Over the past 10 years, Dr. Abdullah Alharthi's research has focused on pattern recognition to solve a wide range of new tasks including how to learn intelligent behavior in complex dynamic environments. He worked on topics of cognition, perception, theory of mind, gait and human-robot interaction. His work focuses on both human kinetics and the understanding and resolution of how humans and robots dynamically interact with each other. He worked on Deep Learning approaches to solve image, object, and signal recognition and classification problems.
PhD Electrical and Electronics Engineering from The University of Manchester, Imaging and sensing and signal processing.
- Postdoctoral Research Fellow - Robotics, Intelligent Systems & Control (RISC) Laboratory at King Abdullah University of Science and Technology KAUST, Robotics and AI Engineering Research. (Aug 2022 - Present)
- Postdoctoral Research Associates - Department of Electrical and Electronics Engineering the University of Manchester, Robotics and Artificial Intelligence for Nuclear Industry. (Aug 2021 - Aug 2022)
- R&D Electrical Engineer - king Abdulaziz city of science and technology KACST, Knowledge and Technology Transfer. (2016-2018)
- Abdullah S. Alharthi, Syed U. Yunas and K. B. Ozanyan, "Deep Learning for Monitoring of Human Gait: A Review," in IEEE Sensors Journal, vol. 19, no. 21, pp. 9575-9591, 1 Nov.1, 2019. DOI: 10.1109/JSEN.2019.2928777
- Abdullah S. Alharthi, Alexander J. Casson and Krikor B. Ozanyan,” Gait Spatiotemporal Signal Analysis for Parkinson’s Disease Detection and Severity Rating,” IEEE Sensors Journal, vol. 21, no. 2, pp. 1838 - 1848, 20 Aug 2020, DOI: 10.1109/JSEN.2020.3018262
- Abdullah S. Alharthi, Alexander J. Casson and Krikor B. Ozanyan, ”Spatiotemporal Analysis by Deep Learning of Gait Signatures from Floor Sensors,” IEEE Sensors Journal, vol. 21, no. 15, pp. 16904 – 16914, 2021. DOI: 10.1109/JSEN.2021.3078336
- Omar Costilla-Reyes, Ruben Vera-Rodriguez, Abdullah S Alharthi, Syed U Yunas, and Krikor B Ozanyan, “Deep learning in gait analysis for security and healthcare,” (accepted) Book Chapter in “Deep Learning: Algorithms and Applications”, Pedrycz W. and Chen S.-M., Eds. Springer Nature, vol. 865, pp. 299- 334, 2019. DOI: 10.1007/978-3-319-89629-8
- Abdullah S. Alharthi, Krikor B. Ozanyan, “Multimodal Gait Spatiotemporal Data of Different Walking Speeds Fusion,” IEEE Sensors conference, 2021. DOI: 10.1109/SENSORS47087.2021.9639816