As technology becomes integrated into everyday life, the relationship between the human and machine must be kept in a balance. Not only does the technology have to perform its intended function, but it must do so in accordance with the dynamic parameters of the complex use environment, in a way that does not encumber the user either physically or mentally. The dissertation explores the challenges of such a scenario through an analysis of three core facets: Affordances, or the user interface cues that allow an intuitive means of operating a device; the aspect of simplicity and its effect on the use factors of the technology (while something might seem to be simple, it may actually be complex to use); and cognitive load in terms of user impact, as a result of utilizing the technology. These illustrate the challenges inherent in understanding the facets fundamental to human- machine interaction in a complex environment. The three factors will be explored first through an historical analysis of the work in each aspect. The theories at hand will be utilized to inform the creation of iterative generations of wearable sensory systems. By utilizing the systems in the field of dance, the theoretical aspects of the core factors can be gauged by actual implementation. Each implementation will be compared side by side to gauge differences in terms of movement dynamics and efficacy of execution. The study will reveal that the three core factors of affordance, simplicity and cognitive load combine synergistically to create a foundational methodology for seamless integration of technology in a complex use scenario. Furthermore, the facets of interoperability between devices in a complex use environment will be illustrated through the theory of intra-interactivity. Finally, the dissertation will illustrate the advantages of passive user interaction and its necessity in relation to the factor of cognitive load. / text

This thesis is concerned with multigraphs and their complexity which is defined and quantified by the distribution of edge multiplicities. Two random multigraph models are considered.  The first model is random stub matching (RSM) where the edges are formed by randomly coupling pairs of stubs according to a fixed stub multiplicity sequence. The second model is obtained by independent edge assignments (IEA) according to a common probability distribution over the edge sites. Two different methods for obtaining an approximate IEA model from an RSM model are also presented. In Paper I, multigraphs are analyzed with respect to structure and complexity by using entropy and joint information. The main results include formulae for numbers of graphs of different kinds and their complexity. The local and global structure of multigraphs under RSM are analyzed in Paper II. The distribution of multigraphs under RSM is shown to depend on a single complexity statistic. The distributions under RSM and IEA are used for calculations of moments and entropies, and for comparisons by information divergence. The main results include new formulae for local edge probabilities and probability approximation for simplicity of an RSM multigraph. In Paper III, statistical tests of a simple or composite IEA hypothesis are performed using goodness-of-fit measures. The results indicate that even for very small number of edges, the null distributions of the test statistics under IEA have distributions that are  well approximated by their asymptotic χ2-distributions. Paper IV contains the multigraph algorithms that are used for numerical calculations in Papers I-III.