Individuals with certain types of central nervous system damage, such as stroke, have an asymmetric walking gait. Using a splitbelt treadmill, where each leg walks at a different speed, has been shown to help rehabilitate walking impaired individuals, but there is one distinct drawback; the corrected gait does not transfer well to walking over ground. To increase the gait transference to another environment, we designed and built a passive shoe that admits a motion similar to that felt when walking on a split-belt treadmill. Our gait enhancing mobile shoe (GEMS) alters the wearer's gait by causing one foot to move backward during the stance phase while walking over ground. No external power is required since the shoe mechanically converts the wearers downward and horizontal forces into a backward motion. This shoe would allow a patient to walk over ground while experiencing the same gait altering effects as felt on a split-belt treadmill, which should aid in transferring the corrected gait to walking in natural environments.
This paper describes a cost effective automated methodology to analyze launch video of artillery projectiles. Image processing and computer vision techniques are used to segment and classify the projectile shape in each video frame. Within minutes of being fired, the initial position, velocity, and orientation history of the projectile in three-dimensional space is determined at the gun site. An overview of several standard methods used by the Army to characterize pitching and yawing motion of projectiles is included, as well as a discussion of the limitations of these methods. Results from real artillery testing using the automated video analysis method are validated through comparisons to results measured using conventional techniques.
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