arXiv subjects

Florian Grensing

Publications and source records attributed to Florian Grensing.

4 recordsLinked to original sources

arXiv2026

Evaluating Different Modalities of Behavioral Approach Tests for Spider Phobia in Virtual Reality

Behavioral approach tests are a common means of assessing specific phobias. In these tests, participants move towards an anxiety-inducing stimulus as close as they are willing to, with the final distance indicating the severity of the anxiety. In this work, we aim to evaluate a virtual reality implementation of the BAT. For this purpose, four different BATs were designed, consisting of two approach methods, both replicated in vivo and in virtuo. Evaluation of these BATs is done by using a standardised presence questionnaire, application-specific questions, as well as the physiological reactions of the participants. The study focuses on the fear of spiders and uses a real and virtual spider as an anxiety-inducing stimulus. Our results show that the developed VR BAT perform within established presence norms, while the different modalities influenced participants' subjective impressions. Furthermore, the standardized structure of the VR environment ensured a consistent experience regarding the anxiety-inducing stimulus. This differs from the observation in the real-world setting, where the behavior of the spider might differ between individuals and also between sessions. This highlights one of the key advantages of virtual reality: complete control over the stimulus and environment. Correlations between presence and physiological signals were found. Particularly, tonic electrodermal activity levels are more stable with increased presence. However, more research into this is required, as the effects of anxiety on the physiological signals make the correlations difficult to interpret. The evaluation has revealed, which design choices are particularly promising for increasing presence in VR applications, and some which should be avoided. Overall, these results indicates that our VR-based implementation is a promising tool for assessing avoidance behavior for individuals with spider phobia.

cs.HC↗

arXiv2026

Towards Improved Short-term Hypoglycemia Prediction and Diabetes Management based on Refined Heart Rate Data

Hypoglycemia is a severe condition of decreased blood glucose, specifically below 70 mg/dL (3.9 mmol/L). This condition can often be asymptomatic and challenging to predict in individuals with type 1 diabetes (T1D). Research on hypoglycemic prediction typically uses a combination of blood glucose readings and heart rate data to predict hypoglycemic events. Given that these features are collected through wearable sensors, they can sometimes have missing values, necessitating efficient imputation methods. This work makes significant contributions to the current state of the art by introducing two novel imputation techniques for imputing heart rate values over short-term horizons: Controlled Weighted Rational B\'ezier Curves (CRBC) and Controlled Piecewise Cubic Hermite Interpolating Polynomial with mapped peaks and valleys of Control Points (CMPV). In addition to these imputation methods, we employ two metrics to capture data patterns, alongside a combined metric that integrates the strengths of both individual metrics with RMSE scores for a comprehensive evaluation of the imputation techniques. According to our combined metric assessment, CMPV outperforms the alternatives with an average score of 0.33 across all time gaps, while CRBC follows with a score of 0.48. These findings clearly demonstrate the effectiveness of the proposed imputation methods in accurately filling in missing heart rate values. Moreover, this study facilitates the detection of abnormal physiological signals, enabling the implementation of early preventive measures for more accurate diagnosis.

q-bio.QM↗

arXiv2026

A Proposed Paradigm for Imputing Missing Multi-Sensor Data in the Healthcare Domain

Chronic diseases such as diabetes pose significant management challenges, particularly due to the risk of complications like hypoglycemia, which require timely detection and intervention. Continuous health monitoring through wearable sensors offers a promising solution for early prediction of glycemic events. However, effective use of multisensor data is hindered by issues such as signal noise and frequent missing values. This study examines the limitations of existing datasets and emphasizes the temporal characteristics of key features relevant to hypoglycemia prediction. A comprehensive analysis of imputation techniques is conducted, focusing on those employed in state-of-the-art studies. Furthermore, imputation methods derived from machine learning and deep learning applications in other healthcare contexts are evaluated for their potential to address longer gaps in time-series data. Based on this analysis, a systematic paradigm is proposed, wherein imputation strategies are tailored to the nature of specific features and the duration of missing intervals. The review concludes by emphasizing the importance of investigating the temporal dynamics of individual features and the implementation of multiple, feature-specific imputation techniques to effectively address heterogeneous temporal patterns inherent in the data.

cs.LG↗

arXiv2025

Regression-Based Approach to Anxiety Estimation of Spider Phobics During Behavioural Avoidance Tasks

Phobias significantly impact the quality of life of affected persons. Two methods of assessing anxiety responses are questionnaires and behavioural avoidance tests (BAT). While these can be used in a clinical environment they only record momentary insights into anxiety measures. In this study, we estimate the intensity of anxiety during these BATs, using physiological data collected from unobtrusive, wrist-worn sensors. Twenty-five participants performed four different BATs in a single session, while periodically being asked how anxious they currently are. Using heart rate, heart rate variability, electrodermal activity, and skin temperature, we trained regression models to predict anxiety ratings from three types of input data: (1) using only physiological signals, (2) adding computed features (e.g., min, max, range, variability), and (3) computed features combined with contextual task information. Adding contextual information increased the effectiveness of the model, leading to a root mean squared error (RMSE) of 0.197 and a mean absolute error (MAE) of 0.041. Overall, this study shows, that data obtained from wearables can continuously provide meaningful estimations of anxiety, which can assist in therapy planning and enable more personalised treatment.

cs.HC↗