing the professional, and, whatever needs correcting, there is likely enough indicators for what requires a prescription right-there in the upload. One of the likely-tools for this would be a pro version of a consumer program that is, ordinarily, designed first for a shoe store's professional, or student, fitter.

The field of performance/gait analysis is actually quite large, but each of the interested parties is often compiling the information in their own, separate, databases. There are a variety of reasons, some are philosophical, but most are simple differences in procedure, where one lab's interpretations are not like another's. What it leads to is a discipline with many researchers in it hardly aware of each other's work -- most of it due to the problem of translation. The complexity in the measurements that come from one lab's personnel-habits, when running their equipment, are not similar enough to another's lab's methodology. Each party, seemingly, is communicating in a different language. Roots are the same ... but there are not enough 'markers' for wise interpretations. In our scenario, the starting point is pretty basic. There are simple pieces of equipment, shoes and a handheld, that researchers and practitioners can focus their attention on. Due to the broad palate of environments they might test their client/patients in and range of programs that can output the data, there will be an ever-growing supply new information. If the change to footware expands, the range in expert conversations can widen even further. When the language is common, the need for interpretation is lessened.

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