arXiv subjects

Florian Fischer

Publications and source records attributed to Florian Fischer.

At least 19 recordsLinked to original sources

arXiv2026

MyoInteract: A Framework for Fast Prototyping of Biomechanical HCI Tasks using Reinforcement Learning

Reinforcement learning (RL)-based biomechanical simulations have the potential to revolutionise HCI research and interaction design, but currently lack usability and interpretability. Using the Human Action Cycle as a design lens, we identify key limitations of biomechanical RL frameworks and develop MyoInteract, a novel framework for fast prototyping of biomechanical HCI tasks. MyoInteract allows designers to setup tasks, user models, and training parameters from an easy-to-use GUI within minutes. It trains and evaluates muscle-actuated simulated users within minutes, reducing training times by up to 98%. A workshop study with 12 interaction designers revealed that MyoInteract allowed novices in biomechanical RL to successfully setup, train, and assess goal-directed user movements within a single session. By transforming biomechanical RL from a days-long expert task into an accessible hour-long workflow, this work significantly lowers barriers to entry and accelerates iteration cycles in HCI biomechanics research.

cs.HC↗

arXiv2025

Demystifying Reward Design in Reinforcement Learning for Upper Extremity Interaction: Practical Guidelines for Biomechanical Simulations in HCI

Designing effective reward functions is critical for reinforcement learning-based biomechanical simulations, yet HCI researchers and practitioners often waste (computation) time with unintuitive trial-and-error tuning. This paper demystifies reward function design by systematically analyzing the impact of effort minimization, task completion bonuses, and target proximity incentives on typical HCI tasks such as pointing, tracking, and choice reaction. We show that proximity incentives are essential for guiding movement, while completion bonuses ensure task success. Effort terms, though optional, help refine motion regularity when appropriately scaled. We perform an extensive analysis of how sensitive task success and completion time depend on the weights of these three reward components. From these results we derive practical guidelines to create plausible biomechanical simulations without the need for reinforcement learning expertise, which we then validate on remote control and keyboard typing tasks. This paper advances simulation-based interaction design and evaluation in HCI by improving the efficiency and applicability of biomechanical user modeling for real-world interface development.

cs.HC↗

arXiv2025

Mind & Motion: Opportunities and Applications of Integrating Biomechanics and Cognitive Models in HCI

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. Cognition models have been used to understand the role of attention and individual preferences and beliefs on human decision making during interaction, while biomechanical simulations have been successfully applied to analyse and predict physical effort, fatigue, and discomfort. The next frontier in HCI lies in connecting these models to enable robust, diverse, and representative simulations of different user groups. These embodied user simulations could predict user intents, strategies, and movements during interaction more accurately, benchmark interfaces and interaction techniques in terms of performance and ergonomics, and guide adaptive system design. This UIST workshop explores ideas for integrating computational models into HCI and discusses use cases such as UI/UX design, automated system testing, and personalised adaptive interfaces. It brings researchers from relevant disciplines together to identify key opportunities and challenges as well as feasible next steps for bridging mind and motion to simulate interactive user behaviour.

cs.HC↗

arXiv2025

Characterizations of $p$-Parabolicity on Graphs

We study $p$-energy functionals on infinite locally summable graphs for $p\in (1,\infty)$ and show that many well-known characterizations for a parabolic space are also true in this discrete, non-local and non-linear setting. Among the characterizations are an Ahlfors-type, a Kelvin-Nevanlinna-Royden-type, a Khas'minski\u{\i}-type and a Poincar\'{e}-type characterization. We also illustrate some applications and describe examples of graphs which are locally summable but not locally finite. Finally, we study the obstacle problem for the $p$-Laplacian using an approximation procedure by finite graphs in the summable, not necessarily locally finite, case. This is then utilized to give an alternative proof of the Khas'minski\u{\i}-type characterization.

math.FA↗

arXiv2025

What Makes a Model Breathe? Understanding Reinforcement Learning Reward Function Design in Biomechanical User Simulation

