| Deep neural networks enable quantitative movement analysis using single-camera videos Ł Kidziński, B Yang, JL Hicks, A Rajagopal, SL Delp, MH Schwartz Nature communications 11 (1), 4054, 2020 | 269 | 2020 |
| Dynamic functional principal components S Hörmann, Ł Kidziński, M Hallin Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2015 | 243 | 2015 |
| OpenCap: Human movement dynamics from smartphone videos SD Uhlrich, A Falisse, Ł Kidziński, J Muccini, M Ko, AS Chaudhari, ... PLoS computational biology 19 (10), e1011462, 2023 | 234 | 2023 |
| Wearable sensors enable personalized predictions of clinical laboratory measurements J Dunn, L Kidzinski, R Runge, D Witt, JL Hicks, ... Nature medicine 27 (6), 1105-1112, 2021 | 208 | 2021 |
| Automated human-level diagnosis of dysgraphia using a consumer tablet T Asselborn, T Gargot, Ł Kidziński, W Johal, D Cohen, C Jolly, ... NPJ digital medicine 1 (1), 42, 2018 | 139 | 2018 |
| Multimodal teaching analytics: Automated extraction of orchestration graphs from wearable sensor data LP Prieto, K Sharma, Ł Kidzinski, MJ Rodríguez‐Triana, P Dillenbourg Journal of computer assisted learning 34 (2), 193-203, 2018 | 139 | 2018 |
| MOOC video interaction patterns: What do they tell us? N Li, Ł Kidziński, P Jermann, P Dillenbourg Design for Teaching and Learning in a Networked World: 10th European …, 2015 | 139 | 2015 |
| Automated classification of radiographic knee osteoarthritis severity using deep neural networks KA Thomas, Ł Kidziński, E Halilaj, SL Fleming, GR Venkataraman, ... Radiology: Artificial Intelligence 2 (2), e190065, 2020 | 138 | 2020 |
| Automatic real-time gait event detection in children using deep neural networks Ł Kidziński, S Delp, M Schwartz PloS one 14 (1), e0211466, 2019 | 138 | 2019 |
| Deep reinforcement learning for modeling human locomotion control in neuromechanical simulation S Song, Ł Kidziński, XB Peng, C Ong, J Hicks, S Levine, CG Atkeson, ... Journal of neuroengineering and rehabilitation 18, 1-17, 2021 | 135 | 2021 |
| Learning to run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments Ł Kidziński, SP Mohanty, CF Ong, Z Huang, S Zhou, A Pechenko, ... The NIPS'17 Competition: Building Intelligent Systems, 121-153, 2018 | 108 | 2018 |
| Introduction to smart learning analytics: foundations and developments in video-based learning MN Giannakos, DG Sampson, L Kidziński | 95 | 2016 |
| Translating head motion into attention-towards processing of student’s body-language M Raca, L Kidzinski, P Dillenbourg Proceedings of the 8th international conference on educational data mining, 2015 | 95 | 2015 |
| Learning to run challenge: Synthesizing physiologically accurate motion using deep reinforcement learning Ł Kidziński, SP Mohanty, CF Ong, JL Hicks, SF Carroll, S Levine, ... The NIPS'17 Competition: Building Intelligent Systems, 101-120, 2018 | 87 | 2018 |
| Orchestration load indicators and patterns: In-the-wild studies using mobile eye-tracking LP Prieto, K Sharma, Ł Kidzinski, P Dillenbourg IEEE Transactions on Learning Technologies 11 (2), 216-229, 2017 | 75 | 2017 |
| How do in-video interactions reflect perceived video difficulty? N Li, L Kidzinski, P Jermann, P Dillenbourg Proceedings of the European MOOCs stakeholder summit 2015, 112-121, 2015 | 70 | 2015 |
| How to quantify student’s regularity? MS Boroujeni, K Sharma, Ł Kidziński, L Lucignano, P Dillenbourg Adaptive and Adaptable Learning: 11th European Conference on Technology …, 2016 | 66 | 2016 |
| A neural network to predict the knee adduction moment in patients with osteoarthritis using anatomical landmarks obtainable from 2D video analysis MA Boswell, SD Uhlrich, K Thomas, JA Kolesar, GE Gold, GS Beaupre, ... Osteoarthritis and cartilage 29 (3), 346-356, 2021 | 58 | 2021 |
| Assessing inertial measurement unit locations for freezing of gait detection and patient preference J O’Day, M Lee, K Seagers, S Hoffman, A Jih-Schiff, Ł Kidziński, S Delp, ... Journal of neuroengineering and rehabilitation 19 (1), 20, 2022 | 56 | 2022 |
| Artificial intelligence for prosthetics: Challenge solutions Ł Kidziński, C Ong, SP Mohanty, J Hicks, S Carroll, B Zhou, H Zeng, ... The NeurIPS'18 Competition: From Machine Learning to Intelligent …, 2020 | 55 | 2020 |
Scott DelpProfessor of Bioengineering and Mechanical Engineering, Stanford UniversityPotvrđena adresa e-pošte na stanford.edu
