53 citations · 123 across the 12 of their papers we have counts for
12 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
The emergence and evolution of feature reliance in deep reinforcement learning agents remain poorly understood. Here, we introduce a methodological framework for analyzing the lear…
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…
SIM2VR: Towards Automated Biomechanical Testing in VR
Florian Fischer, Aleksi Ikkala, Markus Klar +6
Automated biomechanical testing has great potential for the development of VR applications, as initial insights into user behaviour can be gained in silico early in the design proc…
Towards a Deep(er) Understanding of Interaction through Modeling, Simulation, and Optimization
Florian Fischer, Arthur Fleig, Markus Klar +2
The traditional user-centered design process can hardly keep up with the ever faster technical development and increasingly diverse user preferences. As a solution, we propose to a…
Simulating Interaction Movements via Model Predictive Control
Markus Klar, Florian Fischer, Arthur Fleig +2
We present a method to simulate movement in interaction with computers, using Model Predictive Control (MPC). The method starts from understanding interaction from an Optimal Feedb…
OptiTrap: Optimal Trap Trajectories for Acoustic Levitation Displays
Viktorija Paneva, Arthur Fleig, Diego Martínez Plasencia +2
Acoustic levitation has recently demonstrated the ability to create volumetric content by trapping and quickly moving particles along reference paths to reveal shapes in mid-air. H…
Optimal Feedback Control for Modeling Human-Computer Interaction
Florian Fischer, Arthur Fleig, Markus Klar +1
Optimal feedback control (OFC) is a theory from the motor control literature that explains how humans move their body to achieve a certain goal, e.g., pointing with the finger. OFC…
Reinforcement Learning Control of a Biomechanical Model of the Upper Extremity
Florian Fischer, Miroslav Bachinski, Markus Klar +2
Among the infinite number of possible movements that can be produced, humans are commonly assumed to choose those that optimize criteria such as minimizing movement time, subject t…
An Optimal Control Model of Mouse Pointing Using the LQR
Florian Fischer, Arthur Fleig, Markus Klar +2
In this paper we explore the Linear-Quadratic Regulator (LQR) to model movement of the mouse pointer. We propose a model in which users are assumed to behave optimally with respect…