Biomechanical models allow for diverse simulations of user movements in interaction. Their performance depends critically on the careful design of reward functions, yet the interplay between reward components and emergent behaviours remains poorly understood. We investigate what makes a model "breathe" by systematically analysing the impact of rewarding effort minimisation, task completion, and target proximity on movement trajectories. Using a choice reaction task as a test-bed, we find that a combination of completion bonus and proximity incentives is essential for task success. Effort terms are optional, but can help avoid irregularities if scaled appropriately. Our work offers practical insights for HCI designers to create realistic simulations without needing deep reinforcement learning expertise, advancing the use of simulations as a powerful tool for interaction design and evaluation in HCI.

cs.HC↗

arXiv2025

AlphaPIG: The Nicest Way to Prolong Interactive Gestures in Extended Reality

Mid-air gestures serve as a common interaction modality across Extended Reality (XR) applications, enhancing engagement and ownership through intuitive body movements. However, prolonged arm movements induce shoulder fatigue, known as "Gorilla Arm Syndrome", degrading user experience and reducing interaction duration. Although existing ergonomic techniques derived from Fitts' law (such as reducing target distance, increasing target width, and modifying control-display gain) provide some fatigue mitigation, their implementation in XR applications remains challenging due to the complex balance between user engagement and physical exertion. We present AlphaPIG, a meta-technique designed to Prolong Interactive Gestures by leveraging real-time fatigue predictions. AlphaPIG assists designers in extending and improving XR interactions by enabling automated fatigue-based interventions. Through adjustment of intervention timing and intensity decay rate, designers can explore and control the trade-off between fatigue reduction and potential effects such as decreased body ownership. We validated AlphaPIG's effectiveness through a study (N=22) implementing the widely-used Go-Go technique. Results demonstrated that AlphaPIG significantly reduces shoulder fatigue compared to non-adaptive Go-Go, while maintaining comparable perceived body ownership and agency. Based on these findings, we discuss positive and negative perceptions of the intervention. By integrating real-time fatigue prediction with adaptive intervention mechanisms, AlphaPIG constitutes a critical first step towards creating fatigue-aware applications in XR.

cs.HC↗

arXiv2025

Optimal Poincar\'e-Hardy-type Inequalities on Manifolds and Graphs

We review a method to obtain optimal Poincar\'e-Hardy-type inequalities on the hyperbolic spaces, and discuss briefly generalisations to certain classes of Riemannian manifolds. Afterwards, we recall a corresponding result on homogeneous regular trees and provide a new proof using the aforementioned method. The same strategy will then be applied to obtain new optimal Hardy-type inequalities on weakly spherically symmetric graphs which include fast enough growing trees and anti-trees. In particular, this yields optimal weights which are larger at infinity than the optimal weights classically constructed via the Fitzsimmons ratio of the square root of the minimal positive Green's function.

math.AP↗

arXiv2024

SIM2VR: Towards Automated Biomechanical Testing in VR

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 process. In particular, it allows prediction of user movements and ergonomic variables, such as fatigue, prior to conducting user studies. However, there is a fundamental disconnect between simulators hosting state-of-the-art biomechanical user models and simulators used to develop and run VR applications. Existing user simulators often struggle to capture the intricacies of real-world VR applications, reducing ecological validity of user predictions. In this paper, we introduce SIM2VR, a system that aligns user simulation with a given VR application by establishing a continuous closed loop between the two processes. This, for the first time, enables training simulated users directly in the same VR application that real users interact with. We demonstrate that SIM2VR can predict differences in user performance, ergonomics and strategies in a fast-paced, dynamic arcade game. In order to expand the scope of automated biomechanical testing beyond simple visuomotor tasks, advances in cognitive models and reward function design will be needed.

