Publications

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Abstract not provided.

The development of tools for complex dynamic security systems is not a straight forward engineering task but, rather, a scientific task where discovery of new scientific principles and math is necessary. For years, scientists have observed complex behavior but have had difficulty understanding it. Prominent examples include: insect colony organization, the stock market, molecular interactions, fractals, and emergent behavior. Engineering such systems will be an even greater challenge. This report explores four tools for engineered complex dynamic security systems: Partially Observable Markov Decision Process, Percolation Theory, Graph Theory, and Exergy/Entropy Theory. Additionally, enabling hardware technology for next generation security systems are described: a 100 node wireless sensor network, unmanned ground vehicle and unmanned aerial vehicle.

We developed an Augmented Musculature Device (AMD) that assists the movements of its wearer. It has direct application to aiding military and law enforcement personnel, the neurologically impaired, or those requiring any type of cybernetic assistance. The AMD consists of a collection of artificial muscles, each individually actuated, strategically placed along the surface of the human body. The actuators employed by the AMD are known as 'air muscles' and operate pneumatically. They are commercially available from several vendors and are relatively inexpensive. They have a remarkably high force-to-weight ratio--as high as 400:1 (as compared with 16:1 typical of DC motors). They are flexible and elastic, even when powered, making them ideal for interaction with humans.

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Evidence for the existence of discrete sub-movements underlying continuous human movement has motivated many attempts to "extract" them. Although they produce visually convincing results, all of the methodologies that have been employed are prone to produce spurious decompositions. Examples of potential failures are given. A branch-and-bound algorithm for submovement extraction, capable of global nonlinear minimization (and hence capable of avoiding spurious decompositions), is developed and demonstrated.

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Submovements are hypothesized building blocks of human movement, discrete ballistic movements of which more complex movements are composed. Using a novel algorithm, submovements were extracted from the point-to-point movements of 41 persons recovering from stroke. Analysis of the extracted submovements showed that, over the course of therapy, patients' submovements tended to increase in peak speed and duration. The number of submovements employed to produce a given movement decreased. The time between the peaks of adjacent submovements decreased for inpatients (those less than 1 month post-stroke), but not for outpatients (those greater than 12 months post-stroke) as a group. Submovements became more overlapped for all patients, but more markedly for inpatients. The strength and consistency with which it quantified patients' recovery indicates that analysis of submovement overlap might be a useful tool for measuring learning or other changes in motor behavior in future human movement studies.