Sriharsha Putrevu

Balancing Trust and Automation Needs for Effective Home Energy Management

With the increasing shortage of energy resources and the adverse impact of non-renewable fuels on the environment, there is a shift in the consumer’s mindset to emphasize managing and utilizing energy efficiently, reducing green house emissions and contributing to a clean environment. This is especially true to the residential markets where a trusted Home Energy Manager (HEM) device can aid in automating and delivering effective energy management strategies in homes. Home users are often passive in their interaction and have to be engaged and reassured that a HEM device contributes positively to the goal of home energy management. The objective is to boost their trust and confidence in HEM by making information (such as energy costs, usage patterns, etc.) accessible and enabling them to act and conserve energy effectively based on the same. In this research, we explore and understand the potential factors that influence how users would engage and interact with HEM device. Some basic functions of the HEM device include: a trusted advisor that provides dynamic recommendations based on user’s interaction and behavior in the home; ability to sense occupancy within the home and automatically adjust schedules without the need for explicit human intervention; deduce energy usage patterns; and adapt energy management strategies based on the user profiles derived from their behaviors and interaction with the thermostat. Using a HEM device with the proper balance of automation and user engagement can have a positive impact on reducing the global energy consumption and the sustenance of our environment.

Eye Movement Behavior During Confusion Toward a Method

The purpose of the present study was to develop a methodology to identify when a user is confused while using a product. Eye movements were measured to determine whether they reflect confusion while users completed tasks with two simulated devices. First, two devices that differed in subjective ratings of confusion were identified. Then eye movements and task performance were measured while experienced and inexperienced users conducted nine tasks with the devices. The relationship between eye movement and confusion measures depended on the task and the user group. Results provide a foundation for developing methods to identify and predict user confusion on the basis of eye movements, and ultimately to design products to avoid confusion.