Multimodal Feedback

Multimodal feedback systems aim to improve human-computer and human-robot interaction by integrating multiple sensory modalities (e.g., visual, haptic, auditory) to provide richer, more effective feedback. Current research focuses on developing and evaluating these systems across diverse applications, including prosthetic control, robot training, and human-robot collaboration, often employing machine learning models like transformers and incorporating adaptive feedback mechanisms based on real-time performance assessment. This work is significant for enhancing user experience, improving training efficacy, and enabling more intuitive and efficient interaction with complex systems, ultimately leading to advancements in fields ranging from assistive technologies to human-robot teaming.