Gait Analysis and Smart Wearable Sensors
In this research area, the gait analysis tools such as motion capature, wearable sensors, and machine learning techniques have been used to understand human gait and activities and hence to predict the human and gait and movements intent. This can help in gait analysis and assements and also in developing werable devices.
Related Journal Publication List
- U Martinez-Hernandez, MI Awad, AA Dehghani-Sanij. “Learning architecture for the recognition of walking and prediction of gait period using wearable sensors”, Neurocomputing, 2021.
- H Sarwat, H Sarwat, SA Maged, TH Emara, AM Elbokl, MI Awad. “Design of a Data Glove for Assessment of Hand Performance Using Supervised Machine Learning”, Sensors, 2021.
- Mohamed H Abdelhafiz, Mohammed I Awad, Ahmed Sadek and Farid Tolbah, “Sensor positioning for a human activity recognition system using a double layer classifier”, Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, 2021.
- Ahmed Halim, A. Abdellatif, Mohammed I. Awad and Mostafa R. A. Atia, “Prediction of Human Gait Activities Using Wearable Sensors”, Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, 2021.
- Alireza Abouhossein, Mohammed I. Awad, Hafiz F. Maqbool, Carl Crisp, Todd Stewart, Neil Messenger, Robert C. Richardson, Abbas A. Dehghani-Sanij, and David Bradley, "Foot trajectories and loading rates in a transfemoral amputee for six different commercial prosthetic knees: An indication of adaptability", Medical engineering & physics, 2019.