Human Machine Interface

Human-machine interfaces (HMIs) aim to create intuitive and efficient communication between humans and machines, focusing on improving user experience and safety across diverse applications. Current research emphasizes developing robust and efficient HMIs using various methods, including machine learning models like convolutional neural networks (CNNs) and transformers for gesture recognition, and psychophysiological measures (EEG, ECG, EDA) for workload assessment in contexts such as autonomous vehicle operation. These advancements are crucial for enhancing the usability and safety of technologies ranging from prosthetic limbs and assistive devices to autonomous vehicles and interactive robots, impacting both the scientific understanding of human-computer interaction and the development of practical applications.

Human Machine Interface

Papers

Deep learning based Hand gesture recognition system and design of a Human-Machine Interface

Deep Learning and Handheld Augmented Reality Based System for Optimal Data Collection in Fault Diagnostics Domain

First Contact: Unsupervised Human-Machine Co-Adaptation via Mutual Information Maximization