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Wearable Cognitive Assistance (WCA) applications present a challenge tobenchmark and characterize due to their human-in-the-loop nature. Employinguser testing to optimize system parameters is generally not feasible, given thescope of the problem and the number of observations needed to detect small butimportant effects in controlled experiments. Considering the intendedmass-scale deployment of WCA applications in the future, there exists a needfor tools enabling human-independent benchmarking.  We present in this paper the first model for the complete end-to-endemulation of humans in WCA. We build this model through statistical analysis ofdata collected from previous work in this field, and demonstrate its utility bystudying application task durations. Compared to first-order approximations,our model shows a ~36% larger gap between step execution times at high systemimpairment versus low. We further introduce a novel framework for stochasticoptimization of resource consumption-responsiveness tradeoffs in WCA, and showthat by combining this framework with our realistic model of human behavior,significant reductions of up to 50% in number processed frame samples and 20%in energy consumption can be achieved with respect to the state-of-the-art.

Realistic Modeling of Human Timings for Wearable Cognitive Assistance