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For smart home researchers, it is essential to test activity recognition algorithms with various sets of sensory data. However, diverse sensory datasets are not always available due to several constraints, including limited budgets. Consequently, smart home simulators have recently grown in importance. However, there is still a need for realistic synthetic(More)
Activity recognition research relies heavily on test data to verify the modeling technique and the performance of the activity recognition algorithm. But data from real deployments are expensive and time consuming to obtain. And even if cost is not an issue, regulatory limitations on the use of human subjects prohibit the collection of extensive datasets(More)
In this paper we propose a two-phase methodology for designing datasets that can be used to test and evaluate activity recognition algorithms. The trade offs between time, cost and recognition performance is one challenge. The effectiveness of a dataset, which contrasts the incremental performance gain with the increase in time, efforts, and number and cost(More)
As demands for human activity recognition technology increase, simulation of human activities for providing datasets and testing purposes is becoming increasingly important. Traditional simulation, however, is based on an event-driven approach, which focuses on single sensor events and models within a single human activity. It requires detailed description(More)
Experimental investigations were conducted to determine the influence of polydimethylsiloxane (PDMS) microfluidic channels containing aligned circular obstacles (with diameters of 172 µm and 132 µm) on the flow velocity and pressure drop under steady-state flow conditions. A significant PDMS bulging was observed when the fluid flow initially contacted the(More)
Emerging smart space applications are increasingly relying on capabilities for recognizing human activities. Activity recognition research is however challenged and slowed by the lack of data necessary for testing and validation. Collecting data through live-in trials in real world deployments is often very expensive and complicated. Legitimate limitations(More)
Simulation of human activities in smart spaces is becoming a necessary toolset to enable telehealth research that promises smart, humanly scalable and cost-effective solutions for aging, disabilities and independence. Human activities are complicated and contain a large number of varieties. Most of the existing research on activity simulations has relied on(More)