MS Industrial and Systems Engineering   (2016-2017)

Korea Advanced Institute of Science and Technology (KAIST), South Korea

http://www.kaist.ac.kr/html/en/

BSc Industrial and Manufacturing Engineering  (2010-2014)                          

Faculty of Mechanical Engineering                               

University of Engineering & Technology, Lahore, Pakistan   

http://www.uet.edu.pk/

I am interested in developing novel tools in the form of algorithms for the extraction of the meaningful gait measures from the wearable inertial sensors data in general and applying the deep leaning techniques to find the progression of the diseases in particular. I am working in the following domains:

• Wearable Technology
• Signal Processing
• Machine Learning/Deep learning
• Gait

• Rehman RZU, Rochester L, Yarnall AJ, Del Din S. Predicting the Progression of Parkinson’s Disease MDS-UPDRS-III Motor Severity Score from Gait Data using Deep Learning. In: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2021, Mexico: IEEE.
• Rehman RZU, Buckley C, Micó-Amigo ME, Kirk C, Dunne-Willows M, Mazzà C, Shi JQ, Alcock L, Rochester L, Del Din S. Accelerometry-based digital gait characteristics for classification of Parkinson’s disease: what counts?. IEEE Open Journal of Engineering in Medicine and Biology 2020, 1, 65-73.
• Zhou Y, Rehman RZU, Hansen C, Maetzler W, Del Din S, Rochester L, Hortobágyi T, Lamoth CJC. Classification of Neurological Patients to Identify Fallers Based on Spatial-Temporal Gait Characteristics Measured by a Wearable Device. Sensors 2020, 20(15), 4098.
• Rehman RZU, Zhou Y, Del Din S, Alcock L, Hansen C, Guan Y, Hortobágyi T, Maetzler W, Rochester L, Lamoth CJC. Gait Analysis with Wearables can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders. Sensors 2020, 20(23), 6992.
• Rehman RZU, Klocke P, Hryniv S, Galna B, Rochester L, Del Din S, Alcock L. Turning detection during gait: Algorithm validation and influence of sensor location and turning characteristics in the classification of Parkinson’s disease. Sensors 2020, 20(18), 5377.
• Rehman RZU, Del Din S, Shi JQ, Galna B, Lord S, Yarnall AJ, Guan Y, Rochester L. Comparison of walking protocols and gait assessment systems for machine learning based classification of Parkinson’s disease. Sensors 2019, 19(24), 5363.
• Gadaleta M, Cisotto G, Rossi M, Rehman RZU, Rochester L, Del Din S. Deep Learning Techniques for Improving Digital Gait Segmentation. In: 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2019, Berlin: IEEE.
• Rehman RZU, Del Din S, Guan Y, Yarnall AJ, Shi JQ, Rochester L. Selecting Clinically Relevant Gait Characteristics for Classification of Early Parkinson’s Disease: A Comprehensive Machine Learning Approach. Scientific Reports 2019, 9, 17269.
• Buckley C, Alcock L, Mc Ardle R, Rehman RZU, Del Din S, Mazzà C, Yarnall A, Rochester L. The role of movement analysis in diagnosing and monitoring neurodegenerative conditions: Insights from gait and postural control. Brain Sciences 2019, 9(2), 34.