cs.HC↗

arXiv2023

Capacity of infinite graphs over non-Archimedean ordered fields

In this article we study the notion of capacity of a vertex for infinite graphs over non-Archimedean fields. In contrast to graphs over the real field monotone limits do not need to exist. Thus, in our situation next to positive and null capacity there is a third case of divergent capacity. However, we show that either of these cases is independent of the choice of the vertex and is therefore a global property for connected graphs. The capacity is shown to connect the minimization of the energy, solutions of the Dirichlet problem and existence of a Green's function. We furthermore give sufficient criteria in form of a Nash-Williams test, study the relation to Hardy inequalities and discuss the existence of positive superharmonic functions. Finally, we investigate the analytic features of the transition operator in relation to the inverse of the Laplace operator.

math.AP↗

arXiv2023

Sharp Hardy-type inequalities for non-compact harmonic manifolds and Damek-Ricci spaces

We show various sharp Hardy-type inequalities for the linear and quasi-linear Laplacian on non-compact harmonic manifolds with a particular focus on the case of Damek-Ricci spaces. Our methods make use of the optimality theory developed by Devyver/Fraas/Pinchover and Devyver/Pinchover and are motivated by corresponding results for hyperbolic spaces by Berchio/Ganguly/Grillo, and Berchio/Ganguly/Grillo/Pinchover.

math.AP↗

arXiv2023

Towards a Deep(er) Understanding of Interaction through Modeling, Simulation, and Optimization

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 augment the tried-and-tested approach of conducting user studies with simulation and optimization of the entire human-computer interaction loop. This approach allows to better understand phenomena through explicit modeling, build virtual prototypes through simulation, and improve interaction techniques through optimization. Building predictive user models also supports the creation and validation of HCI theories, and constitutes a decisive step towards new, intelligent, and adaptive user interfaces. We report our experience in virtually developing new interaction techniques on the example of acoustic levitation, and present our optimization-based framework for HCI. With this, we strive to gain a better understanding of interaction and at the same time feed the discussion on questions such as which tools and tutorials are necessary to make virtual prototyping more accessible to different audiences.

cs.HC↗

arXiv2022

Testbed for Functional Safety-Relevant Wireless Communication Based on IO-Link Wireless and 5G

In the field of industrial production automation, wireless networks support highly flexible manufacturing processes and enable technologies to set-up new production chains and future software businesses. The IO-Link Wireless (IOLW) protocol is an already established energy-efficient and cost-effective communication standard for smart sensor devices on the industrial shop floor, whereas the mobile communication standard 5G will be mainly applied for medium and long-range wireless communication applications promising low latency times and high reliability. Therefore, 5G with the coming enhancement of deterministic ultra-Reliable Low-Latency Communication (uRLLC) is combined with the robustness and low-latency performance characteristics of IO-Link Wireless. Features of both technologies are highly beneficial to realize even highly demanding safety-related applications. The presented testbed shall qualify wireless functional safety communication with respect to its Residual Error Probability (REP) and quantify the Probability of Failure per Hour (PFH).

cs.CR↗

arXiv2022

On the Optimality and Decay of $p$-Hardy Weights on Graphs

We construct optimal Hardy weights to subcritical energy functionals $h$ associated with quasilinear Schr\"odinger operators on locally finite graphs. Here, optimality means that the weight $w$ is the largest possible with respect to a partial ordering, and that the corresponding shifted energy functional $h-w$ is null-critical. Moreover, we show a decay condition of Hardy weights in terms of their integrability with respect to certain integral weights. As an application of the decay condition, we show that null-criticality implies optimality near infinity. We also briefly discuss an uncertainty-type principle and a Rellich-type inequality.

math.AP↗

arXiv2022

On the Security of IO-Link Wireless Communication in the Safety Domain

Security is an essential requirement of Industrial Control System (ICS) environments and its underlying communication infrastructure. Especially the lowest communication level within Supervisory Control and Data Acquisition (SCADA) systems - the field level - commonly lacks security measures. Since emerging wireless technologies within field level expose the lowest communication infrastructure towards potential attackers, additional security measures above the prevalent concept of air-gapped communication must be considered. Therefore, this work analyzes security aspects for the wireless communication protocol IO-LinkWireless (IOLW), which is commonly used for sensor and actuator field level communication. A possible architecture for an IOLW safety layer has already been presented recently [1]. In this paper, the overall attack surface of IOLW within its typical environment is analyzed and attack preconditions are investigated to assess the effectiveness of different security measures. Additionally, enhanced security measures are evaluated for the communication systems and the results are summarized. Also, interference of security measures and functional safety principles within the communication are investigated, which do not necessarily complement one another but may also have contradictory requirements. This work is intended to discuss and propose enhancements of the IOLW standard with additional security considerations in future implementations.

