It’s alive!
ECE
Dr. Ali Etemad looks to create the building blocks for a world of ambient intelligence
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ne day soon your coffeemaker will know when you’re about to wake up so that it can have piping hot java ready as you amble into the kitchen. On a cold winter morning, your home will heat up, the lights will gradually turn on, and the blinds will go up to let in the sunlight as you awaken and climb out of bed. Whether you wake at dawn or sleep in, you won’t have to do anything to make all this happen at just the right time. Why? Because everyday objects in your home like your bed, pajamas, and coffeemaker will be fitted with sensors that continuously monitor your biological signals, share that information, and intelligently analyze the data to understand and predict your behaviour and preferences to customize interactions with you. Dr. Ali Etemad, an assistant professor in the Department of Electrical and Computer Engineering (ECE), is creating the building blocks for a world of ambient intelligence in which the objects in a person’s environment are
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THE COMPLETE ENGINEER
Here at Queen’s I get to work on
various multidisciplinary research projects and collaborate with
exceptional and world-leading
researchers. I can think about the
”
big picture and how each small project can help to create the
ultimate new paradigm of how we
interact with and use technology in a super-exciting way.
electronically sensitive and responsive to the presence of people. “My research is about realizing the concept of ambient intelligence through integrating sensors and artificial intelligence into everyday devices with the goal of always understanding what state people are in and interacting with them in a very personalized and adaptive manner,” he
says. Etemad joined the ECE department in July and established the Ambient Intelligence and Interaction Laboratory (Amii) at Queen’s. Etemad uses machine learning and pattern analysis techniques to understand people and identify the best ways of interacting with them. Devices can learn what the user’s physical and mental models are over time and apply that knowledge to adapt, personalize, predict, and interact accordingly with people. “I constantly need to use different types of sensors to understand, analyze, and learn what the person is doing so that proper interactions can be triggered,” he says. “But then I go deeper and use data analysis to understand questions like, ‘How is the person feeling?’ ‘What is he or she perceiving?’ ‘And why is the person doing what he or she is doing?’” Before joining Queen’s, Etemad developed machine learning methods and algorithms to model human walking styles and detect personal characteristics such as gender, age, energy level, and