Bjoern M. Eskofier

Research interests

Publications

• Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium

  Journal of NeuroEngineering and Rehabilitation · 2023 · 10.1186/s12984-023-01198-5

• Advancing digital health applications: priorities for innovation in real-world evidence generation

  The Lancet Digital Health · 2022 · 10.1016/s2589-7500(21)00292-2

• Federated Learning for Healthcare: Systematic Review and Architecture Proposal

  ACM Transactions on Intelligent Systems and Technology · 2022 · https://doi.org/10.1145/3501813

• Technical validation of real-world monitoring of gait: a multicentric observational study

  BMJ Open · 2021 · 10.1136/bmjopen-2021-050785

• CNN-Based Estimation of Sagittal Plane Walking and Running Biomechanics From Measured and Simulated Inertial Sensor Data

  Frontiers in Bioengineering and Biotechnology · 2020 · 10.3389/fbioe.2020.00604

• Estimation of gait kinematics and kinetics from inertial sensor data using optimal control of musculoskeletal models

  Journal of Biomechanics · 2019 · 10.1016/j.jbiomech.2019.07.022

• Internet of Health Things: Toward intelligent vital signs monitoring in hospital wards

  Artificial Intelligence in Medicine · 2018 · 10.1016/j.artmed.2018.05.005

• Multimodal Assessment of Parkinson's Disease: A Deep Learning Approach

  IEEE Journal of Biomedical and Health Informatics · 2018 · 10.1109/jbhi.2018.2866873

• An Overview of Smart Shoes in the Internet of Health Things: Gait and Mobility Assessment in Health Promotion and Disease Monitoring

  Applied Sciences · 2017 · 10.3390/app7100986

• Activity recognition in beach volleyball using a Deep Convolutional Neural Network

  Data Mining and Knowledge Discovery · 2017 · 10.1007/s10618-017-0495-0

• Towards Mobile Gait Analysis: Concurrent Validity and Test-Retest Reliability of an Inertial Measurement System for the Assessment of Spatio-Temporal Gait Parameters

  Sensors · 2017 · 10.3390/s17071522

• Wearable sensors objectively measure gait parameters in Parkinson’s disease

  PLoS ONE · 2017 · https://doi.org/10.1371/journal.pone.0183989

• Recent machine learning advancements in sensor-based mobility analysis: Deep learning for Parkinson's disease assessment

  · 2016 · 10.1109/embc.2016.7590787

• Technology in Parkinson's disease: Challenges and opportunities

  Movement Disorders · 2016 · https://doi.org/10.1002/mds.26642

• Sensor-Based Gait Parameter Extraction With Deep Convolutional Neural Networks

  IEEE Journal of Biomedical and Health Informatics · 2016 · 10.1109/jbhi.2016.2636456

• An approximation of the Gaussian RBF kernel for efficient classification with SVMs

  Pattern Recognition Letters · 2016 · 10.1016/j.patrec.2016.08.013

• Effect of walking speed on gait sub phase durations

  Human Movement Science · 2015 · 10.1016/j.humov.2015.07.009

• An Emerging Era in the Management of Parkinson's Disease: Wearable Technologies and the Internet of Things

  IEEE Journal of Biomedical and Health Informatics · 2015 · https://doi.org/10.1109/jbhi.2015.2461555

• Stride Segmentation during Free Walk Movements Using Multi-Dimensional Subsequence Dynamic Time Warping on Inertial Sensor Data

  Sensors · 2015 · 10.3390/s150306419

• Revisiting QRS Detection Methodologies for Portable, Wearable, Battery-Operated, and Wireless ECG Systems

  PLoS ONE · 2014 · 10.1371/journal.pone.0084018

• Inertial Sensor-Based Stride Parameter Calculation From Gait Sequences in Geriatric Patients

  IEEE Transactions on Biomedical Engineering · 2014 · https://doi.org/10.1109/tbme.2014.2368211

• Hierarchical, Multi-Sensor Based Classification of Daily Life Activities: Comparison with State-of-the-Art Algorithms Using a Benchmark Dataset

  PLoS ONE · 2013 · 10.1371/journal.pone.0075196

• Unbiased and Mobile Gait Analysis Detects Motor Impairment in Parkinson's Disease

  PLoS ONE · 2013 · 10.1371/journal.pone.0056956

• Real-time ECG monitoring and arrhythmia detection using Android-based mobile devices

  · 2012 · 10.1109/embc.2012.6346460

• Biometric and mobile gait analysis for early diagnosis and therapy monitoring in Parkinson's disease

  · 2011 · 10.1109/iembs.2011.6090226

Current projects

No projects listed.