Dr. Abdullah Alharthi's research centers around pattern recognition as a means to tackle a diverse array of new challenges, including how to learn intelligent behavior in complex, dynamic environments. His areas of expertise include cognition, perception, theory of mind, gait, and human-robot interaction. His work delves into both human kinetics and the comprehension and resolution of how humans and robots interact with one another in a dynamic setting. Dr. Alharthi employs Deep Learning techniques to tackle problems related to image, object, and signal recognition and classification.


PhD Electrical and Electronics Engineering from The University of Manchester. 

Work Experience 

  • Postdoctoral 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)

Selected Publications

  • 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