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.
Papers
Human-Machine Interface Evaluation Using EEG in Driving Simulator
Y. C. Liu, N. Figalova, M. Baumann, K Bengler
Workload Assessment of Human-Machine Interface: A Simulator Study with Psychophysiological Measures
Yuan-Cheng Liu, Nikol Figalova, Juergen Pichen, Philipp Hock, Martin Baumann, Klaus Bengler