| Technology in Parkinson's disease: challenges and opportunities AJ Espay, P Bonato, FB Nahab, W Maetzler, JM Dean, J Klucken, ... Movement Disorders 31 (9), 1272-1282, 2016 | 675 | 2016 |
| Federated learning for healthcare: Systematic review and architecture proposal RS Antunes, C André da Costa, A Küderle, IA Yari, B Eskofier ACM Transactions on Intelligent Systems and Technology (TIST) 13 (4), 1-23, 2022 | 411 | 2022 |
| An emerging era in the management of Parkinson's disease: wearable technologies and the internet of things CF Pasluosta, H Gassner, J Winkler, J Klucken, BM Eskofier IEEE journal of biomedical and health informatics 19 (6), 1873-1881, 2015 | 394 | 2015 |
| Inertial sensor-based stride parameter calculation from gait sequences in geriatric patients A Rampp, J Barth, S Schülein, KG Gaßmann, J Klucken, BM Eskofier IEEE transactions on biomedical engineering 62 (4), 1089-1097, 2014 | 367 | 2014 |
| Wearable sensors objectively measure gait parameters in Parkinson’s disease JCM Schlachetzki, J Barth, F Marxreiter, J Gossler, Z Kohl, S Reinfelder, ... PloS one 12 (10), e0183989, 2017 | 355 | 2017 |
| Internet of Health Things: Toward intelligent vital signs monitoring in hospital wards CA Da Costa, CF Pasluosta, B Eskofier, DB Da Silva, R da Rosa Righi Artificial intelligence in medicine 89, 61-69, 2018 | 315 | 2018 |
| Revisiting QRS detection methodologies for portable, wearable, battery-operated, and wireless ECG systems M Elgendi, B Eskofier, S Dokos, D Abbott PloS one 9 (1), e84018, 2014 | 290 | 2014 |
| Unbiased and mobile gait analysis detects motor impairment in Parkinson's disease J Klucken, J Barth, P Kugler, J Schlachetzki, T Henze, F Marxreiter, Z Kohl, ... PloS one 8 (2), e56956, 2013 | 290 | 2013 |
| Hierarchical, multi-sensor based classification of daily life activities: comparison with state-of-the-art algorithms using a benchmark dataset H Leutheuser, D Schuldhaus, BM Eskofier PloS one 8 (10), e75196, 2013 | 246 | 2013 |
| Multimodal assessment of Parkinson's disease: a deep learning approach JC Vásquez-Correa, T Arias-Vergara, JR Orozco-Arroyave, B Eskofier, ... IEEE journal of biomedical and health informatics 23 (4), 1618-1630, 2018 | 237 | 2018 |
| Real-time ECG monitoring and arrhythmia detection using Android-based mobile devices S Gradl, P Kugler, C Lohmüller, B Eskofier 2012 annual international conference of the IEEE engineering in medicine and …, 2012 | 226 | 2012 |
| Stride segmentation during free walk movements using multi-dimensional subsequence dynamic time warping on inertial sensor data J Barth, C Oberndorfer, C Pasluosta, S Schülein, H Gassner, S Reinfelder, ... Sensors 15 (3), 6419-6440, 2015 | 219 | 2015 |
| Activity recognition in beach volleyball using a Deep Convolutional Neural Network: Leveraging the potential of Deep Learning in sports T Kautz, BH Groh, J Hannink, U Jensen, H Strubberg, BM Eskofier Data Mining and Knowledge Discovery 31, 1678-1705, 2017 | 207 | 2017 |
| Sensor-based gait parameter extraction with deep convolutional neural networks J Hannink, T Kautz, CF Pasluosta, KG Gaßmann, J Klucken, BM Eskofier IEEE journal of biomedical and health informatics 21 (1), 85-93, 2016 | 204 | 2016 |
| Recent machine learning advancements in sensor-based mobility analysis: Deep learning for Parkinson's disease assessment BM Eskofier, SI Lee, JF Daneault, FN Golabchi, G Ferreira-Carvalho, ... 2016 38th annual international conference of the IEEE engineering in …, 2016 | 203 | 2016 |
| Biometric and mobile gait analysis for early diagnosis and therapy monitoring in Parkinson's disease J Barth, J Klucken, P Kugler, T Kammerer, R Steidl, J Winkler, ... 2011 annual international conference of the IEEE engineering in medicine and …, 2011 | 195 | 2011 |
| An overview of smart shoes in the internet of health things: gait and mobility assessment in health promotion and disease monitoring BM Eskofier, SI Lee, M Baron, A Simon, CF Martindale, H Gaßner, ... Applied Sciences 7 (10), 986, 2017 | 164 | 2017 |
| Towards mobile gait analysis: concurrent validity and test-retest reliability of an inertial measurement system for the assessment of spatio-temporal gait parameters F Kluge, H Gaßner, J Hannink, C Pasluosta, J Klucken, BM Eskofier Sensors 17 (7), 1522, 2017 | 159 | 2017 |
| Mobile stride length estimation with deep convolutional neural networks J Hannink, T Kautz, CF Pasluosta, J Barth, S Schülein, KG Gaßmann, ... IEEE journal of biomedical and health informatics 22 (2), 354-362, 2017 | 149 | 2017 |
| Estimation of gait kinematics and kinetics from inertial sensor data using optimal control of musculoskeletal models E Dorschky, M Nitschke, AK Seifer, AJ van den Bogert, BM Eskofier Journal of biomechanics 95, 109278, 2019 | 136 | 2019 |
Jochen KluckenUniversity of Luxembourg, Centre Hospitalier du Luxembourg在 uni.lu 的电子邮件经过验证