Rana Zia Ur Rehman

Deep Learning Techniques for Improving Digital Gait Segmentation

Giulia Cisotto, Matteo Gadaleta, Michele Rossi, 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

Accelerometry-Based Digital Gait Characteristics for Classification of Parkinson's Disease: What Counts?

Jian Qing Shi, Claudia Mazzà, Rana Zia Ur Rehman, 2020, IEEE Open Journal of Engineering in Medicine and Biology.

Usability Evaluations of a Newly Developed Wearable Inertial Sensing System for Assessing Elderly Fall Risk

Shuping Xiong, Rana Zia Ur Rehman, Jihyeok Jang, 2018, Advances in Physical Ergonomics & Human Factors.

Classification of Neurological Patients to Identify Fallers Based on Spatial-Temporal Gait Characteristics Measured by a Wearable Device

Claudine J. C. Lamoth, Rana Zia Ur Rehman, Silvia Del Din, 2020, Sensors.

Turning Detection During Gait: Algorithm Validation and Influence of Sensor Location and Turning Characteristics in the Classification of Parkinson’s Disease

Rana Zia Ur Rehman, Silvia Del Din, Brook Galna, 2020, Sensors.

Application of Wearable Inertial Sensors and A New Test Battery for Distinguishing Retrospective Fallers from Non-fallers among Community-dwelling Older People

Hai Qiu, Rana Zia Ur Rehman, Shuping Xiong, 2018, Scientific Reports.

Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders

Yu Guan, Claudine J. C. Lamoth, Rana Zia Ur Rehman, 2020, Sensors.

Comparison of Walking Protocols and Gait Assessment Systems for Machine Learning-Based Classification of Parkinson’s Disease

Jian Qing Shi, Rana Zia Ur Rehman, Silvia Del Din, 2019, Sensors.