An Investigation of Metrics For the in-situ Detection of Software Expertise
Journal of Human-Computer Interaction
2014
Abstract
We present a sensor visualization system that integrates data streams from individual custom sensor arrays together with Building Automation System (BAS) data. To help bridge the gap between actual building usage by the occupants, and the aggregate assumed usage by the control system, we have developed several sensor processing subsystems moving toward automated human activity recognition without the need for directly instrumenting the occupants. By having a system with a detailed understanding of occupancy behavior and needs, we believe buildings could be much more efficient thereby reducing energy consumption, working toward sustainability of the built environment.
We present a sensor visualization system that integrates data streams from individual custom sensor arrays together with Building Automation System (BAS) data. To help bridge the gap between actual building usage by the occupants, and the aggregate assumed usage by the control system, we have developed several sensor processing subsystems moving toward automated human activity recognition without the need for directly instrumenting the occupants.
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Software Learning
The Learning project aims to investigate advanced techniques for assisting users in learning complicated applications. We are interested in a range of investigations from the scientific study of the human learning process to prototyping novel interaction techniques for improving the general learning mechanisms that can be applied to all applications.
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