5 papers
MyoInteract: A Framework for Fast Prototyping of Biomechanical HCI Tasks using Reinforcement Learning
Ankit Bhattarai, Hannah Selder, Florian Fischer +2
Reinforcement learning (RL)-based biomechanical simulations have the potential to revolutionise HCI research and interaction design, but currently lack usability and interpretabili…
Attention Trajectories as a Diagnostic Axis for Deep Reinforcement Learning
Charlotte Beylier, Hannah Selder, Arthur Fleig +2
While deep reinforcement learning agents demonstrate high performance across domains, their internal decision processes remain difficult to interpret when evaluated only through pe…
Demystifying Reward Design in Reinforcement Learning for Upper Extremity Interaction: Practical Guidelines for Biomechanical Simulations in HCI
Hannah Selder, Florian Fischer, Per Ola Kristensson +1
Designing effective reward functions is critical for reinforcement learning-based biomechanical simulations, yet HCI researchers and practitioners often waste (computation) time wi…
Mind & Motion: Opportunities and Applications of Integrating Biomechanics and Cognitive Models in HCI
Arthur Fleig, Florian Fischer, Markus Klar +5
Computational models of how users perceive and act within a virtual or physical environment offer enormous potential for the understanding and design of user interactions. Cognitio…
What Makes a Model Breathe? Understanding Reinforcement Learning Reward Function Design in Biomechanical User Simulation
Hannah Selder, Florian Fischer, Per Ola Kristensson +1
Biomechanical models allow for diverse simulations of user movements in interaction. Their performance depends critically on the careful design of reward functions, yet the interpl…