| The Role of Movement Analysis in Diagnosing and Monitoring Neurodegenerative Conditions: Insights from Gait and Postural Control C Buckley, L Alcock, R McArdle, RZU Rehman, S Del Din, C Mazzà, ... Brain Sciences 9 (2), 34, 2019 | 107 | 2019 |
| Selecting Clinically Relevant Gait Characteristics for Classification of Early Parkinson’s Disease: A Comprehensive Machine Learning Approach RZU Rehman, S Del Din, Y Guan, AJ Yarnall, JQ Shi, L Rochester Scientific Reports 9 (1), 1-12, 2019 | 80 | 2019 |
| Application of wearable inertial sensors and a new test battery for distinguishing retrospective fallers from non-fallers among community-dwelling older people H Qiu, RZU Rehman, X Yu, S Xiong Scientific reports 8 (1), 16349, 2018 | 54 | 2018 |
| Accelerometry-Based Digital Gait Characteristics for Classification of Parkinson's Disease: What Counts? RZU Rehman, C Buckley, ME Micó-Amigo, C Kirk, M Dunne-Willows, ... IEEE Open Journal of Engineering in Medicine and Biology 1, 65-73, 2020 | 30 | 2020 |
| Deep Learning Techniques for Improving Digital Gait Segmentation M Gadaleta, G Cisotto, M Rossi, RZU Rehman, L Rochester, S Del Din 2019 41st Annual International Conference of the IEEE Engineering in …, 2019 | 30 | 2019 |
| Comparison of Walking Protocols and Gait Assessment Systems for Machine Learning-Based Classification of Parkinson’s Disease RZU Rehman, S Del Din, JQ Shi, B Galna, S Lord, AJ Yarnall, Y Guan, ... Sensors 19 (24), 5363, 2019 | 30 | 2019 |
| Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders RZU Rehman, Y Zhou, S Del Din, L Alcock, C Hansen, Y Guan, ... Sensors 20 (23), 6992, 2020 | 22 | 2020 |
| Classification of Neurological Patients to Identify Fallers Based on Spatial-Temporal Gait Characteristics Measured by a Wearable Device Y Zhou, R Zia Ur Rehman, C Hansen, W Maetzler, S Del Din, L Rochester, ... Sensors 20 (15), 4098, 2020 | 22 | 2020 |
| Turning Detection During Gait: Algorithm Validation and Influence of Sensor Location and Turning Characteristics in the Classification of Parkinson’s Disease RZU Rehman, P Klocke, S Hryniv, B Galna, L Rochester, S Del Din, ... Sensors 20 (18), 5377, 2020 | 18 | 2020 |
| Investigating the Impact of Environment and Data Aggregation by Walking Bout Duration on Parkinson’s Disease Classification Using Machine Learning RZU Rehman, Y Guan, JQ Shi, L Alcock, AJ Yarnall, L Rochester, ... Frontiers in aging neuroscience, 182, 2022 | 3 | 2022 |
| Predicting the Progression of Parkinson’s Disease MDS-UPDRS-III Motor Severity Score from Gait Data using Deep Learning RZU Rehman, L Rochester, AJ Yarnall, S Del Din 2021 43rd Annual International Conference of the IEEE Engineering in …, 2021 | 2 | 2021 |
| Usability evaluations of a newly developed wearable inertial sensing system for assessing elderly fall risk RZU Rehman, J Jang, S Xiong Advances in Physical Ergonomics & Human Factors: Proceedings of the AHFE …, 2019 | 1 | 2019 |
| Walking is Associated With Physical Capacity and Fatigue but not Cognition in Long-Term Care Residents LM Taylor, S Lord, J Parsons, SA Moyes, RZU Rehman, C Buckley, ... Journal of the American Medical Directors Association 23 (11), e1-e2, 2022 | | 2022 |
| Assessing fatigue and sleep in chronic diseases using physiological signals from wearables: A pilot study E Antikainen, H Njoum, J Kudelka, D Branco, RZU Rehman, V Macrae, ... Frontiers in Physiology, 2380, 2022 | | 2022 |
| Evaluations of a wearable inertial sensors based fall risk assessment system for the elderly RZU Rehman 한국과학기술원, 2018 | | 2018 |
| Predicting Daytime Sleepiness from Electrocardiography Based Respiratory Rate Using Deep Learning E Antikainen, RZU Rehman, T Ahmaniemi, M Chatterjee | | |
Silvia Del DinNUAcT Fellow, Translational and Clinical Research Institute, Faculty of Medical Sciences, NewcastleVerified email at ncl.ac.uk
