Optimizing Interactions: Human-Machine Interfaces
Human-machine interfaces are reshaping how people interact with technology across fields like healthcare, manufacturing, defense, and entertainment. This course looks at key applications—from wearables to robotic systems—and examines how these tools are changing workflows, training, and collaboration.
What you will learn
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Understand the principles of HMI design and user experience.
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Analyze the future trends and challenges in HMI development.
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Explore the applications of HMIs in different technological domains.
Course content
Fundamentals of Human-Machine Interfaces
Examine the basic concepts and historical evolution of HMIs.
Applications of HMIs
Discuss the use of HMIs in industries like automotive, healthcare, and robotics.
Future of HMIs
Explore the advancements and emerging challenges in HMI technology.
Your Course Director
Dr. Aniket Bera
Associate Professor at the Department of Computer Science at Purdue University
Dr. Aniket Bera is an Associate Professor at the Department of Computer Science at Purdue University. He directs the interdisciplinary research lab IDEAS (Intelligent Design for Empathetic and Augmented Systems) at Purdue, working on modeling the "human" and "social" aspects using AI in Robotics, Graphics, and Vision. He is also an Adjunct Associate Professor at the University of Maryland at College Park. Prior to this, he was a Research Assistant Professor at the University of North Carolina at Chapel Hill. He received his Ph.D. in 2017 from the University of North Carolina at Chapel Hill. He is also the founder of Project Dost. He is currently serving as the Senior Editor for IEEE Robotics and Automation Letters (RA-L) in the area of "Planning and Simulation" and the Conference Chair for the ACM SIGGRAPH Conference on Motion, Interaction and Games (MIG 2022).
His core research interests are in Affective Computing, Computer Graphics (AR/VR, Augmented Intelligence, Multi-Agent Simulation), Social Robotics, Autonomous Agents, Cognitive modeling, and planning for intelligent characters. He has advised and co-advised multiple M.S. and Ph.D. students. He has authored over 70+ papers, 2000+ citations and his work has won multiple awards at top Graphics/VR conferences. He also works with the University of Maryland at Baltimore Medical School to build algorithms and systems to help therapists and doctors detect mental health and social anxiety issues (AI + Mental Health). His research involves novel combinations of methods and collaborations in machine learning, computational psychology, computer graphics, and physically-based simulation to develop real-time computational models to learn human behaviors. Dr. Bera has previously worked in many research labs, including Disney Research, Intel, and the Centre for Development of Advanced Computing.