Bert Arnrich

Professor

Hasso Plattner Institute

Germany

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Professor Bert Arnrich is a faculty member at the Hasso Plattner Institute in Germany. His research focuses on machine learning applications in healthcare, including alarm fatigue, intermittent fasting, and cognitive load measurement. He has contributed to the development of methodologies for analyzing large datasets, enhancing preoperative outcome predictions, and improving clinical decision support systems.

Bert Arnrich

Research Interests

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Articles (20)

Sensor-based evaluation of intermittent fasting regimes: a machine learning and statistical approach

Developing a Scalable Annotation Method for Large Datasets That Enhances Alarms With Actionability Data to Increase Informativeness: Mixed Methods Approach

Enhancing Preoperative Outcome Prediction: A Comparative Retrospective Case–Control Study on Machine Learning versus the International Esodata Study Group Risk Model for Predicting 90-Day Mortality in Oncologic Esophagectomy

Analyzing population-level trials as N-of-1 trials: An application to gait

Enabling machine learning models in alarm fatigue research: Creation of a large relevance-annotated oxygen saturation alarm data set

Unobtrusive measurement of cognitive load and physiological signals in uncontrolled environments

Twenty-Four Hour Glucose Profiles and Glycemic Variability during Intermittent Religious Dry Fasting and Time-Restricted Eating in Subjects without Diabetes: A Preliminary Study

ARTEMIS: An alarm threshold and policy mining system for the intensive care unit

DUO-GAIT: A gait dataset for walking under dual-task and fatigue conditions with inertial measurement units

Food Choices after Cognitive Load: An Affective Computing Approach

Federated Learning for Network Intrusion Detection in Ambient Assisted Living Environments

Online Learning for Wearable EEG-Based Emotion Classification

Protocol for a Randomized Crossover Trial to Evaluate the Effect of Soft Brace and Rigid Orthosis on Performance and Readiness to Return to Sport Six Months Post-ACL-Reconstruction

Federated Learning for Activity Recognition: A System Level Perspective

HARE: Unifying the Human Activity Recognition Engineering Workflow

Optimization of data pre-processing methods for time-series classification of electroencephalography data

PERSIST: A Multimodal Dataset for the Prediction of Perceived Exertion during Resistance Training

Wearable electroencephalography and multi-modal mental state classification: A systematic literature review

Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-ray Data

SensorHub: Multimodal Sensing in Real-Life Enables Home-Based Studies

Collaborators (10)

Sebastian Kopf

Robert Toivo Tapani Ahlborn

Daniel Denkovski

Axel Winter

Robin Philippus van de Water

Hristijan Gjoreski

Aleksandra Królikowska

Urs Granacher

Lin Zhou

Robert Prill

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