F
Fady Alnajjar
Researcher at College of Information Technology
Publications - 141
Citations - 1431
Fady Alnajjar is an academic researcher from College of Information Technology. The author has contributed to research in topics: Computer science & Robot. The author has an hindex of 14, co-authored 118 publications receiving 657 citations. Previous affiliations of Fady Alnajjar include RIKEN Brain Science Institute & Toyota.
Papers
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Unobtrusive Activity Recognition of Elderly People Living Alone Using Anonymous Binary Sensors and DCNN
TL;DR: An unobtrusive activity recognition classifier using deep convolutional neural network (DCNN) and anonymous binary sensors that are passive infrared motion sensors and door sensors are proposed and indicated that the proposed DCNN model outperforms the existing models.
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Novel IoT-Based Privacy-Preserving Yoga Posture Recognition System Using Low-Resolution Infrared Sensors and Deep Learning
Munkhjargal Gochoo,Tan-Hsu Tan,Shih-Chia Huang,Tsedevdorj Batjargal,Jun-Wei Hsieh,Fady Alnajjar,Yung-Fu Chen +6 more
TL;DR: The proposed IoT-based yoga posture recognition system employing a deep convolutional neural network (DCNN) and a low-resolution infrared sensor-based wireless sensor network (WSN) has a great potential in the privacy-preserving yoga training system.
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Exoskeletons With Virtual Reality, Augmented Reality, and Gamification for Stroke Patients' Rehabilitation: Systematic Review.
TL;DR: A categorization of studies according to technologies used helps with understanding the scope of rehabilitation therapies that can be successfully arranged for home-based rehabilitation, and shows that there were general improvements in the motor function of patients using the novel interfacing techniques with exoskeletons.
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Performance Evaluation of Deep CNN-Based Crack Detection and Localization Techniques for Concrete Structures
TL;DR: In this article, a customized convolutional neural network (CNN) was proposed for crack detection in concrete structures, which is compared to four existing deep learning methods based on training data size, data heterogeneity, network complexity, and the number of epochs.
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Implementing FDM 3D Printing Strategies Using Natural Fibers to Produce Biomass Composite
Waleed K. Ahmed,Fady Alnajjar,Essam Zaneldin,Ali H. Al-Marzouqi,Munkhjargal Gochoo,Munkhjargal Gochoo,Sumayya Khalid +6 more
TL;DR: The results of using natural fibers for 3D Printing are presented and appeared to be satisfactory, while a few studies have reported some issues.