arXiv subjects

Raphael Weibel

Publications and source records attributed to Raphael Weibel.

3 recordsLinked to original sources

arXiv2026

Pre-Clinical Latency Characterization of VRxBioRelax: A Real-Time EMG Biofeedback System for Muscle Relaxation in Virtual Reality

Chronic tension in the upper trapezius (UT), often caused by poor ergonomics, prolonged posture, or psychological stress, contributes to musculoskeletal discomfort, headaches, and impaired interoceptive awareness. Although surface electromyography (sEMG) biofeedback can promote UT relaxation, traditional systems using conventional displays often fail to sustain engagement. Virtual reality (VR) offers a more immersive alternative, provided that latency remains below perceptual thresholds. We introduce VRxBioRelax, a closed-loop VR biofeedback system that streams sEMG data from Delsys Trigno Avanti sensors via MQTT to a Unity scene. Muscle activation drives a dynamic dawn-to-dusk landscape synchronized with a progressive muscle relaxation protocol. To validate system responsiveness, 87,716 EMG samples from the NinaPro DB2 dataset were replayed at $\sim$75 Hz. Timestamps at four key stages-acquisition, Root Mean Square (RMS) processing, network receipt, and rendering-revealed mean latencies of 0.50 ms (processing), 5.62 ms (network), and 19.22 ms (rendering), yielding an average end-to-end delay of 25.34 ms. Notably, 99.3% of frames arrived within 50 ms. One-sided t-tests confirmed mean latency was significantly lower than both the 30 ms VR comfort limit ($t_{87\,715}=-25.2$, $p=5.9{\times}10^{-140}$) and the 50 ms clinical benchmark ($t_{87\,715}=-133.3$, $p<10^{-300}$). These findings support VRxBioRelax for use in remote interoceptive training, stress reduction, and telepresence-enabled rehabilitation.

cs.HC↗

arXiv2022

"Is It My Turn?" Assessing Teamwork and Taskwork in Collaborative Immersive Analytics

Immersive analytics has the potential to promote collaboration in machine learning (ML). This is desired due to the specific characteristics of ML modeling in practice, namely the complexity of ML, the interdisciplinary approach in industry, and the need for ML interpretability. In this work, we introduce an augmented reality-based system for collaborative immersive analytics that is designed to support ML modeling in interdisciplinary teams. We conduct a user study to examine how collaboration unfolds when users with different professional backgrounds and levels of ML knowledge interact in solving different ML tasks. Specifically, we use the pair analytics methodology and performance assessments to assess collaboration and explore their interactions with each other and the system. Based on this, we provide qualitative and quantitative results on both teamwork and taskwork during collaboration. Our results show how our system elicits sustained collaboration as measured along six distinct dimensions. We finally make recommendations how immersive systems should be designed to elicit sustained collaboration in ML modeling.

cs.HC↗

arXiv2022

Creative Uses of AI Systems and their Explanations: A Case Study from Insurance

Recent works have recognized the need for human-centered perspectives when designing and evaluating human-AI interactions and explainable AI methods. Yet, current approaches fall short at intercepting and managing unexpected user behavior resulting from the interaction with AI systems and explainability methods of different stake-holder groups. In this work, we explore the use of AI and explainability methods in the insurance domain. In an qualitative case study with participants with different roles and professional backgrounds, we show that AI and explainability methods are used in creative ways in daily workflows, resulting in a divergence between their intended and actual use. Finally, we discuss some recommendations for the design of human-AI interactions and explainable AI methods to manage the risks and harness the potential of unexpected user behavior.

cs.HC↗