Rana Zia Ur Rehman
Rana Zia Ur Rehman
Postdoctoral Scientist in Data Science AI and Digital Health at The Janssen
Verified email at - Homepage
Cited by
Cited by
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Evaluations of a wearable inertial sensors based fall risk assessment system for the elderly
RZU Rehman
한국과학기술원, 2018
Predicting Daytime Sleepiness from Electrocardiography Based Respiratory Rate Using Deep Learning
E Antikainen, RZU Rehman, T Ahmaniemi, M Chatterjee
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