cs.CR↗

arXiv2022

Quasi-Linear Criticality Theory and Green's Functions on Graphs

We study energy functionals associated with quasi-linear Schrödinger operators on infinite graphs, and develop characterisations of (sub-)criticality via Green's functions, harmonic functions of minimal growth and capacities. We proof a quasi-linear version of the Agmon-Allegretto-Piepenbrink theorem, which says that the energy functional is non-negative if and only if there is a positive superharmonic function. Furthermore, we show that a Green's function exists if and only if the energy functional is subcritical. Comparison principles and maximum principles are the main tools in the proofs.

math-ph↗

arXiv2022

Reinforcement Learning Control of a Biomechanical Model of the Upper Extremity

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 to certain movement constraints like signal-dependent and constant motor noise. While so far these assumptions have only been evaluated for simplified point-mass or planar models, we address the question of whether they can predict reaching movements in a full skeletal model of the human upper extremity. We learn a control policy using a motor babbling approach as implemented in reinforcement learning, using aimed movements of the tip of the right index finger towards randomly placed 3D targets of varying size. We use a state-of-the-art biomechanical model, which includes seven actuated degrees of freedom. To deal with the curse of dimensionality, we use a simplified second-order muscle model, acting at each degree of freedom instead of individual muscles. The results confirm that the assumptions of signal-dependent and constant motor noise, together with the objective of movement time minimization, are sufficient for a state-of-the-art skeletal model of the human upper extremity to reproduce complex phenomena of human movement, in particular Fitts' Law and the 2/3 Power Law. This result supports the notion that control of the complex human biomechanical system can plausibly be determined by a set of simple assumptions and can easily be learned.

q-bio.QM↗

arXiv2022

Optimal Feedback Control for Modeling Human-Computer Interaction

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 is based on the assumption that humans aim to control their body optimally, within the constraints imposed by body, environment, and task. In this paper, we explain how this theory can be applied to understanding Human-Computer Interaction (HCI) in the case of pointing. We propose that the human body and computer dynamics can be interpreted as a single dynamical system. The system state is controlled by the user via muscle control signals, and estimated from observations. Between-trial variability arises from signal-dependent control noise and observation noise. We compare four different models from optimal control theory and evaluate to what degree these models can replicate movements in the case of mouse pointing. We introduce a procedure to identify parameters that best explain observed user behavior. To support HCI researchers in simulating, analyzing, and optimizing interaction movements, we provide the Python toolbox OFC4HCI. We conclude that OFC presents a powerful framework for HCI to understand and simulate motion of the human body and of the interface on a moment by moment basis.

cs.HC↗

arXiv2022

Simulating Interaction Movements via Model Predictive Control

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 Feedback Control (OFC) perspective. We assume that users aim to minimize an internalized cost function, subject to the constraints imposed by the human body and the interactive system. In contrast to previous linear approaches used in HCI, MPC can compute optimal controls for nonlinear systems. This allows us to use state-of-the-art biomechanical models and handle nonlinearities that occur in almost any interactive system. Instead of torque actuation, our model employs second-order muscles acting directly at the joints. We compare three different cost functions and evaluate the simulated trajectories against user movements in a Fitts' Law type pointing study with four different interaction techniques. Our results show that the combination of distance, control, and joint acceleration cost matches individual users' movements best, and predicts movements with an accuracy that is within the between-user variance. To aid HCI researchers and designers, we introduce CFAT, a novel method to identify maximum voluntary torques in joint-actuated models based on experimental data, and give practical advice on how to simulate human movement for different users, interaction techniques, and tasks.

cs.HC↗